{"meta":{"query_hash":"b9d0d0dc13c2","filters":{"topic":"Gaussian Processes and Bayesian Inference"},"cohort_total":309,"direct_labels_cover":0,"predictions_cover":309,"exported":309,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/b9d0d0dc13c2","api":"https://metacan.xera.ac/api/v1/cohort?topic=Gaussian+Processes+and+Bayesian+Inference"},"results":[{"id":"W101391057","doi":"","title":"Learning the Linear Dynamical System with ASOS","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Precomputation; Computer science; Dynamical systems theory; Convergence (economics); Linear dynamical system; Algorithm; Linear system; Dynamical system (definition); Applied mathematics; Theoretical computer science; Machine learning; Mathematics; Computation","score_opus":0.004452779128028905,"score_gpt":0.20407384686347207,"score_spread":0.19962106773544316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W101391057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024353495,0.0000033626345,0.9487179,0.0015674529,0.00013545968,0.000066979475,1.161201e-7,0.0002720776,0.024883155],"genre_scores_gemma":[0.95347184,4.416922e-7,0.04547328,0.00010511607,0.000056925775,0.000006919464,2.524223e-7,0.000004633516,0.00088061247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932516,0.000021072117,0.00009135346,0.00020580129,0.00017675468,0.00017986356],"domain_scores_gemma":[0.99943626,0.000053185042,0.000041365416,0.0003514869,0.000058807247,0.000058909834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001532236,0.00008092215,0.00007321698,0.000020323101,0.0001850816,0.0002001071,0.0007580604,0.000040232462,0.00002393224],"category_scores_gemma":[0.000017237262,0.00003797759,0.000020239313,0.0002178562,0.000051774718,0.0001906786,0.0001204739,0.00037329888,0.00012684391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024684214,0.000013293916,0.002160493,0.000023114433,0.000006698495,0.000014379828,0.00021810304,0.00008814004,0.0004133291,0.9843712,0.00006600611,0.012622799],"study_design_scores_gemma":[0.00017418482,0.00015648853,0.004349574,0.000029545285,0.000004423368,0.00025968833,0.00020701032,0.98956674,0.0006048996,0.00047759752,0.003979192,0.0001906449],"about_ca_topic_score_codex":0.000026568818,"about_ca_topic_score_gemma":0.00004881035,"teacher_disagreement_score":0.9894786,"about_ca_system_score_codex":0.000006526775,"about_ca_system_score_gemma":0.00006114925,"threshold_uncertainty_score":0.19296373},"labels":[],"label_agreement":null},{"id":"W132606982","doi":"10.1609/aaai.v26i1.8309","title":"Hierarchical Double Dirichlet Process Mixture of Gaussian Processes","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hierarchical Dirichlet process; Dirichlet process; Mixture model; Gaussian process; Latent Dirichlet allocation; Computer science; Gaussian; Dirichlet distribution; Process (computing); Mathematics; Algorithm; Statistical physics; Applied mathematics; Artificial intelligence; Topic model; Mathematical analysis; Physics","score_opus":0.047686789340410374,"score_gpt":0.2969526604201138,"score_spread":0.24926587107970344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W132606982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4530349,0.00094243296,0.29023746,0.0561616,0.002314198,0.0024566269,0.00008298665,0.0007287328,0.19404107],"genre_scores_gemma":[0.99397993,0.00009952803,0.0051478953,0.00023835774,0.00007480658,0.000041209812,0.0000014131093,0.000019067513,0.00039778993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968007,0.000022210754,0.0008905014,0.000839087,0.0009138134,0.00053365476],"domain_scores_gemma":[0.995971,0.000115621464,0.00067854207,0.0005445792,0.0025183952,0.00017183408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034119384,0.00036364706,0.00051962235,0.000161058,0.00022790986,0.00034664886,0.0030971996,0.00018225578,0.00012740977],"category_scores_gemma":[0.00060773036,0.00026318544,0.00016539765,0.0025611168,0.00047453222,0.0007110403,0.00056171557,0.00051980047,0.000033323744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010069834,0.00045168016,0.0004749667,0.0008348951,0.0000295469,0.0000041196245,0.0024039198,0.000026112184,0.02158046,0.9547033,0.0001383587,0.019252],"study_design_scores_gemma":[0.00005869217,0.00015258664,0.00016335378,0.00056305306,0.000017466464,0.0000249672,0.00048557625,0.0028613375,0.72476405,0.2704925,0.00014762055,0.00026880592],"about_ca_topic_score_codex":0.000016364278,"about_ca_topic_score_gemma":0.00002204853,"teacher_disagreement_score":0.7031836,"about_ca_system_score_codex":0.000029134966,"about_ca_system_score_gemma":0.00094863086,"threshold_uncertainty_score":0.99998206},"labels":[],"label_agreement":null},{"id":"W1465438125","doi":"10.1017/cbo9780511791277.010","title":"Bayesian inference with Gaussian errors","year":2005,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Bayesian inference; Bayesian probability; Gaussian; Computer science; Artificial intelligence; Frequentist inference; Econometrics; Statistics; Mathematics; Physics","score_opus":0.01549434823961064,"score_gpt":0.1999327249718923,"score_spread":0.18443837673228167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1465438125","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006519075,0.00007297961,0.26673162,0.00012910202,0.00010994016,0.00027190612,0.00005942339,0.0003235443,0.732295],"genre_scores_gemma":[0.014982205,0.0000973383,0.009947515,0.00019492792,0.00014484077,0.000001662391,0.00002106593,0.00005821372,0.9745522],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972775,0.000035578676,0.00030131298,0.0011854109,0.0005766373,0.0006235433],"domain_scores_gemma":[0.9973101,0.000067789835,0.00043313502,0.0014907963,0.00025021046,0.00044798435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000090747744,0.00072801695,0.000599851,0.00034665674,0.00033056884,0.00029602577,0.0026496125,0.0004698174,0.000018861267],"category_scores_gemma":[0.000007388836,0.0007090804,0.00018993212,0.000044075347,0.00035918143,0.00057535165,0.0008303815,0.00081798056,0.00006143913],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003062373,0.000014266521,0.0000051012125,0.00008688514,0.00008429895,0.0005838317,0.000078894474,0.000007802288,0.00000506579,0.986652,0.0046855086,0.007765718],"study_design_scores_gemma":[0.0006969488,0.00023276695,0.000051454834,0.00057755824,0.00011500571,0.00011725522,0.000017391045,0.0015719469,0.00021042538,0.00028445476,0.99477863,0.0013461553],"about_ca_topic_score_codex":0.00007650354,"about_ca_topic_score_gemma":0.000017461116,"teacher_disagreement_score":0.9900931,"about_ca_system_score_codex":0.00022170828,"about_ca_system_score_gemma":0.0005755062,"threshold_uncertainty_score":0.99953604},"labels":[],"label_agreement":null},{"id":"W1483202606","doi":"","title":"Automatic gait optimization with Gaussian process regression","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Smoothness; Gait; Kriging; Bayesian optimization; Gaussian process; Artificial intelligence; Process (computing); Robot; Local optimum; Machine learning; Noise (video); Bayesian probability; Mathematical optimization; Gaussian; Mathematics; Image (mathematics)","score_opus":0.007915926664560351,"score_gpt":0.2503334888718518,"score_spread":0.24241756220729146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483202606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004661622,0.000021211803,0.96387166,0.0007552807,0.0000624511,0.00013385263,1.739166e-7,0.00040338282,0.030090347],"genre_scores_gemma":[0.6719018,0.000002861261,0.32750973,0.00022042288,0.000022941336,0.000005539868,0.000001457181,0.00000766201,0.00032754583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987322,0.000012483767,0.00022345464,0.00035263086,0.00034558843,0.0003336107],"domain_scores_gemma":[0.99919426,0.000030340027,0.00012502828,0.00038715292,0.00012210823,0.00014107786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022751231,0.00015635125,0.0001312516,0.00012640683,0.00014476893,0.00021186957,0.0006094363,0.000061848375,0.00016255287],"category_scores_gemma":[0.000017662343,0.00009484314,0.0000206981,0.0007646091,0.00003770095,0.0008390026,0.00006408157,0.000097000484,0.000037805814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009780536,0.0006219875,0.0074983095,0.0008146787,0.0000613528,0.0003730146,0.0044014547,0.013200349,0.00026070216,0.3600385,0.001750368,0.6108815],"study_design_scores_gemma":[0.0005737669,0.00036768234,0.004939186,0.0003375871,0.000009691208,0.00016361377,0.00019498848,0.97983587,0.007685698,0.0052395337,0.00016301134,0.00048936874],"about_ca_topic_score_codex":0.0000050011186,"about_ca_topic_score_gemma":0.000013057573,"teacher_disagreement_score":0.9666355,"about_ca_system_score_codex":0.000024542978,"about_ca_system_score_gemma":0.000111505746,"threshold_uncertainty_score":0.3867591},"labels":[],"label_agreement":null},{"id":"W1506018867","doi":"10.1109/icassp.2015.7179021","title":"Online local Gaussian process for tensor-variate regression: Application to fast reconstruction of limb movements from brain signal","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Gaussian process; Kriging; Computer science; Artificial intelligence; Regression; Tensor (intrinsic definition); Scalability; Machine learning; Random variate; Gaussian; Data set; Pattern recognition (psychology); Representation (politics); Data mining; Algorithm; Mathematics; Statistics; Random variable","score_opus":0.026959180895492353,"score_gpt":0.2912321244973791,"score_spread":0.26427294360188675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506018867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013974804,0.000019499052,0.98171926,0.0029067213,0.00015003383,0.00043135055,0.00004379571,0.00009142318,0.000663115],"genre_scores_gemma":[0.84572136,0.0000016817525,0.15305512,0.000709872,0.00012211785,0.0000779928,0.00003523506,0.000010794305,0.00026584583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984838,0.00002668702,0.00040480195,0.0005246643,0.00031687692,0.00024321476],"domain_scores_gemma":[0.99868035,0.000052545365,0.00023115042,0.0003984854,0.0003982959,0.00023917962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018559373,0.0001693311,0.00022893684,0.00010979534,0.00006759361,0.000074514806,0.0006680829,0.00009402269,0.000024358042],"category_scores_gemma":[0.00004974499,0.00013098834,0.000047036883,0.00043927625,0.000046063764,0.00045336018,0.000121490644,0.00008094246,0.000018244167],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018612348,0.00045172064,0.001898149,0.000111864436,0.000039733277,0.000002170512,0.0011881606,0.00097676,0.005389617,0.023563977,0.0014204942,0.9647712],"study_design_scores_gemma":[0.0026552672,0.0014854562,0.008559291,0.00042984256,0.000024943063,0.000030002844,0.0019679293,0.7321667,0.06263301,0.18671747,0.0024943762,0.0008356949],"about_ca_topic_score_codex":0.00011314471,"about_ca_topic_score_gemma":0.00005749396,"teacher_disagreement_score":0.96393555,"about_ca_system_score_codex":0.00004655282,"about_ca_system_score_gemma":0.00020720615,"threshold_uncertainty_score":0.53415495},"labels":[],"label_agreement":null},{"id":"W1507070875","doi":"10.48550/arxiv.1206.4635","title":"Deep Mixtures of Factor Analysers","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Overfitting; Latent variable; Computer science; Layer (electronics); Factor (programming language); Graphical model; Artificial intelligence; Boltzmann machine; Machine learning; Deep learning; Variety (cybernetics); Restricted Boltzmann machine; Inference; Artificial neural network; Chemistry","score_opus":0.04871719549440465,"score_gpt":0.18224827660197945,"score_spread":0.1335310811075748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507070875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21997921,0.00007961726,0.7767637,0.0000317734,0.0001053435,0.000034711313,0.0000012832172,0.00004949722,0.002954889],"genre_scores_gemma":[0.99705285,0.000023761559,0.002600007,0.00005142585,0.000023349543,7.983431e-8,5.935749e-7,0.0000036880942,0.00024425547],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993266,0.0000304884,0.00009533005,0.00022891536,0.000056105895,0.00026256338],"domain_scores_gemma":[0.99926656,0.00003870052,0.00009880127,0.00038877307,0.00006472097,0.00014245245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006235304,0.00010057825,0.00012965516,0.00011743353,0.000059662023,0.000023017452,0.00072479737,0.000053592656,0.000073387],"category_scores_gemma":[0.000015948184,0.000096040974,0.00007772748,0.00068684894,0.000057586247,0.00076644815,0.00015097266,0.00007582777,0.000047064128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013633735,0.00016210493,0.13075387,0.000051878502,0.00007277114,0.00002994831,0.00087646866,0.0021823226,0.0010438709,0.8609852,0.00015415746,0.0036737698],"study_design_scores_gemma":[0.0018338382,0.00037697784,0.31337836,0.00009778504,0.00018467978,0.000034236236,0.00066679245,0.56800455,0.036961652,0.07386481,0.002863313,0.0017330068],"about_ca_topic_score_codex":0.000028478771,"about_ca_topic_score_gemma":0.0000071876634,"teacher_disagreement_score":0.7871204,"about_ca_system_score_codex":0.000024658,"about_ca_system_score_gemma":0.00003330017,"threshold_uncertainty_score":0.39164373},"labels":[],"label_agreement":null},{"id":"W151435321","doi":"","title":"Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations","year":2014,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Ode; Ordinary differential equation; Gaussian process; Applied mathematics; Estimation theory; Consistency (knowledge bases); Computer science; Gaussian; Mathematics; Bayesian probability; Mathematical optimization; Bayes estimator; Algorithm; Differential equation; Artificial intelligence; Mathematical analysis","score_opus":0.023736395395733804,"score_gpt":0.2975774090199679,"score_spread":0.2738410136242341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W151435321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014839738,0.000013793854,0.9813843,0.0067064073,0.0003017181,0.00020052049,0.000007663622,0.00015098619,0.009750665],"genre_scores_gemma":[0.9683072,0.0000134959,0.030609757,0.00020795738,0.000089252535,0.000102783815,0.000085689164,0.000015364163,0.0005685281],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984414,0.00009031457,0.0003579594,0.00050375634,0.00031666012,0.00028991894],"domain_scores_gemma":[0.9988862,0.00034336146,0.00020867628,0.00022377635,0.00025354358,0.00008442188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025956286,0.00021471542,0.00020168387,0.00034747226,0.00018376275,0.0004503442,0.0009396891,0.00010741846,0.00013866636],"category_scores_gemma":[0.001033554,0.00019900559,0.000055291173,0.00032611878,0.00003576624,0.00062498683,0.00012585023,0.00043735234,0.000034721783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039748207,0.00012371741,0.0029422005,0.000068661684,0.000015424792,0.0000025440277,0.00033112292,0.00588294,0.00028374346,0.8954255,0.000033388464,0.094850995],"study_design_scores_gemma":[0.000570042,0.00023878807,0.0032983024,0.00015021213,0.0000040026794,0.0000055130636,0.000015758455,0.93016815,0.00015281988,0.06431135,0.000858661,0.0002264072],"about_ca_topic_score_codex":0.00004867083,"about_ca_topic_score_gemma":0.000091730886,"teacher_disagreement_score":0.9668232,"about_ca_system_score_codex":0.000061799365,"about_ca_system_score_gemma":0.00014815076,"threshold_uncertainty_score":0.81152123},"labels":[],"label_agreement":null},{"id":"W151458526","doi":"10.1007/978-94-007-4153-9_7","title":"Modeling Nonlinear Beta Probability Fields","year":2012,"lang":"en","type":"book-chapter","venue":"Quantitative geology and geostatistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"BETA (programming language); Nonlinear system; Statistical physics; Mathematics; Computer science; Physics; Quantum mechanics","score_opus":0.043728161284140095,"score_gpt":0.2756271903406014,"score_spread":0.23189902905646131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W151458526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001375542,0.0024256143,0.96136516,0.0007305329,0.00032147265,0.00019988701,0.00009682159,0.00007131498,0.03465163],"genre_scores_gemma":[0.029292284,0.0014541892,0.93721485,0.00061415136,0.00016601382,0.000017287653,0.00013907057,0.00004041498,0.03106175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834245,0.000045539226,0.0004210076,0.00060844177,0.00017948166,0.00040307848],"domain_scores_gemma":[0.9985878,0.0003793168,0.00018664356,0.00046557753,0.0002317815,0.00014891282],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028810336,0.00036392474,0.00048717938,0.00010661195,0.00020065522,0.00006924071,0.0004549327,0.00046375877,0.00010576916],"category_scores_gemma":[0.000093493014,0.00033358004,0.000059649257,0.00004261477,0.00034565944,0.00022193686,0.00034886447,0.0005902692,0.00014772642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011343109,0.00001902828,0.000050105464,0.00011107638,0.000050513594,0.000019322697,0.00052020827,0.0000698129,2.443549e-7,0.9774119,0.00008247575,0.021653945],"study_design_scores_gemma":[0.00014112571,0.00032583642,0.00007648754,0.00006753783,0.00004841048,0.000039406532,0.000012393545,0.15339008,0.0000014713211,0.8394267,0.0060636275,0.00040691704],"about_ca_topic_score_codex":0.00003526886,"about_ca_topic_score_gemma":0.00009133493,"teacher_disagreement_score":0.15332027,"about_ca_system_score_codex":0.000018020308,"about_ca_system_score_gemma":0.00015449295,"threshold_uncertainty_score":0.9999116},"labels":[],"label_agreement":null},{"id":"W1533803232","doi":"10.48550/arxiv.1402.0929","title":"Input Warping for Bayesian Optimization of Non-stationary Functions","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research","keywords":"Bayesian optimization; Computer science; Hyperparameter; Image warping; Optimization problem; Artificial intelligence; Bayesian probability; Gaussian process; Benchmark (surveying); Mathematical optimization; Machine learning; Algorithm; Gaussian; Mathematics","score_opus":0.028441093291583317,"score_gpt":0.17593642152303599,"score_spread":0.14749532823145267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533803232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004261554,0.0000035197954,0.9928455,0.00015659489,0.00012066591,0.0001164951,0.0000039548427,0.000059959744,0.002431794],"genre_scores_gemma":[0.9335701,0.000007822602,0.065891765,0.00007084313,0.000029637533,9.4731456e-7,0.000007849268,0.00000557461,0.000415479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993436,0.000023402381,0.00012300893,0.0003129366,0.000042795637,0.00015425382],"domain_scores_gemma":[0.9992466,0.00009602938,0.00011997892,0.0002994412,0.00017341091,0.00006454172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011191287,0.00008801229,0.00011008283,0.00013632305,0.0001482644,0.00003141875,0.00041923902,0.000049648093,0.0000144722135],"category_scores_gemma":[0.00003530679,0.00009738894,0.000059458947,0.00052545185,0.00004174661,0.0005733695,0.0000797564,0.000048831917,0.000008671215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014172627,0.00005398482,0.0017014801,0.000055916313,0.000016211003,0.0000022771983,0.00014168546,0.631744,0.00008167883,0.36179453,0.00025369375,0.004140324],"study_design_scores_gemma":[0.00032611704,0.00009639667,0.00074007164,0.000024625635,0.000012618091,0.000001321062,0.000038434016,0.9865705,0.00018511195,0.011619308,0.0002697802,0.00011571684],"about_ca_topic_score_codex":0.0000115057355,"about_ca_topic_score_gemma":0.0000053784356,"teacher_disagreement_score":0.92930853,"about_ca_system_score_codex":0.000025826159,"about_ca_system_score_gemma":0.00006955184,"threshold_uncertainty_score":0.39714056},"labels":[],"label_agreement":null},{"id":"W1534769976","doi":"10.1023/a:1024226918553","title":"Numerical optimization and surface estimation with imprecise function evaluations","year":2003,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Mathematical optimization; Computer science; Quadratic equation; Variety (cybernetics); Function (biology); Class (philosophy); Mathematics; Algorithm; Artificial intelligence; Applied mathematics","score_opus":0.008731772407130528,"score_gpt":0.251082748410638,"score_spread":0.24235097600350747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534769976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033671854,0.0000758836,0.99596286,0.000066134686,0.000060487844,0.00007937343,0.0000026877567,0.000039697054,0.00034566442],"genre_scores_gemma":[0.502542,0.0000068070904,0.49740818,0.000024813677,0.000003880652,6.443828e-7,0.000003121019,0.0000026546245,0.000007882678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993598,0.0000401275,0.00012697425,0.00021970696,0.00013183104,0.00012159564],"domain_scores_gemma":[0.99955255,0.00010172064,0.00007849318,0.000099732824,0.000108000626,0.00005948801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015803627,0.00008293056,0.000081413746,0.000026592103,0.00023250673,0.00024143053,0.000056031255,0.000022361328,0.000006294089],"category_scores_gemma":[0.000052552827,0.00007133786,0.0000043456266,0.00018534776,0.000027819637,0.00017819527,0.000025988838,0.00005813749,0.0000013803943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004628156,0.00002689693,0.001500043,0.0000449332,0.0000114022705,0.0000026102832,0.00039711854,0.38274038,0.000018311652,0.4250115,0.00006674097,0.19017544],"study_design_scores_gemma":[0.00018835497,0.000113299335,0.0022822095,0.00002075641,0.00001092805,0.000023169454,0.00001957839,0.9815471,0.00002602999,0.015646465,0.00002250541,0.00009956853],"about_ca_topic_score_codex":0.0000074348445,"about_ca_topic_score_gemma":9.998266e-7,"teacher_disagreement_score":0.59880674,"about_ca_system_score_codex":0.0000095378,"about_ca_system_score_gemma":0.000056123597,"threshold_uncertainty_score":0.29090735},"labels":[],"label_agreement":null},{"id":"W1560659665","doi":"10.1023/a:1013947519741","title":"Metric-Based Methods for Adaptive Model Selection and Regularization","year":2002,"lang":"en","type":"article","venue":"Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Regularization (linguistics); Artificial intelligence; Computer science; Machine learning; Labeled data; Training set; Exploit; Model selection; Supervised learning; Regression; Pattern recognition (psychology); Mathematics; Statistics; Artificial neural network","score_opus":0.029202128097776763,"score_gpt":0.29442337625596693,"score_spread":0.26522124815819015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560659665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014610405,0.0002398729,0.9980432,0.00059262186,0.00002900058,0.000113288814,5.210749e-7,0.00014033733,0.00069510116],"genre_scores_gemma":[0.39745298,0.0000067343067,0.6019513,0.00008767148,0.000012291867,0.000013359192,0.00000156108,0.000006248431,0.000467873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992681,0.000089594934,0.0001178039,0.00028016025,0.00008436137,0.00015994237],"domain_scores_gemma":[0.99956125,0.0001274054,0.00008814826,0.0000960203,0.00007979244,0.000047355214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032062584,0.00009612305,0.0001086057,0.00015665143,0.00023864013,0.00013497197,0.00015704922,0.000048821206,0.00001052412],"category_scores_gemma":[0.00019688682,0.00008841565,0.000028727512,0.00052692153,0.000011080095,0.00027509726,0.000044117216,0.00012783283,0.0000017795415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010462806,0.000037757804,0.00085291546,0.000047331887,0.000013138067,4.9267607e-7,0.0004322148,0.13464716,0.0011434379,0.11386098,0.000058239704,0.7488959],"study_design_scores_gemma":[0.00024168428,0.0001424236,0.00010784155,0.000008839416,0.0000075520006,0.0000037540306,0.000002833794,0.9866405,0.0011763299,0.010911893,0.00064551906,0.00011081651],"about_ca_topic_score_codex":0.000008028908,"about_ca_topic_score_gemma":0.0000024329406,"teacher_disagreement_score":0.8519933,"about_ca_system_score_codex":0.000021206853,"about_ca_system_score_gemma":0.000015755291,"threshold_uncertainty_score":0.36054853},"labels":[],"label_agreement":null},{"id":"W1564746094","doi":"10.1007/978-3-642-04180-8_58","title":"Kernel-Based Copula Processes","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Copula (linguistics); Computer science; Gaussian process; Gaussian; Kernel (algebra); Interdependence; Heteroscedasticity; Series (stratigraphy); Algorithm; Machine learning; Econometrics; Mathematics; Discrete mathematics","score_opus":0.013563922369210826,"score_gpt":0.2391933064908747,"score_spread":0.22562938412166386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564746094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012560791,0.00085970486,0.98567706,0.0017776043,0.0007813596,0.00035432068,0.000005061273,0.00032910847,0.010203245],"genre_scores_gemma":[0.28421575,0.00010436501,0.7072171,0.0065500396,0.00064301485,0.00001904696,0.000011529794,0.00006426874,0.0011749025],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951077,0.000022807159,0.0006329839,0.0020469646,0.0012607706,0.0009287399],"domain_scores_gemma":[0.99677056,0.00032219276,0.00040893778,0.0016686909,0.00054654694,0.00028306965],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005316605,0.00074928487,0.000667381,0.0008826921,0.00031515016,0.001173704,0.005428302,0.00041293504,0.000041897605],"category_scores_gemma":[0.00017140266,0.000658956,0.00013267124,0.0013240932,0.0006676757,0.00083806127,0.0006804818,0.00087823573,0.000106009844],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006613581,0.00006119625,0.00006589188,0.0002366941,0.0000063305633,0.00019885381,0.00023916138,0.009227401,0.00003059944,0.027507117,0.000055153738,0.962365],"study_design_scores_gemma":[0.0005597341,0.00057192944,0.0002703859,0.0018002454,0.000016970453,0.00013851713,1.05210184e-7,0.3124664,0.003961964,0.6707601,0.0075730677,0.0018805551],"about_ca_topic_score_codex":0.000011236783,"about_ca_topic_score_gemma":0.00006006168,"teacher_disagreement_score":0.96048445,"about_ca_system_score_codex":0.00025017725,"about_ca_system_score_gemma":0.0027278287,"threshold_uncertainty_score":0.9999528},"labels":[],"label_agreement":null},{"id":"W1565548452","doi":"10.1007/978-3-642-15705-9_16","title":"Measurement Selection in Untracked Freehand 3D Ultrasound","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Decorrelation; Computer science; Speckle pattern; Computer vision; Artificial intelligence; Probabilistic logic; 3D ultrasound; Selection (genetic algorithm); Position (finance); Tracking (education); Ultrasound; Acoustics","score_opus":0.011057769840112081,"score_gpt":0.23418485016773516,"score_spread":0.22312708032762307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565548452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08843029,0.000047454858,0.90962404,0.000640175,0.00093201565,0.00017476326,3.45867e-7,0.00008536929,0.00006553011],"genre_scores_gemma":[0.69890827,0.000003071567,0.30062267,0.00034417445,0.00010754609,0.00000907073,1.6045757e-7,0.000004597062,4.2206378e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971715,0.00004614857,0.0003359346,0.00091711903,0.00085977773,0.00066950754],"domain_scores_gemma":[0.9988745,0.00014193774,0.00010074593,0.00050211314,0.00024237962,0.00013830977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001383622,0.00022681266,0.00020801014,0.00048775348,0.00020502233,0.0006777494,0.0019051266,0.00011988061,0.000018210647],"category_scores_gemma":[0.0003579934,0.00019799003,0.00003398027,0.0032186953,0.00028670693,0.0010415466,0.0002628261,0.0006545381,0.00002311439],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009322838,0.00025857284,0.050081465,0.000046208566,0.0000045310594,0.00003947721,0.0016778546,0.013612074,0.15558241,0.004956596,0.000012595593,0.7737189],"study_design_scores_gemma":[0.0006416186,0.00018567657,0.15518245,0.00013539991,0.0000026298467,0.00018186704,6.6371484e-7,0.7014056,0.08626322,0.055309568,0.000102905935,0.0005884143],"about_ca_topic_score_codex":0.00010527693,"about_ca_topic_score_gemma":0.0023146905,"teacher_disagreement_score":0.7731305,"about_ca_system_score_codex":0.0001564632,"about_ca_system_score_gemma":0.00047234696,"threshold_uncertainty_score":0.8073799},"labels":[],"label_agreement":null},{"id":"W1579122361","doi":"10.1109/ijcnn.2005.1556042","title":"Learning nonlinear constraints with contrastive backpropagation","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Backpropagation; Independence (probability theory); Computer science; Representation (politics); Nonlinear system; Artificial intelligence; Artificial neural network; Machine learning; Energy (signal processing); Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.018024568077374004,"score_gpt":0.23561525274982753,"score_spread":0.21759068467245352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579122361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050533894,0.000092003946,0.8587647,0.009839261,0.0018278982,0.00093299686,0.000026438931,0.00091901637,0.07706378],"genre_scores_gemma":[0.98568666,0.00008767219,0.011345492,0.00052229676,0.0009305172,0.000051927273,0.000033103766,0.000031570788,0.0013107437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968176,0.000028579077,0.00061309995,0.0009094348,0.0008156295,0.0008156393],"domain_scores_gemma":[0.9981428,0.00006098084,0.00054607756,0.0001689803,0.0008624768,0.0002187135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002994819,0.00047219027,0.00037510376,0.0002417754,0.0002561403,0.0010052104,0.0009628915,0.00015795606,0.00026153916],"category_scores_gemma":[0.000049937214,0.00039311833,0.000100431214,0.00030929796,0.00027954602,0.0012892286,0.00010667654,0.00075609866,0.00009693257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007195885,0.0011869598,0.012557578,0.00013028063,0.00033132546,0.00030954674,0.0004999106,0.075748116,0.006929545,0.7248089,0.017489284,0.15928896],"study_design_scores_gemma":[0.0010168017,0.00038729905,0.0033638268,0.00029979236,0.000016423575,0.00019795535,0.000075399184,0.9879368,0.0020538305,0.0021914416,0.001840114,0.00062034826],"about_ca_topic_score_codex":0.000027778782,"about_ca_topic_score_gemma":0.00003685533,"teacher_disagreement_score":0.93515277,"about_ca_system_score_codex":0.00015906301,"about_ca_system_score_gemma":0.00015848124,"threshold_uncertainty_score":0.99985206},"labels":[],"label_agreement":null},{"id":"W1590315623","doi":"10.1109/icra.2015.7139637","title":"Learning to assess terrain from human demonstration using an introspective Gaussian-process classifier","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Terrain; Artificial intelligence; Computer science; Classifier (UML); Robot; Gaussian process; Machine learning; Gaussian; Computer vision; Pattern recognition (psychology); Geography","score_opus":0.0913199434683088,"score_gpt":0.3428323832905072,"score_spread":0.2515124398221984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590315623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28500724,0.00000563739,0.7058391,0.00037917012,0.00013339058,0.00010725472,0.0000012489451,0.00021313205,0.008313833],"genre_scores_gemma":[0.8962373,2.6956843e-7,0.10311579,0.0002835037,0.00017233321,0.000011519466,0.000007695059,0.000014099911,0.00015749472],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811935,0.000110155845,0.00030513926,0.0006903205,0.00040916848,0.0003658633],"domain_scores_gemma":[0.9987417,0.000027504633,0.00013801333,0.00041776127,0.00025689523,0.0004181256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032678907,0.00021570301,0.00021737642,0.00014675183,0.00028419364,0.0008220008,0.00084284146,0.00010691057,0.000055513014],"category_scores_gemma":[0.00008510651,0.0001910146,0.000034101547,0.0005734594,0.000043934066,0.001924887,0.00017035952,0.00026825594,0.000049446226],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098668126,0.0012707999,0.113442555,0.00009714445,0.00017711284,0.00020650667,0.08079782,0.01890398,0.115060546,0.5471247,0.0013064034,0.121513784],"study_design_scores_gemma":[0.0012403537,0.0013682594,0.04125991,0.00015887931,0.00003169775,0.000052905336,0.010748995,0.70445853,0.027606351,0.21120448,0.00035751145,0.0015120864],"about_ca_topic_score_codex":0.0003365786,"about_ca_topic_score_gemma":0.00023496707,"teacher_disagreement_score":0.68555456,"about_ca_system_score_codex":0.00012602708,"about_ca_system_score_gemma":0.00027006635,"threshold_uncertainty_score":0.79265726},"labels":[],"label_agreement":null},{"id":"W1601835699","doi":"10.1007/978-3-642-01307-2_31","title":"Variational Bayesian Approach for Long-Term Relevance Feedback","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Term (time); Relevance (law); Relevance feedback; Bayesian probability; Artificial intelligence; Machine learning; Physics","score_opus":0.01575308813296563,"score_gpt":0.24331032627635701,"score_spread":0.22755723814339138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601835699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003595862,0.0004499603,0.98998284,0.0011198121,0.0008467426,0.00076094037,0.000012838741,0.0001948312,0.0066284174],"genre_scores_gemma":[0.039287094,0.00006257448,0.9569379,0.0017635442,0.00077678246,0.00003479969,0.000031698477,0.000042791,0.0010628175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949257,0.000024028988,0.0007060469,0.0022860435,0.0010978035,0.0009604239],"domain_scores_gemma":[0.9968715,0.00043817778,0.00047448697,0.0015111469,0.00044452734,0.00026020783],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007251959,0.00072107726,0.0006723226,0.00065922487,0.00040615725,0.0010444751,0.0046772724,0.000464756,0.000021909544],"category_scores_gemma":[0.00012502988,0.00066616794,0.00021414443,0.0007189751,0.0004711124,0.0010192419,0.000671991,0.0007553368,0.000021955018],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012128304,0.000070906615,0.0001098655,0.00015576821,0.000013851286,0.000029505514,0.0002240831,0.010162272,0.00002570915,0.13047488,0.000042906533,0.8586781],"study_design_scores_gemma":[0.00046520517,0.00027702056,0.0014443953,0.00036524516,0.000013743334,0.0001205635,4.892363e-8,0.5817944,0.00025960308,0.41375265,0.00050485716,0.0010022746],"about_ca_topic_score_codex":0.0000029505727,"about_ca_topic_score_gemma":0.000012775455,"teacher_disagreement_score":0.85767585,"about_ca_system_score_codex":0.0003094489,"about_ca_system_score_gemma":0.0010490865,"threshold_uncertainty_score":0.99999255},"labels":[],"label_agreement":null},{"id":"W1607933843","doi":"10.1080/10618600.2016.1237363","title":"Divide-and-Conquer With Sequential Monte Carlo","year":2016,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Vetenskapsrådet","keywords":"Divide and conquer algorithms; Computer science; Probabilistic logic; Graphical model; Inference; Monte Carlo method; Algorithm; Class (philosophy); Theoretical computer science; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.007713407732557479,"score_gpt":0.222033098086382,"score_spread":0.2143196903538245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1607933843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029661065,0.00012309084,0.96670204,0.0033652522,0.00006331795,0.000025940475,0.00002379976,0.000007263822,0.000028208999],"genre_scores_gemma":[0.85953313,0.000070121,0.14014348,0.00018237163,0.000047475663,5.416572e-7,3.0916752e-7,0.0000031810143,0.000019398407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99912703,0.000031246116,0.00026625607,0.00012587449,0.0003282696,0.00012134245],"domain_scores_gemma":[0.99895847,0.00032158164,0.00019042639,0.000051259212,0.00032311576,0.0001551388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013262268,0.00009212769,0.0001563266,0.00008361196,0.00007956648,0.00011722454,0.00015844665,0.000030828447,0.000007677155],"category_scores_gemma":[0.000039378912,0.00004857021,0.000022009124,0.000120863566,0.00016377955,0.00029939172,0.000054956396,0.000098917066,9.486499e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051803607,0.000046372963,0.008718745,0.000030076671,0.000070690796,0.00014479125,0.00012669124,0.00030655533,0.00003248298,0.8678696,0.0007036934,0.12189846],"study_design_scores_gemma":[0.0008652377,0.0005022351,0.10099654,0.000092337505,0.000022987719,0.0006894494,0.0000033220088,0.0068466724,0.000015091367,0.8889593,0.0008637823,0.00014304645],"about_ca_topic_score_codex":0.000004031295,"about_ca_topic_score_gemma":0.0000034481432,"teacher_disagreement_score":0.8298721,"about_ca_system_score_codex":0.00000812065,"about_ca_system_score_gemma":0.00009764926,"threshold_uncertainty_score":0.19806358},"labels":[],"label_agreement":null},{"id":"W1647070782","doi":"10.48550/arxiv.1207.2940","title":"Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institute for Advanced Research","keywords":"Statistical physics; Gaussian process; Computer science; Process (computing); Dynamical systems theory; Gaussian; Algorithm; Applied mathematics; Mathematics; Physics; Quantum mechanics; Programming language","score_opus":0.0371171118406686,"score_gpt":0.20241836727737011,"score_spread":0.16530125543670152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1647070782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25442734,0.000099264835,0.7418116,0.00009789863,0.0006487304,0.00053827284,0.000007632283,0.00022824328,0.0021410733],"genre_scores_gemma":[0.99878865,0.000046111283,0.00076400425,0.000016450062,0.00007448477,0.000007715747,0.000041902702,0.000017700098,0.00024298203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775916,0.00016843418,0.00032551208,0.0010814109,0.00018833985,0.00047715014],"domain_scores_gemma":[0.99840564,0.000042476444,0.00038844085,0.00077933,0.00019432441,0.00018979309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025759716,0.00034518586,0.0003496078,0.0004378982,0.00012517201,0.00021828164,0.0014001096,0.00039277718,0.000018182935],"category_scores_gemma":[0.000034408262,0.00036849253,0.00009648166,0.00090931414,0.00007215351,0.0011953975,0.0006589933,0.0005907464,0.00009844673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018117961,0.0009803309,0.023869315,0.00283947,0.00009443314,0.0006127003,0.0048983716,0.15838173,0.00019902826,0.8047283,0.00012641709,0.003088724],"study_design_scores_gemma":[0.00058902084,0.000056471985,0.015935028,0.00054578215,0.00003605737,0.000013587191,0.00054394227,0.9582988,0.0001266694,0.023207197,0.000024875175,0.0006225515],"about_ca_topic_score_codex":0.000119569006,"about_ca_topic_score_gemma":0.000037428006,"teacher_disagreement_score":0.7999171,"about_ca_system_score_codex":0.0004408518,"about_ca_system_score_gemma":0.00033744803,"threshold_uncertainty_score":0.9998767},"labels":[],"label_agreement":null},{"id":"W1680997276","doi":"10.1063/1.1381917","title":"A Bayesian revolution in spectral analysis","year":2001,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian probability; Computer science; Artificial intelligence","score_opus":0.015176765121618337,"score_gpt":0.2425491665540583,"score_spread":0.22737240143243997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1680997276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057524297,0.0000715088,0.9137479,0.0033781826,0.00007176027,0.00016121939,9.975745e-7,0.00022473116,0.024819378],"genre_scores_gemma":[0.98457235,0.00011991193,0.014624536,0.00023295195,0.000051083494,0.00005226315,0.0000021127644,0.000008091666,0.00033671927],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979382,0.000012132857,0.00039348815,0.000714577,0.00034272496,0.00059886655],"domain_scores_gemma":[0.9991365,0.000020013826,0.00015954449,0.0002708702,0.00024531572,0.00016774276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030445386,0.00023615328,0.00034398332,0.00064972375,0.00010768188,0.00047346056,0.001124534,0.00011594667,0.00017581641],"category_scores_gemma":[0.00006578353,0.0002258571,0.000114513605,0.0040288265,0.000065761196,0.0014431608,0.00017634653,0.0002706603,0.00006098658],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018472865,0.00013685662,0.6687709,0.000042101186,0.000070429654,0.00004772396,0.0025497028,0.0000080935115,0.0011018474,0.31608182,0.0003607371,0.010811315],"study_design_scores_gemma":[0.0004780774,0.00016804574,0.36639145,0.000096955104,0.000076013675,0.00007894516,0.00034621503,0.53475183,0.00027574864,0.09566247,0.0010412581,0.0006329681],"about_ca_topic_score_codex":0.00015440631,"about_ca_topic_score_gemma":0.00017079034,"teacher_disagreement_score":0.927048,"about_ca_system_score_codex":0.00010972057,"about_ca_system_score_gemma":0.00015123161,"threshold_uncertainty_score":0.92101854},"labels":[],"label_agreement":null},{"id":"W1739407491","doi":"","title":"A KNN based kalman filter Gaussian process regression","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Kalman filter; Gaussian process; k-nearest neighbors algorithm; Computer science; Gaussian; Artificial intelligence; Kriging; Pattern recognition (psychology); Gaussian filter; Regression; Extended Kalman filter; Synthetic data; Data mining; Filter (signal processing); Machine learning; Algorithm; Mathematics; Statistics; Computer vision; Image (mathematics)","score_opus":0.012865547940942013,"score_gpt":0.24810916797146,"score_spread":0.23524362003051796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1739407491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072721276,0.000033460245,0.9172427,0.01005994,0.00018465865,0.00031830792,8.3737956e-7,0.00046786596,0.064420074],"genre_scores_gemma":[0.9418395,0.0000020870734,0.053700797,0.0019407985,0.000049429564,0.00008466228,0.0000022759582,0.000011094776,0.0023693128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849474,0.000030654595,0.00023736224,0.00050140225,0.00032348256,0.0004123582],"domain_scores_gemma":[0.998853,0.000033952136,0.00009612681,0.0006619713,0.00014792611,0.00020699088],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000093158196,0.00020394546,0.00016569767,0.00011611886,0.00015045774,0.00046481015,0.0011541083,0.00008459315,0.0015471944],"category_scores_gemma":[0.000025563912,0.00013171481,0.00005863347,0.00048230356,0.00004434651,0.0011853351,0.00013819698,0.00014403152,0.0009933598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024876681,0.0009328393,0.016723748,0.00086809543,0.00004986327,0.00012721142,0.002289786,0.00008028296,0.0050587654,0.3817132,0.12588722,0.4662441],"study_design_scores_gemma":[0.0019773787,0.0005943262,0.055687852,0.0007442708,0.00001766263,0.00009272727,0.00022992998,0.7099997,0.05975393,0.15798923,0.010831582,0.0020814508],"about_ca_topic_score_codex":0.000046294972,"about_ca_topic_score_gemma":0.0000063890116,"teacher_disagreement_score":0.9345674,"about_ca_system_score_codex":0.000017699022,"about_ca_system_score_gemma":0.00013629082,"threshold_uncertainty_score":0.99978447},"labels":[],"label_agreement":null},{"id":"W1764471553","doi":"10.48550/arxiv.1203.1269","title":"A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Graphics; Computer science; Process (computing); Gaussian process; Computer graphics (images); Gaussian; Data mining; Programming language","score_opus":0.15053879344802013,"score_gpt":0.2594343199998393,"score_spread":0.10889552655181917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1764471553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048814584,0.000105074374,0.9490346,0.0000677053,0.0003659765,0.00064021,0.0003619285,0.00027567893,0.0003342575],"genre_scores_gemma":[0.99218047,0.000050666058,0.007072418,0.00019138657,0.00017212608,0.000006049079,0.00023657232,0.00005304464,0.000037280774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965783,0.00007624038,0.0004004176,0.0016721969,0.00024031824,0.0010325701],"domain_scores_gemma":[0.997319,0.00006454677,0.00035136993,0.0013693278,0.0005195555,0.00037622402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005242134,0.00063237123,0.00053246453,0.00050626637,0.0006619639,0.00044462847,0.0026934922,0.0005156411,0.00000454362],"category_scores_gemma":[0.000068963105,0.00067651103,0.00017112686,0.0015127228,0.00006866555,0.0013883923,0.0013307221,0.0009033092,0.0000090950625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015664776,0.0006043379,0.0012290512,0.003120173,0.00014313839,0.00020549676,0.0015405047,0.49980736,0.000038048325,0.48826724,0.00010941008,0.004778591],"study_design_scores_gemma":[0.00037239218,0.00004391728,0.00004713521,0.00070946803,0.00013481233,0.000011100397,0.00008133711,0.96026593,0.00010726976,0.03725621,0.00015895019,0.0008114915],"about_ca_topic_score_codex":0.000037222395,"about_ca_topic_score_gemma":0.000032695523,"teacher_disagreement_score":0.9433659,"about_ca_system_score_codex":0.00018875049,"about_ca_system_score_gemma":0.00087914284,"threshold_uncertainty_score":0.9995686},"labels":[],"label_agreement":null},{"id":"W1781062201","doi":"10.48550/arxiv.1102.1492","title":"On Nonparametric Guidance for Learning Autoencoder Representations","year":2011,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; University of Toronto","funders":"","keywords":"Autoencoder; Discriminative model; Computer science; Artificial intelligence; Visualization; Unsupervised learning; Machine learning; Nonparametric statistics; Deep learning; Exploratory data analysis; Feature learning; Data visualization; Exploratory analysis; Pattern recognition (psychology); Data science; Data mining; Mathematics; Econometrics","score_opus":0.09050904946150796,"score_gpt":0.20413681251530583,"score_spread":0.11362776305379786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1781062201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01746249,0.000011011566,0.961701,0.000048356862,0.000117805015,0.00012594843,0.0000013497706,0.00016089428,0.020371139],"genre_scores_gemma":[0.96539474,0.000011411026,0.03142253,0.00010000082,0.000014060059,0.0000017624632,0.0000010045106,0.000006736079,0.0030477843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990454,0.00003619436,0.000108236774,0.00051246607,0.00005107605,0.00024663878],"domain_scores_gemma":[0.9991084,0.00015140693,0.00010485517,0.00042676774,0.00011674715,0.00009185775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011741153,0.000110309105,0.00010459204,0.00019753742,0.00022389751,0.00005373855,0.000757296,0.000051637544,0.00004755109],"category_scores_gemma":[0.00013431905,0.00011590362,0.00007509627,0.0010854849,0.000046762376,0.000498151,0.00011220562,0.00012191008,0.00011314386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017613882,0.000070678405,0.0037040866,0.000013137999,0.00001268087,0.00003100119,0.00028678737,0.010935752,0.000012860965,0.98325706,0.0003402085,0.0013181322],"study_design_scores_gemma":[0.00052108115,0.00029881566,0.010187418,0.000028764762,0.00001665008,0.0000047159715,0.00007928942,0.75139296,0.00053210853,0.23609637,0.00056087103,0.00028098666],"about_ca_topic_score_codex":0.00003498117,"about_ca_topic_score_gemma":0.000006164171,"teacher_disagreement_score":0.94793224,"about_ca_system_score_codex":0.000037432255,"about_ca_system_score_gemma":0.0000605827,"threshold_uncertainty_score":0.47264123},"labels":[],"label_agreement":null},{"id":"W1809492646","doi":"10.48550/arxiv.1505.04413","title":"Harmonic Exponential Families on Manifolds","year":2015,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Exponential family; Conjugate prior; Mathematics; Generalization; Harmonic; Exponential function; Algorithm; Computer science; Fourier transform; Factorization; Prior probability; Applied mathematics; Artificial intelligence; Bayesian probability; Mathematical analysis; Physics","score_opus":0.024167879557021404,"score_gpt":0.20532025045368718,"score_spread":0.18115237089666578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1809492646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40198752,0.00012660549,0.5409953,0.0035828468,0.0007787973,0.00022938866,0.000023151357,0.0003175242,0.05195884],"genre_scores_gemma":[0.9868365,0.000023615301,0.011438742,0.00013408139,0.000033151126,1.8202078e-7,0.0000041147414,0.000006312147,0.0015233115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884903,0.000042791427,0.000110306784,0.00036815144,0.00037232522,0.00025741916],"domain_scores_gemma":[0.999013,0.00002501062,0.00012403038,0.0005102711,0.00014961876,0.00017804521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012336014,0.00014626818,0.00020193792,0.00017960159,0.00013833845,0.000069714,0.0012333344,0.000079147976,0.00007546835],"category_scores_gemma":[0.000011594495,0.00016293442,0.00009084916,0.00033228158,0.00010130278,0.0006835396,0.00043842042,0.00012052001,0.00026128648],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006601431,0.0013447601,0.007471862,0.0005452366,0.00033670224,0.0015080882,0.0482636,0.0002656919,0.003723141,0.45110676,0.08830584,0.3964682],"study_design_scores_gemma":[0.027691074,0.012697866,0.28119805,0.0022066461,0.00044376406,0.0005098087,0.06720258,0.07668046,0.026477978,0.11288585,0.3840513,0.007954635],"about_ca_topic_score_codex":0.00018464767,"about_ca_topic_score_gemma":0.000054921165,"teacher_disagreement_score":0.58484894,"about_ca_system_score_codex":0.000058988087,"about_ca_system_score_gemma":0.0001549952,"threshold_uncertainty_score":0.6644273},"labels":[],"label_agreement":null},{"id":"W1826607394","doi":"10.1007/978-3-642-10268-4_94","title":"Generating Video Textures by PPCA and Gaussian Process Dynamical Model","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Gaussian process; Process (computing); Computer vision; Gaussian; Computer graphics (images); Pattern recognition (psychology); Programming language","score_opus":0.009307873704370624,"score_gpt":0.24159422331523134,"score_spread":0.23228634961086073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1826607394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016372518,0.0011945148,0.9933276,0.0018661439,0.0002662605,0.0003024864,0.000010199182,0.0001851208,0.0026839445],"genre_scores_gemma":[0.47472623,0.000101796286,0.52068317,0.0036009827,0.00027857308,0.000012381555,0.0000088556235,0.000043435717,0.0005445678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953753,0.000023539784,0.0006055328,0.0021176878,0.001007767,0.00087016827],"domain_scores_gemma":[0.9979808,0.00014575545,0.0003242218,0.0010080404,0.00021102998,0.00033020368],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00046162895,0.000730735,0.0006296964,0.00048592148,0.00044172019,0.0013952598,0.0030396674,0.00046030307,0.000008044568],"category_scores_gemma":[0.000070228285,0.00062355644,0.00010932635,0.00051998836,0.0006720505,0.0009754482,0.0008125759,0.001030942,0.000008576079],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050461854,0.0000389219,0.000020108415,0.00010913509,0.000010082847,0.000059827133,0.0007554161,0.053617816,0.00045656448,0.035644237,0.000068769994,0.9092141],"study_design_scores_gemma":[0.00014003289,0.00011066525,0.000014155833,0.00027544601,0.000006652318,0.00008697642,1.4187289e-7,0.72998667,0.0003994453,0.26829767,0.000094978284,0.0005871816],"about_ca_topic_score_codex":0.00000889667,"about_ca_topic_score_gemma":0.000038704224,"teacher_disagreement_score":0.9086269,"about_ca_system_score_codex":0.00015450407,"about_ca_system_score_gemma":0.0007097517,"threshold_uncertainty_score":0.99964136},"labels":[],"label_agreement":null},{"id":"W1827716646","doi":"10.1007/s10514-015-9455-y","title":"Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression","year":2015,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Cambridge","keywords":"Smoothing; Trajectory; Computer science; Gaussian process; Covariance; Nonlinear system; Stochastic differential equation; Interpolation (computer graphics); Applied mathematics; Mathematics; Algorithm; Mathematical optimization; Gaussian; Statistics; Artificial intelligence","score_opus":0.019018395681047146,"score_gpt":0.27177226131322824,"score_spread":0.2527538656321811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1827716646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10148283,0.00048115593,0.8459936,0.0045848675,0.0011561185,0.0009564409,0.000010564185,0.0017300175,0.04360442],"genre_scores_gemma":[0.8770269,0.000011094519,0.11929992,0.00046604942,0.00016875101,0.000048265912,0.00002602014,0.000040789633,0.0029121842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975056,0.0000879115,0.00049203483,0.0007774178,0.00054771383,0.000589319],"domain_scores_gemma":[0.9980925,0.00005518157,0.00033158733,0.00079973176,0.00026861066,0.00045239695],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039898278,0.0003722589,0.00041905546,0.00017653155,0.00019468981,0.00042222138,0.0012398323,0.00019800928,0.0000742312],"category_scores_gemma":[0.00015358522,0.0003134997,0.00008593071,0.00052921975,0.00008710184,0.0014039602,0.00019219278,0.00028374812,0.0014859481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024795934,0.0020794075,0.0037065805,0.0005015272,0.00018382155,0.0010565134,0.020005908,0.046818007,0.003921637,0.030355051,0.016514018,0.8746096],"study_design_scores_gemma":[0.0018196241,0.00086234655,0.0029314659,0.00041318225,0.000043370335,0.0004204324,0.00021942864,0.9540413,0.012453585,0.020286636,0.005177179,0.0013314753],"about_ca_topic_score_codex":0.00007906269,"about_ca_topic_score_gemma":0.000008729922,"teacher_disagreement_score":0.9072233,"about_ca_system_score_codex":0.00013612647,"about_ca_system_score_gemma":0.0010505463,"threshold_uncertainty_score":0.9999317},"labels":[],"label_agreement":null},{"id":"W1843364611","doi":"10.1073/pnas.1507266112","title":"Random sampling of skewed distributions does not necessarily imply Taylor’s law","year":2015,"lang":"en","type":"letter","venue":"Proceedings of the National Academy of Sciences","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Sampling (signal processing); Law; Statistics; Statistical physics; Mathematics; Political science; Computer science; Physics","score_opus":0.05460763403163593,"score_gpt":0.3073939504018117,"score_spread":0.2527863163701758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843364611","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051280675,0.0003812737,0.0057909563,0.9693265,0.0004187228,0.00090582354,0.0009273727,0.000111122645,0.017010164],"genre_scores_gemma":[0.94267905,0.000028958022,0.018945267,0.037491195,0.0005873311,0.000029968422,0.0000031209486,0.00001146022,0.00022363829],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952382,0.000019659716,0.0009078201,0.0006382921,0.0028000588,0.00039596017],"domain_scores_gemma":[0.99609464,0.0004892032,0.0018222459,0.00004625691,0.0014818119,0.00006585114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002087243,0.0002870595,0.0005746695,0.00027278322,0.00035678953,0.00019856884,0.0052059214,0.0004286336,0.0000109060775],"category_scores_gemma":[0.00094116526,0.00016851032,0.00023425954,0.0014394962,0.0017670316,0.001256995,0.0007666541,0.00087558816,0.0000020649168],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032274518,0.000093832605,0.0004891794,0.0010535922,0.000098572345,1.2204987e-7,0.0004585487,0.000049356946,0.03094956,0.8413226,0.12448476,0.00096756977],"study_design_scores_gemma":[0.0008340882,0.00012297723,0.0020074847,0.0006914907,0.00007643937,0.000036185826,0.00006673012,0.001973548,0.17525929,0.78178483,0.036578793,0.0005681651],"about_ca_topic_score_codex":0.000044542845,"about_ca_topic_score_gemma":2.7356822e-7,"teacher_disagreement_score":0.937551,"about_ca_system_score_codex":0.0000705449,"about_ca_system_score_gemma":0.00025345414,"threshold_uncertainty_score":0.96739835},"labels":[],"label_agreement":null},{"id":"W1914588449","doi":"10.18637/jss.v019.i09","title":"<b>tgp</b>: An<i>R</i>Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":206,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Bayesian probability; Gaussian process; Semiparametric regression; Gaussian; Computer science; Nonlinear system; Bayesian inference; Inference; Dimension (graph theory); Mathematics; Applied mathematics; Algorithm; Regression; Artificial intelligence; Statistics","score_opus":0.022667416435603303,"score_gpt":0.3016473060079822,"score_spread":0.27897988957237885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1914588449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010837569,0.00037852922,0.9975265,0.000382064,0.00012670926,0.0002815928,0.00012341166,0.00005504869,0.0000423652],"genre_scores_gemma":[0.27791572,0.00004419649,0.7216993,0.00019336722,0.000081284656,0.000005720613,0.000015714215,0.000020462758,0.000024250401],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755037,0.00007727622,0.00080979633,0.0004137419,0.0006443329,0.00050447544],"domain_scores_gemma":[0.99660355,0.0014152176,0.0005443805,0.00025017193,0.00057750795,0.00060917315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010473392,0.0002715984,0.0004373622,0.00029702828,0.0002441567,0.00028697704,0.0006973373,0.00015269093,0.000015823629],"category_scores_gemma":[0.00079857634,0.00020322412,0.00006540014,0.00062087784,0.00013032032,0.0014687871,0.000061986706,0.0003245507,0.000001717587],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013624066,0.0015980052,0.0024926176,0.0009351452,0.0001420148,0.00069790747,0.0028041378,0.0011282361,0.0010008246,0.06165798,0.014256533,0.9119242],"study_design_scores_gemma":[0.003926696,0.004583565,0.0038420316,0.000610692,0.00014247917,0.0010280065,0.0004338151,0.45055735,0.004622402,0.52843916,0.0006572892,0.0011565019],"about_ca_topic_score_codex":0.000004553065,"about_ca_topic_score_gemma":0.0000021869546,"teacher_disagreement_score":0.9107677,"about_ca_system_score_codex":0.00006146475,"about_ca_system_score_gemma":0.00030166862,"threshold_uncertainty_score":0.8287239},"labels":[],"label_agreement":null},{"id":"W1917694786","doi":"10.48550/arxiv.1406.4631","title":"A Sober Look at Spectral Learning","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Psychology; Cognitive science","score_opus":0.037164928525920404,"score_gpt":0.1706752679656027,"score_spread":0.1335103394396823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1917694786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15275659,0.00004617049,0.826537,0.00017524388,0.00040489837,0.0001344911,0.000002192704,0.00036229676,0.019581096],"genre_scores_gemma":[0.9851011,0.0000928343,0.0026577178,0.00012877857,0.00012173846,6.788995e-7,0.0000075046046,0.000019199662,0.011870426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779487,0.00011646192,0.00018805017,0.0012789204,0.0001135763,0.0005081526],"domain_scores_gemma":[0.99827564,0.000069763795,0.00027823978,0.0010450536,0.00010876103,0.00022253516],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019050263,0.00035370167,0.00035133233,0.00019163106,0.00026636335,0.00023149364,0.0021211333,0.00029361295,0.00016160724],"category_scores_gemma":[0.00003171526,0.00038806515,0.00022162884,0.00038562645,0.00011148406,0.00031164812,0.003195783,0.00083990104,0.000700995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038565202,0.00011639872,0.019627746,0.00034026013,0.00013522722,0.0008455012,0.000603257,0.1714153,0.000096076474,0.80259615,0.0016962662,0.002489257],"study_design_scores_gemma":[0.0005667859,0.00012971519,0.003442611,0.00020127743,0.00006953745,0.00003556516,0.00003405532,0.8298418,0.00044600182,0.15730378,0.006831872,0.0010969813],"about_ca_topic_score_codex":0.000056851342,"about_ca_topic_score_gemma":0.00003695896,"teacher_disagreement_score":0.83234453,"about_ca_system_score_codex":0.00023548213,"about_ca_system_score_gemma":0.00020583323,"threshold_uncertainty_score":0.9998571},"labels":[],"label_agreement":null},{"id":"W1927271208","doi":"","title":"Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models","year":2013,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaussian process; Inference; Gaussian; Bayesian inference; Computer science; Conjugate prior; Algorithm; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Bayesian probability; Applied mathematics; Bayes' theorem; Physics","score_opus":0.015120198043976034,"score_gpt":0.24996899536664924,"score_spread":0.2348487973226732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1927271208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046237204,0.000024900855,0.9840658,0.0034407019,0.00032906595,0.0013536209,0.000039186787,0.0005109972,0.00561205],"genre_scores_gemma":[0.7021466,0.000024931982,0.29417017,0.0015943524,0.000106778934,0.001163699,0.000009993229,0.000021983444,0.00076149707],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635965,0.000056532674,0.0007093369,0.0009813815,0.00060176145,0.0012913143],"domain_scores_gemma":[0.99748397,0.00022307255,0.00036801235,0.00096633885,0.00046827085,0.0004903555],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006946551,0.00043772283,0.00039927397,0.0003612979,0.0005231995,0.0011259135,0.002342408,0.00028103075,0.00012651553],"category_scores_gemma":[0.00016445837,0.00041394227,0.00016587142,0.0010758523,0.00026875082,0.004105564,0.0005681273,0.00036461547,0.00016571222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002175389,0.00037676722,0.002429478,0.00016951688,0.000032362255,0.000025679768,0.0016865087,0.00566684,0.019112175,0.92503476,0.006053442,0.039390717],"study_design_scores_gemma":[0.00043618193,0.0003404619,0.008204987,0.00014423604,0.000009308818,0.000056352685,0.000032334032,0.76579744,0.015829798,0.20783608,0.00059198186,0.0007208503],"about_ca_topic_score_codex":0.0003750777,"about_ca_topic_score_gemma":0.00004658231,"teacher_disagreement_score":0.7601306,"about_ca_system_score_codex":0.00016427126,"about_ca_system_score_gemma":0.00088400365,"threshold_uncertainty_score":0.999911},"labels":[],"label_agreement":null},{"id":"W1950803081","doi":"10.1609/aaai.v28i1.8904","title":"Automatic Construction and Natural-Language Description of Nonparametric Regression Models","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smoothness; Extrapolation; Nonparametric regression; Computer science; Nonparametric statistics; Regression; Natural language; Statistician; Regression analysis; Set (abstract data type); Artificial intelligence; Machine learning; Econometrics; Mathematics; Statistics; Programming language","score_opus":0.035485039362256734,"score_gpt":0.2663903902323852,"score_spread":0.23090535087012848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1950803081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64408815,0.00010607253,0.3505838,0.00086193695,0.000415702,0.00029667135,0.000001824326,0.00008904509,0.0035568373],"genre_scores_gemma":[0.9729409,0.000048040667,0.026875649,0.000052231666,0.000023125558,0.000008875081,2.495248e-7,0.0000069556622,0.000043933374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984173,0.00002370248,0.0005200415,0.00039516235,0.00041884772,0.00022494356],"domain_scores_gemma":[0.99851894,0.00009363773,0.0005868353,0.0002661773,0.00046697556,0.000067409914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040256095,0.0001934762,0.00028559312,0.00023660346,0.00013288793,0.00019547837,0.0010008212,0.00008768881,0.000015584781],"category_scores_gemma":[0.0003760504,0.00013098586,0.00006895042,0.0008789432,0.0003284034,0.0007808101,0.00025791654,0.00022534798,0.000007064754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011841816,0.000039351642,0.00012503093,0.000114935865,0.0000049504847,7.944694e-8,0.00096396613,0.000015219431,0.027845956,0.6168413,0.0000103807915,0.35402694],"study_design_scores_gemma":[0.000024188674,0.00011664228,0.00034759645,0.00036997176,0.000009176879,0.000010022785,0.00033248673,0.5308108,0.20949589,0.25836053,0.0000028354675,0.00011988819],"about_ca_topic_score_codex":0.000033968467,"about_ca_topic_score_gemma":0.0000034268203,"teacher_disagreement_score":0.5307956,"about_ca_system_score_codex":0.000022376284,"about_ca_system_score_gemma":0.000051691677,"threshold_uncertainty_score":0.5341449},"labels":[],"label_agreement":null},{"id":"W1979024706","doi":"10.1016/j.neuroimage.2014.04.018","title":"Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; National Institute on Aging; University of California, San Diego; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Genentech; National Institutes of Health; Servier; Eisai; Bundesministerium für Bildung und Forschung; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; Deutscher Akademischer Austauschdienst; National Center for Research Resources; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Wellcome Trust; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Medical Research Council; Meso Scale Diagnostics; Foundation for the National Institutes of Health","keywords":"Normative; Inference; Dementia; Psychology; Disease; Artificial intelligence; Medicine; Machine learning; Computer science; Pathology","score_opus":0.006741152433292742,"score_gpt":0.22783316728596165,"score_spread":0.2210920148526689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979024706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5544191,0.000034209344,0.44419947,0.00023773927,0.00006295573,0.00009921073,0.000007599641,0.000047399877,0.00089231366],"genre_scores_gemma":[0.99829894,0.0000054308302,0.0014158933,0.00024131486,0.000012706799,0.000011755732,0.0000015575995,0.0000057867055,0.0000066267976],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99893636,0.00008847489,0.00023173413,0.00034482716,0.00019450637,0.00020409867],"domain_scores_gemma":[0.99954826,0.000041910702,0.0001031206,0.00019477628,0.00004264704,0.00006929081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018428512,0.00013410824,0.0001657328,0.00010623913,0.000059274356,0.00013611479,0.00019008532,0.00006406512,0.0000047573244],"category_scores_gemma":[0.000024408015,0.00012147502,0.000017883322,0.00031832934,0.00013561029,0.00052318274,0.000059422473,0.00010981458,0.0000025193583],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018755096,0.00026530813,0.6876938,0.0017487528,0.000017236593,0.000045258475,0.001867317,0.00014706282,0.0064877137,0.01185424,0.0000752293,0.2896106],"study_design_scores_gemma":[0.0011517727,0.000514405,0.95083606,0.00008861309,0.00000880189,0.000061986866,0.000054795542,0.029554607,0.010412335,0.0070565133,0.00008195582,0.000178139],"about_ca_topic_score_codex":0.00007168137,"about_ca_topic_score_gemma":0.000076764656,"teacher_disagreement_score":0.4438798,"about_ca_system_score_codex":0.000012299812,"about_ca_system_score_gemma":0.00004075504,"threshold_uncertainty_score":0.4953608},"labels":[],"label_agreement":null},{"id":"W2019752194","doi":"10.1093/biomet/asm065","title":"Use of the Gibbs Sampler to Obtain Conditional Tests, with Applications","year":2007,"lang":"en","type":"article","venue":"Biometrika","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Mathematics; Statistics; Statistic; Gibbs sampling; Conditional probability distribution; Sampling distribution; Distribution (mathematics); Parametric statistics; Applied mathematics; Mathematical analysis","score_opus":0.036142068515594525,"score_gpt":0.2701076191465338,"score_spread":0.23396555063093927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019752194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006627551,0.000029941546,0.9915537,0.0010023342,0.000035887588,0.00022912043,0.000029015342,0.00003554618,0.00045685712],"genre_scores_gemma":[0.8415402,0.0000015084779,0.15767537,0.00052993355,0.0000288821,0.000020073308,0.0000035461528,0.000004072422,0.00019636711],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99912894,0.000010189052,0.0001682822,0.00020998188,0.0003012662,0.00018132111],"domain_scores_gemma":[0.9989891,0.0002037035,0.00009279156,0.00045260656,0.00016954815,0.00009225762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018509416,0.00007677843,0.00008526231,0.00032851956,0.0000924541,0.000080528596,0.0006504715,0.00003301806,0.000018781562],"category_scores_gemma":[0.00008016278,0.00004732804,0.000029246388,0.004433507,0.00007258347,0.00019651256,0.00013335134,0.000053716336,0.00002830021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015376547,0.0002571146,0.038215622,0.000043660657,0.00003507728,0.0000035085766,0.00020127288,0.000033631797,0.0066392426,0.8530949,0.0043377425,0.0971229],"study_design_scores_gemma":[0.00022979367,0.0001775714,0.78352135,0.000034399854,0.000008873934,0.000026295898,0.000020044405,0.00016576331,0.019269565,0.008912539,0.1873945,0.00023931239],"about_ca_topic_score_codex":0.000035401194,"about_ca_topic_score_gemma":0.000022746366,"teacher_disagreement_score":0.8441823,"about_ca_system_score_codex":0.000025648082,"about_ca_system_score_gemma":0.00012259369,"threshold_uncertainty_score":0.21301532},"labels":[],"label_agreement":null},{"id":"W2024935685","doi":"10.1109/cvpr.2009.5206576","title":"Shared Kernel Information Embedding for discriminative inference","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Latent variable; Inference; Embedding; Kernel (algebra); Computer science; Artificial intelligence; Machine learning; Multiple kernel learning; Pattern recognition (psychology); Latent variable model; Kernel method; Data mining; Mathematics; Support vector machine","score_opus":0.04136144629903507,"score_gpt":0.3113860563562339,"score_spread":0.2700246100571988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024935685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010852797,0.000013732144,0.9852572,0.0018119337,0.00049900974,0.00043560978,0.0000723701,0.00017398433,0.00088335207],"genre_scores_gemma":[0.95823634,0.000056841553,0.038771257,0.0026335872,0.0001152433,0.000035967772,0.000117279225,0.0000067045003,0.000026760512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983798,0.00006064543,0.0004212842,0.0005076765,0.00028595468,0.00034462337],"domain_scores_gemma":[0.9987153,0.00014702698,0.00025251988,0.0002958176,0.00041824108,0.00017112694],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00019993435,0.0003100985,0.00027369751,0.00025464187,0.00024016012,0.0010811836,0.0004699622,0.00013210255,0.000034464298],"category_scores_gemma":[0.00003264098,0.00026488688,0.00007589172,0.00021208424,0.000038067374,0.0023843301,0.000066369925,0.00020145383,0.00011649446],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022147717,0.00007866812,0.00002850646,0.000049378446,0.000006186015,0.0000031953946,0.000648098,0.000018373306,0.000110004126,0.0056797327,0.000987588,0.9923681],"study_design_scores_gemma":[0.001313147,0.002396304,0.011821667,0.0008525253,0.000015537764,0.000024968527,0.000056661538,0.9181212,0.0011279841,0.063126504,0.00046092086,0.0006825293],"about_ca_topic_score_codex":0.000007329885,"about_ca_topic_score_gemma":0.0000033350298,"teacher_disagreement_score":0.99168557,"about_ca_system_score_codex":0.000031947064,"about_ca_system_score_gemma":0.00006649573,"threshold_uncertainty_score":0.99998033},"labels":[],"label_agreement":null},{"id":"W2046916337","doi":"10.1137/130941912","title":"Massively Parallel Approximate Gaussian Process Regression","year":2014,"lang":"en","type":"article","venue":"SIAM/ASA Journal on Uncertainty Quantification","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Computer science; Emulation; Massively parallel; Gaussian process; Set (abstract data type); Computation; Parallel computing; Big data; General-purpose computing on graphics processing units; Process (computing); Scheme (mathematics); Multiprocessing; CUDA; Computational science; Theoretical computer science; Gaussian; Algorithm; Graphics; Data mining; Mathematics; Computer graphics (images)","score_opus":0.023162448693726707,"score_gpt":0.2831799163398947,"score_spread":0.260017467646168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046916337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008986951,0.000096085656,0.9753512,0.00955561,0.00074558816,0.000270211,0.0000031937402,0.0002127706,0.0047783614],"genre_scores_gemma":[0.97726065,0.00010663291,0.021253346,0.0006244027,0.00028376622,0.000034355213,0.000015361817,0.000027529972,0.00039394235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967146,0.00027665225,0.00076037424,0.0007603974,0.0008669753,0.0006210218],"domain_scores_gemma":[0.997201,0.00013866753,0.00093980785,0.00088686164,0.00047115562,0.00036247584],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011679846,0.00038352824,0.00037610508,0.00033059323,0.0007473894,0.00095876673,0.0017054759,0.00018094007,0.000056585188],"category_scores_gemma":[0.00031233914,0.00027557797,0.00015459557,0.00074375264,0.00010563059,0.0010584604,0.00008195044,0.0006549013,0.00022542985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018788374,0.00044262115,0.00065040536,0.00022365745,0.00004666959,0.00003801232,0.0012826587,0.0072687105,0.00323291,0.78935575,0.0028456694,0.19442506],"study_design_scores_gemma":[0.0023365347,0.0011063693,0.010620424,0.001350639,0.000043600954,0.00057949894,0.00039330655,0.6447027,0.007737048,0.3071171,0.022471962,0.0015408021],"about_ca_topic_score_codex":0.0000058401224,"about_ca_topic_score_gemma":0.000003179154,"teacher_disagreement_score":0.9682737,"about_ca_system_score_codex":0.00012177316,"about_ca_system_score_gemma":0.00024926494,"threshold_uncertainty_score":0.99996966},"labels":[],"label_agreement":null},{"id":"W2053652664","doi":"10.1080/10618600.2013.841584","title":"Parallel Bayesian Additive Regression Trees","year":2014,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Office of Science; U.S. Department of Energy","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Boosting (machine learning); Approximate Bayesian computation; Bayesian inference; Inference; Computation; Machine learning; Algorithm; Artificial intelligence","score_opus":0.007996393648830565,"score_gpt":0.24549760152655614,"score_spread":0.23750120787772558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053652664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011812861,0.0000841874,0.99669147,0.0016834649,0.00011646924,0.000025912137,0.000017355038,0.000010630448,0.00018925351],"genre_scores_gemma":[0.59687316,0.000048513495,0.402731,0.00025858107,0.00007125547,5.1469715e-7,0.000003545736,0.0000028442594,0.000010561923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988847,0.00007508211,0.0003696555,0.00013755925,0.00039748257,0.0001355416],"domain_scores_gemma":[0.998534,0.0005623002,0.00031061185,0.00007058605,0.00034347142,0.00017902942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021869125,0.00010990969,0.000203586,0.0001227798,0.00012323595,0.00013372905,0.00027525326,0.000046967034,0.000014341451],"category_scores_gemma":[0.0001401353,0.00007571774,0.000047245394,0.00020578821,0.00010411291,0.00025054402,0.000055811903,0.00019070903,0.00000225358],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025515923,0.00005464186,0.0005793546,0.000014004884,0.000019758514,0.000026324671,0.00010105406,0.0008142871,0.0000033477659,0.86256975,0.0025302297,0.13326173],"study_design_scores_gemma":[0.00037118563,0.000306753,0.050632033,0.000051988496,0.000008859745,0.00014272096,0.0000066999733,0.1411783,0.000005108753,0.805434,0.0017667932,0.0000955595],"about_ca_topic_score_codex":0.000001666933,"about_ca_topic_score_gemma":0.0000022141562,"teacher_disagreement_score":0.5956919,"about_ca_system_score_codex":0.000007893298,"about_ca_system_score_gemma":0.000072690586,"threshold_uncertainty_score":0.30876797},"labels":[],"label_agreement":null},{"id":"W2055246903","doi":"10.1109/access.2015.2425304","title":"A Deep-Structured Fully Connected Random Field Model for Structured Inference","year":2015,"lang":"en","type":"article","venue":"IEEE Access","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation","keywords":"Computer science; Inference; Field (mathematics); Artificial intelligence; Mathematics","score_opus":0.047719736243344114,"score_gpt":0.3189510584323415,"score_spread":0.27123132218899737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055246903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015367294,0.000089881345,0.9814936,0.0008858999,0.000880337,0.0004758046,0.000011984439,0.00023326825,0.0005619509],"genre_scores_gemma":[0.94293195,0.0000067121887,0.055501092,0.0011923086,0.00013727025,0.00009623309,0.000007072525,0.000015809996,0.00011154506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815625,0.000040619634,0.000369983,0.00059757236,0.00034759447,0.00048799763],"domain_scores_gemma":[0.9980263,0.00025937118,0.0002059166,0.00070625014,0.00052012486,0.00028202354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001813439,0.0002779311,0.00035950867,0.00012715375,0.00014644927,0.00083826313,0.0025664838,0.00018097923,0.00001858057],"category_scores_gemma":[0.00047095644,0.00022945012,0.00009351717,0.00047547187,0.000046884637,0.0014174406,0.0002514083,0.00019672548,0.000008159439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033857052,0.00044640203,0.0083222855,0.0014345202,0.0005221228,0.00015543206,0.017981522,0.1645794,0.009444853,0.40382433,0.059624564,0.33027884],"study_design_scores_gemma":[0.0022941313,0.00009902377,0.00022692441,0.000023503273,0.00001425315,0.00001329226,0.000012858749,0.78476554,0.008141444,0.20392223,0.00016670926,0.00032007325],"about_ca_topic_score_codex":0.000042979413,"about_ca_topic_score_gemma":0.00020099353,"teacher_disagreement_score":0.9275647,"about_ca_system_score_codex":0.00003841254,"about_ca_system_score_gemma":0.00045578487,"threshold_uncertainty_score":0.93567044},"labels":[],"label_agreement":null},{"id":"W2058444317","doi":"10.1109/iros.2010.5649191","title":"Active estimation of object dynamics parameters with tactile sensors","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Dynamics (music); Object (grammar); Computer vision; Estimation; Artificial intelligence; Tactile sensor; Acoustics; Engineering; Robot; Physics","score_opus":0.0051503375663771046,"score_gpt":0.2184883523059266,"score_spread":0.21333801473954947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058444317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3525912,3.635804e-7,0.6402649,0.00021618967,0.000068008,0.00006283183,0.0000018052981,0.000054542696,0.006740157],"genre_scores_gemma":[0.7240087,5.3327074e-7,0.27586773,0.000026203063,0.000003075084,0.000003299008,0.0000012650528,0.0000031156778,0.00008606866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993937,0.000009627812,0.00011093133,0.00019531175,0.00015694974,0.00013348645],"domain_scores_gemma":[0.99939543,0.000062059866,0.000102575534,0.00031258282,0.000077743236,0.00004962667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004877608,0.00008614617,0.00010416968,0.000061478815,0.00004047606,0.00006412373,0.00032030724,0.000043546635,0.000021876775],"category_scores_gemma":[0.000029303093,0.000060849554,0.000022154087,0.0002728882,0.000058260648,0.00048620295,0.000044767552,0.0001317934,0.00001112515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004814569,0.00018586352,0.0019594682,0.00009232838,0.0000576065,0.000016746568,0.0014082218,0.0048538153,0.002626267,0.61954075,0.00012027094,0.36909053],"study_design_scores_gemma":[0.00018965115,0.00017884705,0.010219276,0.000018100267,0.0000071621694,0.000040785384,0.000094574265,0.9462437,0.034828033,0.007992108,0.000016622957,0.00017114579],"about_ca_topic_score_codex":0.000081527716,"about_ca_topic_score_gemma":0.0001288995,"teacher_disagreement_score":0.94138986,"about_ca_system_score_codex":0.000012718998,"about_ca_system_score_gemma":0.00008346888,"threshold_uncertainty_score":0.24813728},"labels":[],"label_agreement":null},{"id":"W2095897186","doi":"","title":"Expectation Propagation in Gaussian Process Dynamical Systems","year":2012,"lang":"en","type":"article","venue":"TUbilio (Technical University of Darmstadt)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Expectation propagation; Computer science; Message passing; Gaussian process; State space; Inference; Graphical model; Approximate inference; Iterated function; Dynamical systems theory; Bayesian inference; State-space representation; Probabilistic logic; Algorithm; Artificial intelligence; Machine learning; Bayesian probability; Theoretical computer science; Gaussian; Mathematics","score_opus":0.009437202492629896,"score_gpt":0.22408174733554917,"score_spread":0.21464454484291928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095897186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24960388,0.000100122576,0.7448695,0.00042975877,0.00011631158,0.00038896638,0.0000039887377,0.00019746792,0.0042900397],"genre_scores_gemma":[0.9928755,0.000010984607,0.0070145563,0.000010522788,0.000018769453,0.0000016650789,0.000005245619,0.000005537996,0.000057190544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9987378,0.00007488929,0.00024022581,0.00030108134,0.00031893633,0.0003270704],"domain_scores_gemma":[0.9992041,0.00005122006,0.00015703675,0.00034973002,0.0000998511,0.00013805868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030637477,0.0001276274,0.00022570672,0.0002111306,0.000077942925,0.000033691347,0.00073713105,0.00013088058,0.000018041386],"category_scores_gemma":[0.00004730165,0.00012786528,0.00005120194,0.0008140919,0.000118204116,0.0011689679,0.00015185757,0.00020484712,0.000021973849],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027097817,0.0030939414,0.104359746,0.0018027356,0.000053375436,0.00012360458,0.014273157,0.0012425693,0.014439764,0.8316008,0.0005693788,0.028169977],"study_design_scores_gemma":[0.006083028,0.0015041954,0.708513,0.0016248554,0.00012617285,0.00033094466,0.013593064,0.23546278,0.009511084,0.018214846,0.0020521819,0.0029838302],"about_ca_topic_score_codex":0.00008734786,"about_ca_topic_score_gemma":0.000037021902,"teacher_disagreement_score":0.8133859,"about_ca_system_score_codex":0.00013554112,"about_ca_system_score_gemma":0.000115103976,"threshold_uncertainty_score":0.5214195},"labels":[],"label_agreement":null},{"id":"W2103431968","doi":"10.1007/3-540-45665-1_2","title":"Scaling Large Learning Problems with Hard Parallel Mixtures","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Parallelizable manifold; Probabilistic logic; Generalization; Quadratic equation; Support vector machine; Algorithm; Theoretical computer science; Machine learning; Artificial intelligence; Mathematics","score_opus":0.01503603747282623,"score_gpt":0.218231011784099,"score_spread":0.20319497431127279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103431968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000037113514,0.0008929455,0.9904652,0.000809534,0.0004879231,0.00037489898,0.0000027706244,0.00031076238,0.006618819],"genre_scores_gemma":[0.23644015,0.00022397173,0.7591749,0.0013593278,0.00047204085,0.000025050726,0.0000068981863,0.00007875132,0.0022189396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949847,0.000038810034,0.0005482124,0.0020119718,0.0012600231,0.0011562867],"domain_scores_gemma":[0.99756783,0.000270709,0.00042590033,0.0011757269,0.0002990826,0.0002607746],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000673744,0.00073903025,0.0006572204,0.00067855016,0.0005616981,0.0013248366,0.00360085,0.0003699191,0.00007018474],"category_scores_gemma":[0.00007514079,0.0005748877,0.00012440658,0.0008219719,0.000522913,0.0008976571,0.0010882057,0.0015803374,0.00009133724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015804831,0.00012399207,0.0008039773,0.00039181032,0.00005124748,0.00049284723,0.0035243933,0.17560779,0.00009538002,0.10322412,0.00008003683,0.7155886],"study_design_scores_gemma":[0.0007399603,0.00053695316,0.00026320934,0.0019838442,0.000019493567,0.00032502558,4.5094467e-7,0.8438029,0.00037101787,0.14111942,0.009106302,0.001731421],"about_ca_topic_score_codex":0.00001259372,"about_ca_topic_score_gemma":0.000032260272,"teacher_disagreement_score":0.7138572,"about_ca_system_score_codex":0.00016156225,"about_ca_system_score_gemma":0.00040180757,"threshold_uncertainty_score":0.9997119},"labels":[],"label_agreement":null},{"id":"W2103504567","doi":"","title":"Self Supervised Boosting","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Artificial intelligence; Computer science; Machine learning; Binary classification; Pattern recognition (psychology); Gradient boosting; Training set; Feature (linguistics); Classifier (UML); Data mining; Random forest; Support vector machine","score_opus":0.020081696000680625,"score_gpt":0.20726091293695204,"score_spread":0.1871792169362714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103504567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012762359,0.00011428838,0.74647135,0.002195251,0.000097504766,0.000045896355,1.470622e-7,0.00053425675,0.24926507],"genre_scores_gemma":[0.70141757,0.00001835691,0.29636177,0.00066830806,0.000030939787,0.0000029261514,9.7107545e-8,0.000003201265,0.001496813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935424,0.000009862771,0.00010500479,0.00020711643,0.0001235699,0.00020020263],"domain_scores_gemma":[0.99956626,0.000025832598,0.000020836113,0.0002838534,0.00003400064,0.0000692263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052358148,0.00007084584,0.00006479602,0.000035055986,0.000085033455,0.00018164064,0.00057648483,0.000025847083,0.0004463407],"category_scores_gemma":[0.000014278538,0.000056690256,0.000024940775,0.00026170939,0.00000855082,0.00042738285,0.000116665266,0.000056496607,0.0006217933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.851214e-7,0.00013899569,0.00194879,0.000060476304,0.000012318291,0.000042482963,0.0018717619,0.0000038059134,0.00026632872,0.7052903,0.009282951,0.2810814],"study_design_scores_gemma":[0.00023797891,0.000058197118,0.0007721334,0.000013990247,0.0000025555619,0.000045759676,0.000021526912,0.9716865,0.0014727698,0.007771993,0.017655596,0.00026102376],"about_ca_topic_score_codex":0.0000039597885,"about_ca_topic_score_gemma":0.000001059915,"teacher_disagreement_score":0.97168267,"about_ca_system_score_codex":0.00000848675,"about_ca_system_score_gemma":0.000009428581,"threshold_uncertainty_score":0.79921025},"labels":[],"label_agreement":null},{"id":"W2103510282","doi":"","title":"Learning Shared Latent Structure for Image Synthesis and Robotic Imitation","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":183,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Humanoid robot; Computer science; Artificial intelligence; Curse of dimensionality; Isomap; Latent variable; Degrees of freedom (physics and chemistry); Computer vision; Process (computing); Latent variable model; Imitation; Gaussian process; Robot; Nonlinear dimensionality reduction; Gaussian; Dimensionality reduction","score_opus":0.011782454158075934,"score_gpt":0.23087505972869193,"score_spread":0.219092605570616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103510282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00921235,0.000045037446,0.98667455,0.003277114,0.000034960856,0.000109433124,0.000001492445,0.000098339384,0.0005467278],"genre_scores_gemma":[0.67158455,0.0000065376785,0.32804385,0.00009537697,0.000024931127,0.000008448927,0.0000011624138,0.000003570181,0.00023157983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942046,0.000012039073,0.0001070008,0.00022692693,0.00007959295,0.00015400187],"domain_scores_gemma":[0.99963874,0.00009360741,0.000045851033,0.00011160731,0.0000604012,0.000049789105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000595966,0.00007991965,0.000087034605,0.000042667478,0.00011126326,0.00027789484,0.00019582642,0.00003441558,0.00005169283],"category_scores_gemma":[0.00008323598,0.000063352265,0.000021896221,0.00009302216,0.00001488008,0.00060531974,0.00005611308,0.00005545043,0.000009156352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012169391,0.000041179097,0.0014906962,0.00021474154,0.00003232054,0.000004480398,0.0018829592,0.0035035484,0.011474429,0.12928459,0.0012915567,0.8507673],"study_design_scores_gemma":[0.0003074493,0.00011599709,0.016233781,0.000046380144,0.000017989198,0.000029848832,0.000069775044,0.952282,0.014840139,0.0147857405,0.0009577592,0.00031312398],"about_ca_topic_score_codex":0.000003227851,"about_ca_topic_score_gemma":0.000009213085,"teacher_disagreement_score":0.94877845,"about_ca_system_score_codex":0.000012943004,"about_ca_system_score_gemma":0.000020557296,"threshold_uncertainty_score":0.26797464},"labels":[],"label_agreement":null},{"id":"W2107564391","doi":"10.3150/10-bej248","title":"Second order ancillary: A differential view from continuity","year":2010,"lang":"en","type":"article","venue":"Bernoulli","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Scalar (mathematics); Consistency (knowledge bases); Quantile function; Vector field; Nonlinear system; Taylor series; Monte Carlo method","score_opus":0.007005552295495729,"score_gpt":0.22092803223220925,"score_spread":0.2139224799367135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107564391","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.525995,0.00021874036,0.45753965,0.0010661979,0.0017293867,0.00019260285,0.000048611404,0.0002765415,0.0129332375],"genre_scores_gemma":[0.9714826,0.000018927722,0.026947321,0.00047064177,0.00025959712,0.000013552719,0.000010142336,0.0000115941875,0.0007856726],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986688,0.000026190639,0.00022465955,0.0004840286,0.00025562453,0.00034067803],"domain_scores_gemma":[0.9988472,0.000054633038,0.00010220805,0.00070764,0.00012598473,0.00016232696],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008966471,0.0001875669,0.00023428275,0.000040073402,0.00012317403,0.00035128454,0.0010401912,0.00012863724,0.0021461647],"category_scores_gemma":[0.00004459882,0.00015719612,0.00006338607,0.00026773557,0.00006566609,0.00037916916,0.00029808545,0.00034006362,0.00034633584],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025940115,0.0005494938,0.024631718,0.00014845504,0.00013227262,0.00013930071,0.0022070557,8.5598947e-7,0.061744384,0.42864022,0.01305314,0.46872717],"study_design_scores_gemma":[0.0018810051,0.00016645067,0.2746163,0.00010195736,0.000042998945,0.00007664822,0.000025541096,0.0072900397,0.017447585,0.09343797,0.6034734,0.0014400781],"about_ca_topic_score_codex":0.00008940244,"about_ca_topic_score_gemma":0.00070077647,"teacher_disagreement_score":0.59042025,"about_ca_system_score_codex":0.000009719867,"about_ca_system_score_gemma":0.00012995693,"threshold_uncertainty_score":0.998766},"labels":[],"label_agreement":null},{"id":"W2111467524","doi":"","title":"An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward","year":2009,"lang":"en","type":"article","venue":"TUbilio (Technical University of Darmstadt)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Parameterized complexity; Markov decision process; Mathematical optimization; Computer science; Maximization; Linear-quadratic-Gaussian control; Computation; Gaussian process; Heuristics; Optimization problem; Algorithm; Quadratic equation; Markov process; Gaussian; Mathematics; Optimal control","score_opus":0.005562707577122003,"score_gpt":0.2138742374957114,"score_spread":0.20831152991858942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111467524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015298272,0.000037216698,0.98301387,0.000372931,0.000023268944,0.00038026905,0.000013188579,0.0002472802,0.0006136849],"genre_scores_gemma":[0.47240275,0.000036325022,0.5274454,0.000056190835,0.0000116009105,7.9159463e-7,0.00001450414,0.0000047992785,0.000027651336],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99874014,0.000028977242,0.00019958054,0.0004963525,0.00030116964,0.00023381175],"domain_scores_gemma":[0.99867034,0.00014165939,0.00018399858,0.0004570895,0.00042134983,0.00012555538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014397432,0.00015791117,0.00025668825,0.00016114672,0.00017315197,0.00007036768,0.00089260563,0.0001138205,0.0000137050165],"category_scores_gemma":[0.00006390767,0.00014639951,0.000055382945,0.00072866236,0.0001009262,0.0011212898,0.000057993715,0.00011323539,0.0000022539348],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050794537,0.00088794174,0.0005177398,0.00017524954,0.000022129849,0.00006592671,0.0009082229,0.00016265723,0.0016984709,0.011231954,0.00074173824,0.98308],"study_design_scores_gemma":[0.027458988,0.049598556,0.124183714,0.0036326589,0.0007007768,0.0007582424,0.00725707,0.3199466,0.07037447,0.37921882,0.009857174,0.0070129605],"about_ca_topic_score_codex":0.000012399768,"about_ca_topic_score_gemma":0.000028212453,"teacher_disagreement_score":0.97606707,"about_ca_system_score_codex":0.000048310576,"about_ca_system_score_gemma":0.00028961027,"threshold_uncertainty_score":0.5969999},"labels":[],"label_agreement":null},{"id":"W2111683289","doi":"","title":"An Alternative Infinite Mixture Of Gaussian Process Experts","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian process; Computer science; Classification of discontinuities; Multivariate normal distribution; Covariance; Bayesian inference; Inference; Generative model; Mixture model; Component (thermodynamics); Algorithm; Conditional probability distribution; Artificial intelligence; Gaussian; Mathematics; Bayesian probability; Machine learning; Multivariate statistics; Generative grammar; Econometrics; Statistics","score_opus":0.012119979856208104,"score_gpt":0.2795389576570921,"score_spread":0.267418977800884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111683289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037404962,0.000091823444,0.9214084,0.0023304725,0.000110862464,0.00012930253,0.000002908623,0.00017908963,0.03834218],"genre_scores_gemma":[0.9296174,0.000011026388,0.06929995,0.00074137526,0.00010081435,0.0000108362065,0.0000014182497,0.0000068682375,0.00021032488],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884605,0.000025506539,0.00024993092,0.00035859522,0.00027605754,0.00024386836],"domain_scores_gemma":[0.9990639,0.000021579404,0.0001280211,0.0004978555,0.00015227954,0.0001363759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009016155,0.00014865275,0.00016630634,0.00010641155,0.000058608246,0.000114089285,0.001171215,0.00006189221,0.00013711299],"category_scores_gemma":[0.000014546805,0.00011120109,0.000038632606,0.00037399738,0.000052616055,0.0013078906,0.00007639515,0.00008333873,0.000023882953],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026989528,0.0008379211,0.0050520087,0.0001737426,0.000051449188,0.00003215071,0.02186664,0.0010787962,0.0047188825,0.6413441,0.001611457,0.32320592],"study_design_scores_gemma":[0.0019848843,0.0011952114,0.010528111,0.00033267922,0.00002171042,0.00015252295,0.0014096425,0.4050178,0.46441972,0.097468354,0.015592674,0.0018766855],"about_ca_topic_score_codex":0.000020122692,"about_ca_topic_score_gemma":0.000023804145,"teacher_disagreement_score":0.8922124,"about_ca_system_score_codex":0.000013571299,"about_ca_system_score_gemma":0.00009776676,"threshold_uncertainty_score":0.45346487},"labels":[],"label_agreement":null},{"id":"W2112518971","doi":"","title":"Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression","year":2012,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaussian process; Inference; Algorithm; Mathematical optimization; Mathematics; Computer science; Ordinal regression; Convergence (economics); Approximate inference; Gaussian; Conjugate prior; Bayesian probability; Artificial intelligence; Bayes' theorem; Machine learning","score_opus":0.018701152931170816,"score_gpt":0.28794862034905533,"score_spread":0.2692474674178845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112518971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004524002,0.00023648386,0.98751587,0.00064845185,0.0012695647,0.00086839497,0.000013918126,0.00045353392,0.004469765],"genre_scores_gemma":[0.99210167,0.0000071521686,0.006532552,0.00044902577,0.00030797388,0.00035056577,0.000038891998,0.000020033212,0.00019213266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719965,0.000044983735,0.0009529512,0.0003293062,0.00058183214,0.0008912851],"domain_scores_gemma":[0.99763584,0.00007125697,0.00085895346,0.000497266,0.0005789672,0.00035769978],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0005477666,0.0004027514,0.00039501593,0.000303149,0.0006338003,0.0017322993,0.0011099677,0.00020096434,0.0000070978494],"category_scores_gemma":[0.00013540054,0.00030856737,0.000090196314,0.0008024029,0.00006651104,0.016125694,0.00013192258,0.00025850107,0.00007989523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016004534,0.00037715948,0.013859726,0.0150067005,0.000066435714,0.000005539233,0.042793818,0.003745467,0.0010506002,0.08928427,0.0049044,0.82874584],"study_design_scores_gemma":[0.0009389976,0.00018817632,0.001877863,0.0010340032,0.000020838215,0.00013755597,0.0011106561,0.9833688,0.0026537003,0.0011369059,0.006658485,0.00087402854],"about_ca_topic_score_codex":0.000015423502,"about_ca_topic_score_gemma":0.0000010986419,"teacher_disagreement_score":0.9875777,"about_ca_system_score_codex":0.00007604884,"about_ca_system_score_gemma":0.00026266274,"threshold_uncertainty_score":0.99993664},"labels":[],"label_agreement":null},{"id":"W2116418767","doi":"","title":"Generalized Optimal Reverse Prediction","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Minification; Kernel (algebra); Bregman divergence; Computer science; Mathematical optimization; Extension (predicate logic); Equivalence (formal languages); Artificial intelligence; Divergence (linguistics); Mathematics; Machine learning; Algorithm; Applied mathematics","score_opus":0.01630843226169435,"score_gpt":0.23609902508368236,"score_spread":0.219790592821988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116418767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010227923,0.000067871515,0.96568376,0.00072321197,0.00041041683,0.000048400678,9.17137e-7,0.0002142622,0.02262322],"genre_scores_gemma":[0.74157417,0.000012905233,0.25640604,0.00046599796,0.00013683845,0.0000067294272,0.0000011240488,0.0000029921905,0.0013932056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938315,0.000015505828,0.00010429705,0.00013463273,0.00012506453,0.00023737735],"domain_scores_gemma":[0.9995742,0.000008117142,0.000029333141,0.00024708032,0.000029377887,0.000111894624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012110277,0.00006678555,0.00006135787,0.00003551757,0.00006230638,0.00007173841,0.00030339896,0.000034999695,0.00018852726],"category_scores_gemma":[0.000009648117,0.000052241383,0.00002783829,0.00017750262,0.000013297939,0.001010318,0.00009700934,0.000049316855,0.0002219384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033410006,0.00008260742,0.004398042,0.000013312998,0.000009820952,0.0000022007114,0.0006109791,0.000031212432,0.0009544909,0.94966763,0.019083971,0.025142375],"study_design_scores_gemma":[0.0030338867,0.0004874978,0.07929726,0.00008190707,0.000054604974,0.0005033176,0.00028676662,0.3059245,0.049405787,0.029475808,0.5296309,0.0018177741],"about_ca_topic_score_codex":0.000012135709,"about_ca_topic_score_gemma":4.191502e-7,"teacher_disagreement_score":0.9201918,"about_ca_system_score_codex":0.000013708842,"about_ca_system_score_gemma":0.000023268743,"threshold_uncertainty_score":0.2852643},"labels":[],"label_agreement":null},{"id":"W2116563254","doi":"10.48550/arxiv.1310.1415","title":"Narrowing the Gap: Random Forests In Theory and In Practice","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":176,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Random forest; Computer science; Algorithm; Econometrics; Artificial intelligence; Mathematics","score_opus":0.025884854412430436,"score_gpt":0.18748775322784927,"score_spread":0.16160289881541884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116563254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65674037,0.000112311274,0.33533853,0.0010559644,0.00005847142,0.00022427457,1.8637373e-7,0.00003559366,0.0064343144],"genre_scores_gemma":[0.9988853,0.00005921194,0.00048535803,0.0003113855,0.000008214808,0.0000012564628,1.2484001e-7,0.0000033860272,0.0002457932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991321,0.00020764304,0.00009898217,0.00030658636,0.000042500982,0.0002121966],"domain_scores_gemma":[0.9990044,0.0005562216,0.00005869229,0.00028870325,0.000040876803,0.000051095863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052766874,0.000089504094,0.00009932114,0.0001394481,0.00007888341,0.00011862046,0.0005615287,0.000043034106,0.00001439334],"category_scores_gemma":[0.0001629771,0.0000698136,0.000021685852,0.00079293567,0.000075442484,0.0014727293,0.00020186232,0.00016844738,0.000034444325],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006784341,0.000048883085,0.036043853,0.000012658739,0.0000084138155,0.00017463989,0.001139079,0.0018845205,0.000031114127,0.95749015,0.000044636552,0.0030541862],"study_design_scores_gemma":[0.002888514,0.00007048895,0.13722122,0.00010459552,0.000016586591,0.000045396988,0.0018247355,0.24480972,0.00008670776,0.6120575,0.0004992978,0.00037520414],"about_ca_topic_score_codex":0.0001553758,"about_ca_topic_score_gemma":0.00019602191,"teacher_disagreement_score":0.34543264,"about_ca_system_score_codex":0.00002914788,"about_ca_system_score_gemma":0.000044929024,"threshold_uncertainty_score":0.2846916},"labels":[],"label_agreement":null},{"id":"W2119548491","doi":"","title":"Global Coordination of Local Linear Models","year":2001,"lang":"en","type":"article","venue":"ScholarlyCommons (University of Pennsylvania)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Divergence (linguistics); Degeneracy (biology); Manifold (fluid mechanics); Computer science; Probabilistic logic; Inference; Path (computing); Function (biology); Kullback–Leibler divergence; Nonlinear dimensionality reduction; Mathematical optimization; Applied mathematics; Mathematics; Algorithm; Artificial intelligence; Dimensionality reduction","score_opus":0.017356467271283742,"score_gpt":0.22086576236628605,"score_spread":0.20350929509500232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119548491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09487837,0.00007968171,0.89358675,0.0009810783,0.00006567255,0.00007895505,0.000015234377,0.00006424753,0.01025003],"genre_scores_gemma":[0.9463862,0.000033282668,0.053296342,0.000025080746,0.000008940502,1.1194616e-7,0.0000035342928,0.0000036684828,0.00024282259],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892426,0.00006016487,0.00016330145,0.00029621716,0.0003362658,0.0002197842],"domain_scores_gemma":[0.9988164,0.00003134129,0.00019841568,0.000507317,0.00032758768,0.00011896673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022202947,0.000120153905,0.00023485924,0.00014176352,0.00016999755,0.00004336789,0.0014348541,0.000106101776,0.000055425004],"category_scores_gemma":[0.000014741412,0.00014669604,0.00010637215,0.0010226413,0.00017802001,0.0025021785,0.00041414885,0.00015462578,0.00002694113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009214548,0.0004555578,0.016457012,0.000096194504,0.00007412519,0.000120874145,0.0010003448,0.0024434174,0.00067739113,0.87008697,0.0010845612,0.107411414],"study_design_scores_gemma":[0.0034327938,0.00087081053,0.11067453,0.0002617337,0.00011094114,0.00019553356,0.0031976674,0.65325713,0.00064892124,0.21849012,0.007833171,0.001026652],"about_ca_topic_score_codex":0.00043014102,"about_ca_topic_score_gemma":0.00031689627,"teacher_disagreement_score":0.85150784,"about_ca_system_score_codex":0.000064478605,"about_ca_system_score_gemma":0.00013758481,"threshold_uncertainty_score":0.5982091},"labels":[],"label_agreement":null},{"id":"W2121433185","doi":"10.48550/arxiv.1003.4944","title":"Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Factorization; Probabilistic logic; Gaussian; Gaussian process; Computer science; Matrix (chemical analysis); Mathematics; Algorithm; Artificial intelligence; Materials science; Chemistry; Computational chemistry","score_opus":0.026969327062609155,"score_gpt":0.18203891591399568,"score_spread":0.15506958885138653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121433185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1154593,0.000009502709,0.8817357,0.0001299782,0.00018239944,0.000470587,0.000011279407,0.00020922381,0.0017920583],"genre_scores_gemma":[0.9893747,0.000020312353,0.010381959,0.000034767192,0.000035746038,0.0000051728284,0.00005215368,0.000012237779,0.00008294422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998452,0.00005295664,0.0003378816,0.0006845759,0.00014988487,0.00032271448],"domain_scores_gemma":[0.998079,0.00006817288,0.00061376404,0.00071446324,0.00039544012,0.00012913231],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001734214,0.00033779888,0.00029580094,0.00042736687,0.00013483186,0.00047899195,0.001322362,0.00033914577,0.000008173349],"category_scores_gemma":[0.00014925266,0.00032015928,0.00004131186,0.001457224,0.00009685226,0.0024489372,0.0006930441,0.00081066746,0.000033855722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006859467,0.00017886037,0.025104223,0.0029057593,0.000037092588,0.00020461497,0.0018065888,0.34672886,0.000049648923,0.6205327,0.000019821411,0.0023632245],"study_design_scores_gemma":[0.0008833165,0.00018044452,0.008985893,0.00089526706,0.00004739185,0.000023148963,0.00021347054,0.70043224,0.0003612372,0.2866685,0.00014097971,0.0011680779],"about_ca_topic_score_codex":0.00018121571,"about_ca_topic_score_gemma":0.0006637025,"teacher_disagreement_score":0.87391543,"about_ca_system_score_codex":0.00016328311,"about_ca_system_score_gemma":0.0012148797,"threshold_uncertainty_score":0.9999251},"labels":[],"label_agreement":null},{"id":"W2122823116","doi":"10.1109/crv.2012.35","title":"Gaussian Process Gauss-Newton: Non-Parametric State Estimation","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian process; Parametric statistics; Covariance; Gaussian function; Mathematical optimization; Kernel (algebra); Gauss; Newton's method; Applied mathematics; Gaussian; Mathematics; Basis function; State (computer science); Computer science; Algorithm; Nonlinear system; Mathematical analysis; Statistics","score_opus":0.011206250170241076,"score_gpt":0.26914390498507357,"score_spread":0.2579376548148325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122823116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014372606,0.000095458694,0.96729165,0.001344933,0.00039352442,0.00019549251,0.0000013331306,0.000279316,0.016025657],"genre_scores_gemma":[0.8945005,0.000011420316,0.10385245,0.00037275266,0.00006372283,0.000030628522,0.0000028025584,0.000012198839,0.0011535514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981851,0.00002540131,0.00031801933,0.00037794036,0.00041781436,0.0006757433],"domain_scores_gemma":[0.9988215,0.000054134416,0.00014964808,0.0005447056,0.00011066891,0.00031935258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002958542,0.00022259957,0.0001989049,0.00026865705,0.00015923778,0.00035554013,0.00090607634,0.00010521304,0.000109453875],"category_scores_gemma":[0.00006220453,0.00017573813,0.000052566502,0.0016619853,0.000041531453,0.0025678438,0.00014338916,0.00023422873,0.0006926105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003462739,0.0014366246,0.058718007,0.0009375639,0.00010664033,0.000042616022,0.007748399,0.0032418035,0.00045454354,0.39974692,0.017749539,0.5097827],"study_design_scores_gemma":[0.001015861,0.00033726217,0.0998577,0.00013642575,0.000029591416,0.0001985854,0.0001498681,0.78864384,0.02907141,0.0747502,0.004226989,0.0015822893],"about_ca_topic_score_codex":0.00002576113,"about_ca_topic_score_gemma":0.0000019503145,"teacher_disagreement_score":0.88012785,"about_ca_system_score_codex":0.00004239974,"about_ca_system_score_gemma":0.00012983977,"threshold_uncertainty_score":0.89023376},"labels":[],"label_agreement":null},{"id":"W2124609748","doi":"10.1109/tpami.2007.1167","title":"Gaussian Process Dynamical Models for Human Motion","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1038,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Research and Innovation Foundation; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Gaussian process; Artificial intelligence; Latent variable; Computer science; Prior probability; Dynamical systems theory; Gaussian; Latent variable model; Representation (politics); Nonlinear system; Motion capture; Motion (physics); Machine learning; Algorithm; Physics","score_opus":0.0235016921053867,"score_gpt":0.29746681412930737,"score_spread":0.27396512202392065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124609748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023624364,0.000027542414,0.99657524,0.00039532478,0.00011175735,0.0001957248,0.00002650296,0.00010754572,0.00019789966],"genre_scores_gemma":[0.99479765,0.000031285603,0.004700318,0.00028188352,0.00002203708,0.000033094377,0.0000078856865,0.0000125287515,0.00011329095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824435,0.000024393174,0.00045351192,0.0006431293,0.00027045666,0.0003641525],"domain_scores_gemma":[0.9990681,0.00008655469,0.0001254612,0.0004102611,0.00012600263,0.00018362128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036397824,0.00024016607,0.00029752002,0.00054217887,0.0003555368,0.00019387533,0.0005309692,0.0001316015,0.000038223636],"category_scores_gemma":[0.0000025911013,0.00020606937,0.00022920164,0.0010380846,0.00006712761,0.00044109672,0.0000044239528,0.00029718052,0.0000064594487],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016701875,0.0002796326,0.00023142449,0.0000737794,0.00023426402,0.0000069371076,0.00057203317,0.02635268,0.00022715653,0.008097361,0.0000018188014,0.9639062],"study_design_scores_gemma":[0.00012947067,0.00019898538,0.0006632678,0.00002924555,0.00025833884,0.000012532949,0.00006876145,0.9109313,0.05886214,0.02848013,0.000009991519,0.00035583242],"about_ca_topic_score_codex":0.0003045981,"about_ca_topic_score_gemma":0.0018080432,"teacher_disagreement_score":0.9924352,"about_ca_system_score_codex":0.000035391222,"about_ca_system_score_gemma":0.000019411,"threshold_uncertainty_score":0.8403265},"labels":[],"label_agreement":null},{"id":"W2124680066","doi":"10.1111/insr.12176","title":"Statistical Inference, Learning and Models in Big Data","year":2016,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University; McMaster University; University of Toronto; University of Waterloo; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Centre de Recherches Mathématiques; Division of Mathematical Sciences; Iowa State University; National Science Foundation","keywords":"Big data; Theme (computing); Thematic map; Statistical analysis; Statistical learning; Statistical model","score_opus":0.08404948873564336,"score_gpt":0.3650214080775483,"score_spread":0.2809719193419049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124680066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015660416,0.0029114285,0.9885885,0.0041696834,0.00014550475,0.0000940207,0.00012816608,0.00003157443,0.003915467],"genre_scores_gemma":[0.75238895,0.06899589,0.17682655,0.0011937997,0.000097900236,0.000034389563,0.00007888268,0.000013094594,0.0003705206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984952,0.0000812789,0.00037442832,0.00048735648,0.0003512761,0.00021041864],"domain_scores_gemma":[0.9984444,0.0009793876,0.00007558084,0.00028900802,0.000088033776,0.00012360286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038862316,0.000120234305,0.00021593945,0.00006105293,0.000031428273,0.000114966206,0.0009786792,0.000031058167,0.00024082352],"category_scores_gemma":[0.001998051,0.000078187404,0.000010227511,0.0001431317,0.00008984795,0.0005477849,0.000693711,0.00013995088,0.000096178235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013797763,0.000013874913,0.0006621003,0.00007805444,0.0000037260177,0.00002205384,0.0000038498833,3.5499576e-7,0.0000026595505,0.5183638,0.0005231479,0.48032504],"study_design_scores_gemma":[0.0006289621,0.000105991436,0.01346353,0.004725749,0.000017790908,0.000060878057,0.0000029139183,0.085878216,0.0000042745446,0.74332154,0.1513338,0.000456344],"about_ca_topic_score_codex":0.000026672978,"about_ca_topic_score_gemma":0.000018846094,"teacher_disagreement_score":0.8117619,"about_ca_system_score_codex":0.000033159202,"about_ca_system_score_gemma":0.00010360755,"threshold_uncertainty_score":0.31883898},"labels":[],"label_agreement":null},{"id":"W2124959221","doi":"10.1007/978-3-642-10677-4_49","title":"Learning Gaussian Process Models from Uncertain Data","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Gaussian process; Process (computing); Artificial intelligence; Machine learning; Data mining; Gaussian; Programming language","score_opus":0.0355781656894634,"score_gpt":0.2738262277310314,"score_spread":0.23824806204156798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124959221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025832254,0.0007108614,0.9849409,0.0015818272,0.00066062325,0.00032482835,0.00002425774,0.0003332583,0.011397597],"genre_scores_gemma":[0.39016205,0.00019335498,0.60552794,0.0021993904,0.000876832,0.000009796767,0.00011542766,0.00007197621,0.0008432243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933527,0.0000485766,0.0007343078,0.003333843,0.0015051258,0.0010254378],"domain_scores_gemma":[0.9951341,0.00032729772,0.00050815346,0.003418733,0.0002881493,0.00032356477],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00082026754,0.0008210186,0.00077664136,0.0006902864,0.0004511406,0.0015247959,0.01262484,0.00047872527,0.00004187184],"category_scores_gemma":[0.00012109233,0.00073070347,0.00009732123,0.00094739953,0.0005447703,0.0027448565,0.002762982,0.0016983296,0.000062604726],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005456526,0.000030167217,0.000024618063,0.00004337663,0.000011634353,0.00015931422,0.0009635458,0.08586347,0.000014843685,0.018735375,0.000021350399,0.89412683],"study_design_scores_gemma":[0.00013340997,0.00010694945,0.000027366686,0.00043721587,0.0000075933785,0.000027582471,3.5646775e-7,0.55704343,0.00009495638,0.44104573,0.0005135297,0.0005618869],"about_ca_topic_score_codex":0.00008476761,"about_ca_topic_score_gemma":0.00010811036,"teacher_disagreement_score":0.89356494,"about_ca_system_score_codex":0.0002110257,"about_ca_system_score_gemma":0.0014861677,"threshold_uncertainty_score":0.9995144},"labels":[],"label_agreement":null},{"id":"W2125569215","doi":"","title":"The Curse of Highly Variable Functions for Local Kernel Machines","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Curse of dimensionality; Kernel (algebra); Artificial intelligence; Machine learning; Computer science; Semi-supervised learning; Kernel method; Smoothness; Gaussian function; Function (biology); Gaussian; Pattern recognition (psychology); Mathematics; Algorithm; Support vector machine","score_opus":0.00809780263854042,"score_gpt":0.23178687217125654,"score_spread":0.2236890695327161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125569215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000096355725,0.00011032994,0.97660345,0.004568339,0.00016637996,0.000090855014,0.0000032454368,0.00006109365,0.01829997],"genre_scores_gemma":[0.81104195,0.000012480624,0.17970219,0.00031342328,0.00009930368,0.000031030708,0.0000011409136,0.0000049092305,0.008793582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938124,0.000009065832,0.00016651704,0.0001615404,0.00010425072,0.00017738143],"domain_scores_gemma":[0.9993433,0.00013204913,0.00005595276,0.00031738597,0.000108969965,0.000042355085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014621997,0.00007064013,0.000078071345,0.000023613176,0.00018498072,0.00009176537,0.00056755275,0.000028648306,0.000027062277],"category_scores_gemma":[0.000023310902,0.00004091882,0.000040493826,0.00021120024,0.000065023596,0.00027108946,0.00008286936,0.000046085446,0.000030341758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004164717,0.000035426532,0.00006179595,0.000010833424,0.00000743533,8.4220986e-8,0.000033851706,0.00037620828,0.00007215126,0.8306044,0.008144792,0.16064888],"study_design_scores_gemma":[0.00035514802,0.00012986784,0.0006275211,0.000014250081,0.000011119839,0.000010344801,0.00003961424,0.70704526,0.0018061453,0.078604475,0.21119913,0.00015713851],"about_ca_topic_score_codex":0.00003693372,"about_ca_topic_score_gemma":0.00006138541,"teacher_disagreement_score":0.8109456,"about_ca_system_score_codex":0.000010408606,"about_ca_system_score_gemma":0.00008979006,"threshold_uncertainty_score":0.16686212},"labels":[],"label_agreement":null},{"id":"W2127831582","doi":"","title":"Sensors to measure mass-flow-rate through a forage harvester","year":2000,"lang":"en","type":"article","venue":"Canadian agricultural engineering","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Forage; Measure (data warehouse); Environmental science; Flow (mathematics); Agricultural engineering; Mathematics; Computer science; Agronomy; Biology; Engineering; Data mining","score_opus":0.007567573178977669,"score_gpt":0.17076432018655488,"score_spread":0.16319674700757722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127831582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26263306,0.00040147916,0.67628866,0.018264243,0.0013780764,0.0011309622,0.00008681542,0.001209092,0.038607612],"genre_scores_gemma":[0.94569355,0.0000095443065,0.047913533,0.00112505,0.00017422964,0.00003101715,0.0000079562615,0.000017404385,0.005027738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863064,0.000013059811,0.00017919086,0.00038001768,0.00015171281,0.0006453929],"domain_scores_gemma":[0.99898237,0.000015562351,0.000019337014,0.00029135816,0.00006892295,0.0006224396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000069046,0.00023634972,0.00016984036,0.00007865468,0.000117087795,0.00056985905,0.0006204184,0.000083786166,0.00012332083],"category_scores_gemma":[0.000024414885,0.00017730083,0.000065272354,0.000710696,0.000008219586,0.0012392519,0.000023548087,0.0001716262,0.00037755337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001673276,0.000051321647,0.00036832842,0.00038384882,0.0002458595,0.0015310368,0.011284726,0.6513534,0.028820673,0.1361667,0.043433335,0.12634401],"study_design_scores_gemma":[0.0013697847,0.000334951,0.1252163,0.0009485721,0.000055789726,0.00075666414,0.00029950644,0.18081625,0.010477979,0.0014834615,0.67312354,0.0051172124],"about_ca_topic_score_codex":0.002423429,"about_ca_topic_score_gemma":0.0044481778,"teacher_disagreement_score":0.68306047,"about_ca_system_score_codex":0.00016932748,"about_ca_system_score_gemma":0.00009597425,"threshold_uncertainty_score":0.7230118},"labels":[],"label_agreement":null},{"id":"W2129458072","doi":"10.48550/arxiv.1006.0868","title":"Slice sampling covariance hyperparameters of latent Gaussian models","year":2010,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperparameter; Markov chain Monte Carlo; Slice sampling; Gaussian process; Covariance; Computer science; Sampling (signal processing); Latent variable; Gaussian; Bayesian probability; Algorithm; Machine learning; Artificial intelligence; Statistics; Mathematics","score_opus":0.08049243678818399,"score_gpt":0.1887163923484924,"score_spread":0.10822395556030842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129458072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22687642,0.000008457528,0.77022,0.00011040391,0.0002075017,0.00007247576,0.0000031291836,0.0000817732,0.002419885],"genre_scores_gemma":[0.9569035,0.000021421109,0.042735655,0.00009533821,0.000020074034,3.4869285e-7,9.647915e-7,0.000008088479,0.0002146069],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886376,0.000026271653,0.00017491251,0.00054066454,0.000085454594,0.00030894656],"domain_scores_gemma":[0.99874973,0.000073953175,0.00016105977,0.00073747267,0.00012213166,0.0001556248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015271826,0.00015923867,0.00019843067,0.0001293703,0.00011655823,0.000069271606,0.0012681768,0.000103049206,0.000026680602],"category_scores_gemma":[0.00002280019,0.00016401679,0.00009054868,0.0007467223,0.000118252974,0.0009143051,0.00024057984,0.00026144125,0.000034037643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012476436,0.000062216546,0.0012117405,0.000027619084,0.000018861057,0.000034086966,0.00016661981,0.030249529,0.0029571326,0.9638714,0.000018842149,0.0013694768],"study_design_scores_gemma":[0.0004643958,0.00007489381,0.0026321216,0.000040280796,0.000022791686,0.000020361558,0.00003224481,0.84761125,0.0021806778,0.14631885,0.0002556769,0.0003464451],"about_ca_topic_score_codex":0.000090428424,"about_ca_topic_score_gemma":0.000032396263,"teacher_disagreement_score":0.81755257,"about_ca_system_score_codex":0.000022279273,"about_ca_system_score_gemma":0.00011578023,"threshold_uncertainty_score":0.66884106},"labels":[],"label_agreement":null},{"id":"W2135217348","doi":"","title":"Gated Softmax Classification","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Softmax function; Latent variable; Artificial intelligence; Probabilistic logic; Support vector machine; Pattern recognition (psychology); Kernel (algebra); Computer science; Mathematics; Bilinear interpolation; Binary number; Binary classification; Artificial neural network; Algorithm; Discrete mathematics; Statistics","score_opus":0.013814005611241607,"score_gpt":0.2413333582992707,"score_spread":0.2275193526880291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135217348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008862379,0.000003058003,0.9193008,0.0027141562,0.0002800893,0.00003979071,1.9641782e-7,0.0002655039,0.068534054],"genre_scores_gemma":[0.8973004,0.000001558501,0.10136611,0.0002851112,0.000030328636,0.00000287301,7.5028555e-7,0.0000023557664,0.0010104909],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947554,0.0000055168975,0.00009339224,0.00019444732,0.00010168158,0.00012939681],"domain_scores_gemma":[0.9994591,0.000016285883,0.00003344456,0.0003658013,0.00006381661,0.00006154063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007677001,0.000055546043,0.0000458095,0.000037019596,0.00006388116,0.00016856534,0.00056351733,0.000045304445,0.00017422865],"category_scores_gemma":[0.000024923806,0.00004298798,0.000017088596,0.00023680544,0.000023612512,0.0003551769,0.00006016405,0.00011946621,0.0003887013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.2843576e-7,0.00001803913,0.00069492735,0.0000030052224,0.0000011738317,0.0000011091065,0.000042160467,1.5322529e-7,0.018087294,0.913366,0.001323964,0.06646183],"study_design_scores_gemma":[0.00065140735,0.00013375143,0.1983352,0.000017292205,0.000007401589,0.00012092163,0.00005615751,0.31194028,0.08212193,0.24777062,0.15793979,0.00090523495],"about_ca_topic_score_codex":0.000007698609,"about_ca_topic_score_gemma":0.000016000828,"teacher_disagreement_score":0.88843805,"about_ca_system_score_codex":0.0000033711462,"about_ca_system_score_gemma":0.000053595948,"threshold_uncertainty_score":0.49960986},"labels":[],"label_agreement":null},{"id":"W2135467130","doi":"","title":"Honorary Lecture on S. James Press and Bayesian Analysis ł","year":2009,"lang":"en","type":"article","venue":"Review of Economic Analysis","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Bayesian statistics; Statistical analysis; Sociology; Statistics; Bayesian inference; Econometrics; Mathematics","score_opus":0.008223871137741768,"score_gpt":0.25264266243935024,"score_spread":0.24441879130160848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135467130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022133042,0.14471868,0.8360444,0.006611982,0.000043423664,0.00024996637,0.00002228733,0.00007261837,0.010023399],"genre_scores_gemma":[0.9201473,0.06986133,0.007793912,0.0020634711,0.00002849548,0.000005589955,0.000013867488,0.0000041441695,0.00008186967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862164,0.00006382923,0.0005103666,0.00051640265,0.00010249666,0.00018523302],"domain_scores_gemma":[0.99864554,0.000058473852,0.00036756074,0.0007798595,0.000038896695,0.00010966637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002747223,0.00018794721,0.0009386052,0.00040849808,0.000053882533,0.00009560239,0.0006390634,0.000052189956,0.00013174424],"category_scores_gemma":[0.00002182656,0.0001518278,0.0005237325,0.001288663,0.000035260644,0.0002809843,0.000062865074,0.00008766402,0.000011051424],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015602374,0.00024373952,0.0141348755,0.0027948355,0.012627839,0.000018685667,0.000327824,0.007892979,0.0000125532415,0.15341282,0.0040374734,0.8044808],"study_design_scores_gemma":[0.0008416711,0.0009357876,0.12040628,0.0035174172,0.037270322,0.000022805925,0.00003147195,0.7697155,0.0009201716,0.035975177,0.027778009,0.002585406],"about_ca_topic_score_codex":0.00005560838,"about_ca_topic_score_gemma":0.000018884477,"teacher_disagreement_score":0.917934,"about_ca_system_score_codex":0.000030502657,"about_ca_system_score_gemma":0.000050468017,"threshold_uncertainty_score":0.6191358},"labels":[],"label_agreement":null},{"id":"W2135892144","doi":"10.18637/jss.v033.i06","title":"Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with<b>tgp</b>Version 2, an<i>R</i>Package for Treed Gaussian Process Models","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"Engineering and Physical Sciences Research Council","keywords":"Categorical variable; Computer science; Gaussian process; Covariate; Markov chain Monte Carlo; Sensitivity (control systems); Bayesian probability; Algorithm; Gaussian; Artificial intelligence; Machine learning; Engineering","score_opus":0.00885045248584295,"score_gpt":0.251482053128426,"score_spread":0.24263160064258302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135892144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029194798,0.000022758428,0.9701558,0.0003313259,0.00007329058,0.00012199264,0.000044523873,0.00004196188,0.000013571229],"genre_scores_gemma":[0.57729447,0.000008567083,0.4225658,0.000061267725,0.000044654495,0.0000026996318,0.000010714714,0.000008285996,0.0000035637977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984395,0.00004504651,0.00044092405,0.0003615305,0.00042146535,0.00029156302],"domain_scores_gemma":[0.99819183,0.0002699805,0.00038980469,0.00024803935,0.00053737406,0.0003629555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042941887,0.00019484713,0.00041867702,0.00017840136,0.00019784232,0.00029482768,0.00030736948,0.00010914427,0.0000143776015],"category_scores_gemma":[0.00024558962,0.00014194807,0.00006179349,0.00056407624,0.00011104916,0.0014724709,0.00005523206,0.00035512535,4.1263985e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021718594,0.0029013858,0.22505221,0.0024043352,0.0018709613,0.0032607182,0.0070211417,0.36143288,0.00638457,0.276996,0.0010980595,0.1094059],"study_design_scores_gemma":[0.0010558094,0.00074299984,0.01727372,0.000045775876,0.00030965896,0.00047005588,0.000073404546,0.9522681,0.0005898789,0.026722627,0.000028308024,0.00041966577],"about_ca_topic_score_codex":0.000010225647,"about_ca_topic_score_gemma":0.000060951512,"teacher_disagreement_score":0.5908352,"about_ca_system_score_codex":0.000031149422,"about_ca_system_score_gemma":0.0002005246,"threshold_uncertainty_score":0.5788474},"labels":[],"label_agreement":null},{"id":"W2136539454","doi":"10.1109/ijcnn.2009.5178637","title":"Fast parzen window density estimator","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Kernel density estimation; Estimator; Density estimation; Computer science; Kernel (algebra); Smoothing; Variable kernel density estimation; Algorithm; Kernel smoother; Covariance; Gaussian process; Probability density function; Matching (statistics); Nonparametric statistics; Parametric statistics; Kernel method; Mathematics; Artificial intelligence; Statistics; Gaussian; Support vector machine; Radial basis function kernel","score_opus":0.0071554368400081735,"score_gpt":0.22370697061540612,"score_spread":0.21655153377539796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136539454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064783664,0.000023585888,0.95608586,0.0040265587,0.000086645145,0.00005400587,2.767781e-7,0.00026454168,0.032980155],"genre_scores_gemma":[0.85026276,0.0000021042292,0.14779189,0.0012914747,0.00002999188,0.0000010614019,3.6592533e-7,0.0000019327165,0.0006183961],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99919885,0.00001047956,0.000121004065,0.00027331733,0.00016181197,0.00023451785],"domain_scores_gemma":[0.99939317,0.000014862157,0.00003724045,0.0003886965,0.000052761825,0.0001132819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000701181,0.0001003997,0.000108241074,0.000037624763,0.00010653194,0.0002131028,0.00065245264,0.000038936952,0.000048803096],"category_scores_gemma":[0.000017458042,0.00007880022,0.00003448808,0.00023703082,0.000018243347,0.0004449552,0.000074286916,0.00007555205,0.00026844378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026680514,0.00007646777,0.002414189,0.00000742046,0.000004575102,0.00004533395,0.00014759139,0.000015391479,0.00076280034,0.76869375,0.004676358,0.22315346],"study_design_scores_gemma":[0.0008628414,0.0005303248,0.465879,0.00006789461,0.000013026805,0.00030299663,0.000045919027,0.08903005,0.03425142,0.40306863,0.0048418227,0.0011060861],"about_ca_topic_score_codex":0.000008348409,"about_ca_topic_score_gemma":0.0000037295324,"teacher_disagreement_score":0.8437844,"about_ca_system_score_codex":0.0000120110335,"about_ca_system_score_gemma":0.000059585644,"threshold_uncertainty_score":0.34503913},"labels":[],"label_agreement":null},{"id":"W2137510948","doi":"","title":"On Tracking The Partition Function","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Partition (number theory); Markov chain; Computer science; Mathematics; Importance sampling; Inference; Algorithm; Artificial intelligence; Pattern recognition (psychology); Statistics; Machine learning; Monte Carlo method","score_opus":0.04385094655007001,"score_gpt":0.21631108828681958,"score_spread":0.17246014173674956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137510948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029903436,0.000007013097,0.89752144,0.0004994221,0.00017448013,0.000035621088,6.445971e-8,0.000096640724,0.09867495],"genre_scores_gemma":[0.9956729,0.0000020638747,0.0030634329,0.0009793987,0.00002163241,0.000006390061,1.493463e-7,0.000001709242,0.00025232093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999607,0.00001367804,0.00006527352,0.00012611852,0.00008992874,0.00009796652],"domain_scores_gemma":[0.9996865,0.000019310364,0.000026215695,0.00022150736,0.00002510518,0.000021384878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008851557,0.000044192362,0.000028904995,0.00001857062,0.00010122272,0.000078434154,0.00027880786,0.00001749064,0.00020230898],"category_scores_gemma":[0.000009055528,0.000024260737,0.000018827395,0.00013315641,0.000014106793,0.00029948953,0.000027870858,0.000053543197,0.00024208249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025202135,0.000018026512,0.000101748454,0.0000015495007,0.0000016068166,8.154556e-7,0.00023598141,0.0000013989652,0.000030698062,0.9513718,0.0006017719,0.04763209],"study_design_scores_gemma":[0.00014157288,0.0003440151,0.06532367,0.000018754801,0.0000056178665,0.000011060595,0.000060662118,0.0088343965,0.008036217,0.9148255,0.0022345418,0.00016399632],"about_ca_topic_score_codex":0.000010408415,"about_ca_topic_score_gemma":0.0000062647373,"teacher_disagreement_score":0.9926826,"about_ca_system_score_codex":0.000004911899,"about_ca_system_score_gemma":0.0000117843,"threshold_uncertainty_score":0.31115612},"labels":[],"label_agreement":null},{"id":"W2141742840","doi":"","title":"The Gaussian Process Density Sampler","year":2008,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Density estimation; Gaussian process; Markov chain Monte Carlo; Probability density function; Prior probability; Gaussian; Computer science; Hyperparameter; Statistical physics; Mathematics; Bayesian probability; Algorithm; Artificial intelligence; Statistics; Physics","score_opus":0.021284168092133002,"score_gpt":0.2440894353219804,"score_spread":0.2228052672298474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141742840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05186611,0.0006437511,0.9309296,0.0037768688,0.0014910434,0.00064997084,0.000004243909,0.00095116324,0.0096872235],"genre_scores_gemma":[0.9982122,0.000023598916,0.00082048104,0.0005711128,0.00009467729,0.000058539794,0.0000054747275,0.0000071079335,0.00020679412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807906,0.000048731654,0.0006195687,0.00021564285,0.0006030728,0.00043393078],"domain_scores_gemma":[0.99841386,0.000056541685,0.00045623851,0.000442871,0.00049956207,0.00013093224],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00030212523,0.00020745363,0.00018492233,0.00009852849,0.0017299686,0.0015003196,0.0011224882,0.0000856109,0.0000022883175],"category_scores_gemma":[0.00009229948,0.00013215771,0.000049760176,0.0007000806,0.00012676611,0.0060974066,0.00010759925,0.00022836984,0.00011619054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009689452,0.00015710568,0.01509216,0.0028550504,0.000072259136,0.000069611924,0.03731124,0.0047083967,0.00017204483,0.18952198,0.023049906,0.72689337],"study_design_scores_gemma":[0.00080730004,0.00015867232,0.016790688,0.00023859098,0.000012326097,0.0024774296,0.0016283399,0.9181859,0.001029493,0.0032982996,0.054396257,0.0009766953],"about_ca_topic_score_codex":0.000042657077,"about_ca_topic_score_gemma":0.000004660129,"teacher_disagreement_score":0.9463461,"about_ca_system_score_codex":0.00004533248,"about_ca_system_score_gemma":0.00028722716,"threshold_uncertainty_score":0.99956965},"labels":[],"label_agreement":null},{"id":"W2148186262","doi":"","title":"Wormholes Improve Contrastive Divergence","year":2003,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Range (aeronautics); Statistical physics; Computer science; Markov chain; Divergence (linguistics); Markov chain mixing time; Distribution (mathematics); Sample (material); Monte Carlo method; Algorithm; Markov property; Artificial intelligence; Applied mathematics; Markov model; Mathematics; Statistics; Bayesian probability; Machine learning; Physics; Engineering; Mathematical analysis","score_opus":0.010685311518134401,"score_gpt":0.22173733759841793,"score_spread":0.21105202608028353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148186262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037963646,0.0005470279,0.9829984,0.00020180104,0.0011775754,0.00028355676,0.0000056849885,0.0003217053,0.010667897],"genre_scores_gemma":[0.9965524,0.000007709911,0.002842475,0.0002575226,0.000037296075,0.000046443565,0.0000035232588,0.0000049717946,0.00024767313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985024,0.00004942388,0.00052057975,0.00021257832,0.00036968046,0.00034538496],"domain_scores_gemma":[0.99882716,0.00003144151,0.00039351042,0.0002621312,0.0003669732,0.00011879607],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00026221556,0.00018816128,0.0001858964,0.00012228377,0.00030954677,0.0013164593,0.0005674329,0.00007849432,0.000007890718],"category_scores_gemma":[0.000120196455,0.00015331306,0.000040664003,0.00052710675,0.00004874237,0.0069372826,0.00006171633,0.00015857344,0.00012531223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001623977,0.000067669745,0.002704238,0.0011176164,0.000025071762,0.0000105852805,0.005675781,0.0019540587,0.0008709718,0.7020362,0.0015354088,0.28398615],"study_design_scores_gemma":[0.001649518,0.00033269235,0.0036799419,0.0005671159,0.000024918834,0.000391535,0.0020604867,0.9195918,0.007304101,0.006954368,0.05590727,0.0015362365],"about_ca_topic_score_codex":0.000015603626,"about_ca_topic_score_gemma":6.558931e-7,"teacher_disagreement_score":0.992756,"about_ca_system_score_codex":0.00004544295,"about_ca_system_score_gemma":0.00017199514,"threshold_uncertainty_score":0.9997203},"labels":[],"label_agreement":null},{"id":"W2152205111","doi":"","title":"Augmented Functional Time Series Representation and Forecasting with Gaussian Processes","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Futures contract; Representation (politics); Series (stratigraphy); Computer science; Gaussian; Covariance matrix; Portfolio; Transaction cost; Econometrics; Gaussian process; Mathematical optimization; Algorithm; Economics; Mathematics; Financial economics; Finance","score_opus":0.01995843686720415,"score_gpt":0.2261324574254595,"score_spread":0.20617402055825537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152205111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020062588,0.000036272624,0.9570476,0.0008547346,0.000039485458,0.00009588039,5.6745824e-7,0.00015948286,0.021703415],"genre_scores_gemma":[0.8739169,0.0000056757585,0.12274447,0.00015551082,0.000049000046,0.00000739173,0.0000050082313,0.000006705115,0.0031093117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99908674,0.000008061323,0.00016629834,0.0003122875,0.00021592894,0.00021069673],"domain_scores_gemma":[0.99946237,0.00006773707,0.00008506018,0.00015254364,0.00015083414,0.000081484155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015699078,0.0001045792,0.00009229943,0.00007820797,0.00016132453,0.00021545151,0.00014818774,0.000029272462,0.000055408258],"category_scores_gemma":[0.000047233083,0.00007414706,0.000009758741,0.0005309971,0.000055385142,0.0011296711,0.00007397879,0.000055358567,0.000016913526],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076484523,0.00037873266,0.12289457,0.0011067443,0.00019963394,0.0003797951,0.0035678234,0.00021119259,0.009210872,0.46931994,0.003453904,0.38851196],"study_design_scores_gemma":[0.004663926,0.0025326565,0.6972439,0.00074356276,0.000072686904,0.0052514444,0.0020355585,0.04958878,0.1615175,0.068755336,0.0048009567,0.0027936702],"about_ca_topic_score_codex":0.000013469731,"about_ca_topic_score_gemma":0.000057962603,"teacher_disagreement_score":0.85385436,"about_ca_system_score_codex":0.000011641081,"about_ca_system_score_gemma":0.000086200926,"threshold_uncertainty_score":0.30236292},"labels":[],"label_agreement":null},{"id":"W2153508491","doi":"","title":"Fast Krylov Methods for N-Body Learning","year":2005,"lang":"en","type":"article","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Krylov subspace; Computer science; Kernel (algebra); Computation; Dimensionality reduction; Artificial intelligence; Stability (learning theory); Segmentation; Curse of dimensionality; Similarity (geometry); Algorithm; Pattern recognition (psychology); Machine learning; Image (mathematics); Iterative method; Mathematics","score_opus":0.031059125426706895,"score_gpt":0.3067328870614051,"score_spread":0.2756737616346982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153508491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067189327,0.00004470669,0.9507989,0.002587204,0.000058690523,0.00046391875,0.000044099663,0.0001547516,0.03912875],"genre_scores_gemma":[0.17206632,0.00071075076,0.8133584,0.000034708904,0.00006938714,5.3117424e-7,0.00003456515,0.000021392745,0.013703936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967172,0.0006182766,0.00018459084,0.00085457956,0.00059116236,0.0010341831],"domain_scores_gemma":[0.9970921,0.00086239603,0.00021048184,0.0007188023,0.0006596915,0.00045652597],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013896778,0.00025977963,0.00042662496,0.0010237355,0.0015649487,0.00011741485,0.00355613,0.00015068048,0.00014190494],"category_scores_gemma":[0.00018011506,0.00032502916,0.00029591535,0.0014501344,0.00068362406,0.0016550925,0.0019627481,0.0008422737,0.000022376214],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004984308,0.0003735301,0.0032846194,0.00022253179,0.00017592465,0.00008384527,0.005034615,0.00043242335,0.0030965752,0.411977,0.0030938718,0.5717266],"study_design_scores_gemma":[0.0018971686,0.001012898,0.0055999863,0.000092167225,0.00003572574,0.000018615026,0.0048320885,0.1574893,0.0004313448,0.010362392,0.81771606,0.000512264],"about_ca_topic_score_codex":0.00017327875,"about_ca_topic_score_gemma":0.0002323003,"teacher_disagreement_score":0.81462216,"about_ca_system_score_codex":0.0002265402,"about_ca_system_score_gemma":0.000534237,"threshold_uncertainty_score":0.9999202},"labels":[],"label_agreement":null},{"id":"W2157440733","doi":"","title":"A Marginalized Particle Gaussian Process Regression","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Hyperparameter; Gaussian process; Kriging; Computer science; Particle filter; Gaussian; Artificial intelligence; Regression; Machine learning; Algorithm; Function (biology); Pattern recognition (psychology); Mathematics; Kalman filter; Statistics","score_opus":0.023439802740486824,"score_gpt":0.285334118798544,"score_spread":0.26189431605805713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157440733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043126833,0.0004198591,0.9050213,0.0046667517,0.0003146979,0.00016222463,5.093422e-7,0.00047620287,0.04581162],"genre_scores_gemma":[0.97349423,0.000012012909,0.024588412,0.0004566625,0.00007748487,0.000024304416,5.65309e-7,0.0000071288982,0.0013391877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987671,0.00003331295,0.00017667048,0.00024264962,0.00025843442,0.00052182836],"domain_scores_gemma":[0.9991663,0.000022639635,0.000070754606,0.00042317313,0.00005351439,0.00026361673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021265348,0.00013611515,0.00013080599,0.00003897021,0.0001282679,0.00015391775,0.0006278174,0.00004987741,0.000293339],"category_scores_gemma":[0.000028058814,0.000090264635,0.000040121096,0.00045996372,0.000032222084,0.001423141,0.00013085126,0.00008996103,0.00034997583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020386762,0.00038305536,0.039429937,0.000114367445,0.000015348947,0.000014222592,0.002806743,0.000003166939,0.0018461323,0.8688423,0.0031220997,0.083402224],"study_design_scores_gemma":[0.004849155,0.0006418972,0.16819108,0.0006526888,0.00006854325,0.0007359887,0.0013068989,0.11380051,0.40770215,0.2247533,0.07367303,0.0036247745],"about_ca_topic_score_codex":0.000006513871,"about_ca_topic_score_gemma":0.0000013069888,"teacher_disagreement_score":0.9303674,"about_ca_system_score_codex":0.000015954085,"about_ca_system_score_gemma":0.000053994703,"threshold_uncertainty_score":0.4498348},"labels":[],"label_agreement":null},{"id":"W2162724919","doi":"","title":"Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes","year":2007,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep belief network; Artificial intelligence; Covariance; Kernel (algebra); Computer science; Gaussian process; Pattern recognition (psychology); Backpropagation; Deep learning; Gaussian; Machine learning; Mathematics; Artificial neural network; Statistics","score_opus":0.04064166434881589,"score_gpt":0.3030173763764282,"score_spread":0.26237571202761234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162724919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028021017,0.00030897022,0.99206257,0.00068569323,0.00072846137,0.0008580205,0.0000071981212,0.00037834098,0.0021686547],"genre_scores_gemma":[0.9316623,0.0000035950877,0.06649751,0.0013765201,0.00018151964,0.000081996586,0.000012890994,0.000019782181,0.00016384735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973454,0.000023896378,0.00097034127,0.0003835323,0.0005411431,0.00073567167],"domain_scores_gemma":[0.99764776,0.00009103277,0.0006152576,0.0003746953,0.0009925665,0.0002787045],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00074933603,0.00030925416,0.0003208689,0.00034721673,0.00058993086,0.002023931,0.0009897468,0.00014865134,0.000004992873],"category_scores_gemma":[0.00027974969,0.00027566912,0.00005492568,0.0014897522,0.00004060818,0.0066677043,0.00013018705,0.00017216193,0.000061126986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024653706,0.00020234135,0.0012195624,0.013680792,0.000061567116,0.000033249467,0.02167593,0.084418066,0.002421032,0.10068915,0.0022453011,0.77310646],"study_design_scores_gemma":[0.0008987463,0.00032376713,0.0009413256,0.0008189244,0.000020242278,0.00033344494,0.0010000373,0.94542515,0.0029666086,0.0011369085,0.04513914,0.0009957175],"about_ca_topic_score_codex":0.000050547085,"about_ca_topic_score_gemma":0.000017006256,"teacher_disagreement_score":0.92886025,"about_ca_system_score_codex":0.00013654228,"about_ca_system_score_gemma":0.00038732798,"threshold_uncertainty_score":0.99996954},"labels":[],"label_agreement":null},{"id":"W2164569010","doi":"10.1145/1273496.1273534","title":"Bayesian actor-critic algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Posterior probability; Gaussian process; Posterior predictive distribution; Covariance; Bayesian probability; Kernel (algebra); Prior probability; Bayesian linear regression; Computer science; Covariance function; Algorithm; Mathematics; Hyperparameter; Artificial intelligence; Mathematical optimization; Gaussian; Bayesian inference; Statistics; Discrete mathematics","score_opus":0.011204703659954036,"score_gpt":0.2582695285622598,"score_spread":0.24706482490230577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164569010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037463268,0.00004921217,0.92710906,0.000994248,0.00028064798,0.000056333673,3.8716806e-7,0.00027780354,0.07085766],"genre_scores_gemma":[0.78511167,0.0000049341797,0.21290989,0.000797236,0.000085978725,0.0000019178524,5.151748e-7,0.0000059351305,0.0010819588],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987567,0.0000103171005,0.00020957147,0.00033535578,0.00024486298,0.00044320893],"domain_scores_gemma":[0.9991906,0.00006602929,0.000036314737,0.00044334825,0.00007276507,0.0001909007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029198278,0.00012638248,0.00011310121,0.00010214231,0.000103586186,0.00020930378,0.00086255744,0.000061029958,0.00015794346],"category_scores_gemma":[0.000025822348,0.000103502,0.00004747686,0.0004525747,0.000037875616,0.0005589695,0.00014453364,0.00011523183,0.00019920748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000209343,0.00007103523,0.0010512945,0.00002384592,0.000008665369,0.000105055966,0.00026806022,0.0000011346475,0.0005270992,0.65519536,0.001400564,0.3413458],"study_design_scores_gemma":[0.0024099883,0.0009450288,0.09605662,0.00020867988,0.000034826426,0.00091739517,0.00065465225,0.16340686,0.10771399,0.517594,0.10640872,0.0036492555],"about_ca_topic_score_codex":0.000024383635,"about_ca_topic_score_gemma":0.000019562174,"teacher_disagreement_score":0.784737,"about_ca_system_score_codex":0.000025270143,"about_ca_system_score_gemma":0.00006305813,"threshold_uncertainty_score":0.42206892},"labels":[],"label_agreement":null},{"id":"W2166063021","doi":"","title":"Gaussian Process Dynamical Models","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":432,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian process; Gaussian; Computer science; Dynamical systems theory; Representation (politics); Prior probability; Series (stratigraphy); Nonlinear system; Space (punctuation); Nonparametric statistics; Statistical physics; Artificial intelligence; Algorithm; Mathematics; Physics; Econometrics","score_opus":0.012988947765123942,"score_gpt":0.25153199783955904,"score_spread":0.2385430500744351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166063021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017261183,0.00003411414,0.87849784,0.007357964,0.000049331036,0.00006394395,5.2317955e-7,0.00029703154,0.111973144],"genre_scores_gemma":[0.883296,0.000005623534,0.114482075,0.0010567969,0.00006165269,0.000011167597,6.9791326e-7,0.0000060883517,0.001079869],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887174,0.000011333942,0.00018486581,0.00036941288,0.00024214348,0.00032047907],"domain_scores_gemma":[0.99934167,0.000014032806,0.000041389845,0.00039549102,0.00006206718,0.0001453316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008040166,0.00012827793,0.000116685624,0.00006449304,0.00008672211,0.00021287845,0.0010097858,0.00009323349,0.00010617752],"category_scores_gemma":[0.00000690844,0.00009833272,0.0000401078,0.000327898,0.000032371343,0.00140866,0.00012307726,0.00017762183,0.00024082886],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001451526,0.00006293978,0.00006010284,0.000014854218,0.0000038275916,0.000004600311,0.00027350785,0.0009197914,0.00002842443,0.9277895,0.00047205956,0.07036891],"study_design_scores_gemma":[0.00013444317,0.000027505252,0.00020409774,0.000011124185,0.0000016431541,0.000030343848,0.000016180622,0.86235297,0.0006539194,0.13534354,0.0010381362,0.00018607412],"about_ca_topic_score_codex":0.000005533193,"about_ca_topic_score_gemma":0.000016739485,"teacher_disagreement_score":0.8815699,"about_ca_system_score_codex":0.00002548064,"about_ca_system_score_gemma":0.000097983444,"threshold_uncertainty_score":0.4009892},"labels":[],"label_agreement":null},{"id":"W2170254727","doi":"10.18187/pjsor.v8i3.512","title":"The Bias in Bayes and How to Measure it","year":2012,"lang":"en","type":"article","venue":"Pakistan Journal of Statistics and Operation Research","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayes' theorem; Mathematics; Measure (data warehouse); Bayes' rule; Bayes factor; Likelihood function; Bayesian probability; Computation; Bayesian inference; Statistical inference; Inference; Approximate Bayesian computation; Statistics; Algorithm; Computer science; Artificial intelligence; Estimation theory; Data mining","score_opus":0.10843564406279382,"score_gpt":0.38976515170819515,"score_spread":0.2813295076454013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170254727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010688049,0.0014619363,0.9688207,0.018478608,0.00010245805,0.00011467486,0.000008271883,0.000002050127,0.0003232576],"genre_scores_gemma":[0.9552293,0.00063442555,0.043836135,0.000087982306,0.000059224098,0.0000026899709,2.2672215e-7,0.0000030964904,0.00014693802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99881315,0.0001863198,0.00019378852,0.00008908884,0.0004714682,0.0002461594],"domain_scores_gemma":[0.99865687,0.0004828318,0.0000549427,0.000101735175,0.0005069585,0.0001966574],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0035051429,0.00005585131,0.00009083576,0.00012578201,0.00028299395,0.0010590157,0.0002539251,0.000021979587,0.0000058070277],"category_scores_gemma":[0.0005679482,0.00003429188,0.000006930968,0.00024210902,0.00007247488,0.00039034226,0.00009471561,0.00023466014,0.0000021814312],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036961752,0.00004366073,0.011857409,0.000043214743,0.000012065842,0.00002897862,0.0047846963,0.000012518625,0.00045484322,0.8095955,0.009863566,0.16326654],"study_design_scores_gemma":[0.0040500076,0.0044207834,0.5027775,0.0010684801,0.00002958479,0.0009800092,0.018815655,0.07448737,0.0030031642,0.1215271,0.26751,0.0013303509],"about_ca_topic_score_codex":0.000009013142,"about_ca_topic_score_gemma":0.00008484962,"teacher_disagreement_score":0.9445412,"about_ca_system_score_codex":0.000024536042,"about_ca_system_score_gemma":0.00013240699,"threshold_uncertainty_score":0.999978},"labels":[],"label_agreement":null},{"id":"W2171627535","doi":"","title":"Exponential Family Predictive Representations of State","year":2007,"lang":"en","type":"article","venue":"Deep Blue (University of Michigan)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Unobservable; Computer science; Observable; Dynamical systems theory; Population; Term (time); Representation (politics); Artificial intelligence; Set (abstract data type); State (computer science); Theoretical computer science; Machine learning; Mathematics; Econometrics; Algorithm","score_opus":0.007253500050776444,"score_gpt":0.20137039763991826,"score_spread":0.19411689758914183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171627535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48328426,0.00002754347,0.5151738,0.000045972345,0.000055157758,0.000045735123,0.000006864271,0.000023399243,0.0013372402],"genre_scores_gemma":[0.9666966,0.000025512893,0.03302184,0.000021505382,0.000010271566,6.192231e-8,0.0000035798666,0.0000032666296,0.00021733111],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9991997,0.000023231407,0.00014020658,0.0002380607,0.0002212515,0.00017758674],"domain_scores_gemma":[0.9991957,0.00005537093,0.00018622112,0.0003195742,0.00016373493,0.00007940789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016054936,0.00007773567,0.00014609327,0.00016486351,0.00010013027,0.0000117679565,0.0006918949,0.000043149692,0.00001911849],"category_scores_gemma":[0.000011475111,0.00009536004,0.000070698705,0.000412449,0.00014124118,0.00045628357,0.00021466178,0.0000846594,0.000013231865],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054686976,0.0010062433,0.0034183762,0.00032850495,0.0004882346,0.00039191422,0.6865128,0.0020474882,0.09454646,0.13520208,0.00022985759,0.07528115],"study_design_scores_gemma":[0.005620146,0.0013699278,0.54400814,0.00031202886,0.00022487521,0.00006684798,0.23084956,0.07758425,0.09033432,0.04597583,0.0022492006,0.0014048846],"about_ca_topic_score_codex":0.00015207744,"about_ca_topic_score_gemma":0.0018415678,"teacher_disagreement_score":0.54058975,"about_ca_system_score_codex":0.000010752285,"about_ca_system_score_gemma":0.000068534326,"threshold_uncertainty_score":0.38886696},"labels":[],"label_agreement":null},{"id":"W2187922941","doi":"","title":"A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Categorical variable; Multinomial probit; Gaussian process; Inference; Computer science; Machine learning; Artificial intelligence; Multinomial distribution; Multinomial logistic regression; Likelihood function; Latent variable; Data modeling; Gaussian; Pattern recognition (psychology); Mathematics; Statistics; Algorithm; Estimation theory","score_opus":0.06023143937556515,"score_gpt":0.2827559926869333,"score_spread":0.22252455331136814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187922941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036035443,0.0001201826,0.9952986,0.0010105417,0.00007453485,0.00017289005,0.000012472871,0.00014331924,0.002807065],"genre_scores_gemma":[0.7316931,0.000006628737,0.26789987,0.0001899886,0.00006801283,0.0000219529,0.000026880429,0.000008351274,0.00008526837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982043,0.000021358024,0.00024167175,0.00057580124,0.0002992311,0.00065764185],"domain_scores_gemma":[0.9980802,0.00007264899,0.000103223625,0.0013633335,0.00009476701,0.00028585072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033762507,0.00018916283,0.0002728488,0.00017169799,0.00014766997,0.0002940922,0.0016542241,0.000060902148,0.000028113142],"category_scores_gemma":[0.0000156653,0.00012348688,0.00006777002,0.0011360788,0.000028484374,0.002029797,0.0005094437,0.000095901545,0.000017484095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021958343,0.00032687144,0.0076131066,0.000055170916,0.0005352905,0.0000085797365,0.00076958624,0.0008638773,0.000021464813,0.9604615,0.0008827418,0.028439872],"study_design_scores_gemma":[0.00030394492,0.00009364856,0.0037312123,0.00000966994,0.00027112057,0.000026074436,0.000037275207,0.9710481,0.00008996574,0.023346791,0.00070078,0.0003413966],"about_ca_topic_score_codex":0.00012710923,"about_ca_topic_score_gemma":0.000090626265,"teacher_disagreement_score":0.97018427,"about_ca_system_score_codex":0.000030019573,"about_ca_system_score_gemma":0.000117197465,"threshold_uncertainty_score":0.5035649},"labels":[],"label_agreement":null},{"id":"W2188480449","doi":"","title":"Forecasting and Trading Commodity Contract Spreads with Gaussian Processes","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Econometrics; Computer science; Gaussian process; Futures contract; Autocovariance; Gaussian; Mathematics; Finance; Economics","score_opus":0.02558355460677283,"score_gpt":0.240924313769079,"score_spread":0.21534075916230616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188480449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09385188,0.00009041861,0.8773581,0.00051615946,0.000039857783,0.00011671763,0.0000010375715,0.0001591979,0.027866641],"genre_scores_gemma":[0.9222357,0.000006146489,0.07739712,0.00023842392,0.00004181602,0.0000035560956,7.977462e-7,0.000007545224,0.000068837355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988331,0.000010368887,0.00020095427,0.00035504467,0.0002008581,0.00039965686],"domain_scores_gemma":[0.9992077,0.00019615641,0.0000980953,0.00022749133,0.00009497875,0.00017563213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034553296,0.00016310612,0.00016974134,0.00007753813,0.0002084187,0.00033983894,0.0003991676,0.000050900107,0.000017122193],"category_scores_gemma":[0.00005203679,0.00011201952,0.000014704046,0.00044707916,0.00007888659,0.0008765811,0.00007940078,0.00013993197,0.0000025928866],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011651924,0.00033077062,0.1785119,0.0010331507,0.00008096276,0.0005445553,0.0050142487,0.000012465593,0.0005181599,0.3693354,0.0005161167,0.44398573],"study_design_scores_gemma":[0.005957829,0.0030157904,0.33343187,0.0018009265,0.00011475159,0.0061893403,0.0014508939,0.46860975,0.08951807,0.07943808,0.0061545996,0.004318099],"about_ca_topic_score_codex":0.00004792126,"about_ca_topic_score_gemma":0.00032257024,"teacher_disagreement_score":0.82838386,"about_ca_system_score_codex":0.000016580358,"about_ca_system_score_gemma":0.00010702589,"threshold_uncertainty_score":0.45680234},"labels":[],"label_agreement":null},{"id":"W2202775496","doi":"10.1088/1367-2630/18/3/033024","title":"Practical Bayesian tomography","year":2016,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Perimeter Institute; University of Waterloo","funders":"Army Research Office","keywords":"Bayesian probability; Physics; Prior probability; Estimator; Tomography; Statistical physics; Algorithm; Quantum tomography; Range (aeronautics); Bayesian inference; Quantum state; Computation; Quantum; Applied mathematics; Computer science; Artificial intelligence; Statistics; Optics; Quantum mechanics; Mathematics","score_opus":0.02144192480626209,"score_gpt":0.27765304268175156,"score_spread":0.25621111787548945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2202775496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035120966,0.00004445337,0.9876336,0.01025955,0.00040423407,0.000021038755,5.746658e-7,0.000018530684,0.0012668518],"genre_scores_gemma":[0.7660096,0.000046190253,0.23247151,0.0003188577,0.0010114929,2.9048863e-7,4.2125414e-8,0.0000070042433,0.00013497866],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905205,0.000029161567,0.00026031488,0.0001262966,0.00033819035,0.00019399334],"domain_scores_gemma":[0.99895144,0.00010583425,0.00030856606,0.00024164487,0.00017632399,0.00021621122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001407776,0.00009686893,0.00015792158,0.00005910233,0.000041574945,0.000108174645,0.0005869366,0.000036619753,0.000030254741],"category_scores_gemma":[0.000055859968,0.000056148794,0.00012962792,0.00033360263,0.000036319925,0.0013017241,0.00007014118,0.00013694567,0.000029436891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013127395,0.00010330721,0.0014815852,0.000009265347,0.000037370304,0.00007930369,0.00014926596,0.0000017080008,0.00212406,0.428171,0.030529538,0.53730047],"study_design_scores_gemma":[0.0018864902,0.0009983764,0.004239057,0.00038473686,0.000046740533,0.00089961244,0.00002580673,0.00043495186,0.04734462,0.8978637,0.04538894,0.00048695292],"about_ca_topic_score_codex":0.0000015539903,"about_ca_topic_score_gemma":5.2438816e-7,"teacher_disagreement_score":0.76565844,"about_ca_system_score_codex":0.00001749407,"about_ca_system_score_gemma":0.00036868476,"threshold_uncertainty_score":0.22896814},"labels":[],"label_agreement":null},{"id":"W2233729533","doi":"","title":"Learning community-based preferences via dirichlet process mixtures of Gaussian processes","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Preference learning; Preference; Computer science; Gaussian process; Dirichlet process; Preference elicitation; Inference; Machine learning; Mixture model; Scalability; Collaborative filtering; Variety (cybernetics); Recommender system; Artificial intelligence; Dirichlet distribution; Bayesian inference; Process (computing); Bayesian probability; Gaussian; Mathematics; Statistics","score_opus":0.017008680672627674,"score_gpt":0.2507306148085493,"score_spread":0.23372193413592163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2233729533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14174637,0.00014637318,0.83150923,0.0014213781,0.00008540293,0.0004279499,0.0000019139832,0.0004827346,0.024178648],"genre_scores_gemma":[0.9777074,0.000011203493,0.02149645,0.00039282316,0.000020846785,0.00009358448,0.000007924041,0.000013088318,0.0002566994],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981983,0.00019442059,0.0004202781,0.00034701734,0.00042862463,0.0004113817],"domain_scores_gemma":[0.9980792,0.0002787934,0.00029966826,0.00056614866,0.00062545727,0.00015075508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002699696,0.0002641997,0.0003191945,0.00018146036,0.00035525337,0.0003409284,0.0019982352,0.000108057546,0.00021730193],"category_scores_gemma":[0.00017461114,0.00019213912,0.00005440886,0.0011157976,0.00016160237,0.0011219013,0.00019737956,0.00049267756,0.00007149743],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011304062,0.005861288,0.27400875,0.03223273,0.00042052814,0.00003224456,0.044241935,0.005844388,0.02579202,0.06318636,0.0064357393,0.54183096],"study_design_scores_gemma":[0.0024150764,0.004186689,0.05250603,0.0017192138,0.000081737424,0.00006763947,0.0033976084,0.10938303,0.4676019,0.3525679,0.0027651936,0.003307971],"about_ca_topic_score_codex":0.00041696712,"about_ca_topic_score_gemma":0.000053314474,"teacher_disagreement_score":0.835961,"about_ca_system_score_codex":0.000012476846,"about_ca_system_score_gemma":0.00036456768,"threshold_uncertainty_score":0.78352064},"labels":[],"label_agreement":null},{"id":"W2267787486","doi":"10.48550/arxiv.1509.06061","title":"A Statistical Theory of Deep Learning via Proximal Splitting","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Statistical learning; Computer science; Artificial intelligence; Psychology; Statistical physics; Physics","score_opus":0.04822403401686984,"score_gpt":0.1950540890959423,"score_spread":0.14683005507907246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2267787486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034721516,0.00007056189,0.9597205,0.00002343492,0.00016535446,0.00015563346,0.0000061398246,0.00014595981,0.004990928],"genre_scores_gemma":[0.9765729,0.000019423462,0.022954049,0.00001869529,0.000041919808,7.1781636e-7,0.000008419356,0.000014141435,0.00036975075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981327,0.00024315604,0.0002634549,0.000863257,0.00013632391,0.00036108468],"domain_scores_gemma":[0.99823934,0.00019380756,0.00038483567,0.0006732344,0.00031047288,0.00019832852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006204427,0.00026340134,0.0003758552,0.00017403632,0.00010337288,0.00009831,0.0015568822,0.00022165861,0.00004906016],"category_scores_gemma":[0.00017355221,0.00027940242,0.00010323083,0.0004137971,0.00017952979,0.00028795874,0.0019987414,0.0007443245,0.000043154796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042503434,0.00008437053,0.003956454,0.0002979884,0.00005920444,0.00024519287,0.0006492015,0.020659655,0.000025133211,0.95793575,0.000019036954,0.016025495],"study_design_scores_gemma":[0.00022755812,0.000096714946,0.0012230419,0.000092070855,0.00004134516,0.000008732922,0.00011479288,0.5298374,0.00007327796,0.46793973,0.000051939795,0.00029336364],"about_ca_topic_score_codex":0.000034098153,"about_ca_topic_score_gemma":0.0000056041745,"teacher_disagreement_score":0.9418514,"about_ca_system_score_codex":0.000101203164,"about_ca_system_score_gemma":0.00031181247,"threshold_uncertainty_score":0.9999658},"labels":[],"label_agreement":null},{"id":"W2284548247","doi":"10.1007/s10994-021-06019-1","title":"Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics","year":2021,"lang":"en","type":"preprint","venue":"Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Bayesian optimization; Overshoot (microwave communication); Computer science; Robotics; Process (computing); Context (archaeology); Gaussian process; Robot; Bayesian probability; Set (abstract data type); Artificial intelligence; Probabilistic logic; Optimization problem; Mathematical optimization; Machine learning; Algorithm; Gaussian; Mathematics","score_opus":0.008947761347103697,"score_gpt":0.22550247125578554,"score_spread":0.21655470990868184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284548247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020865947,0.00035876915,0.99459845,0.0010854708,0.00010551206,0.00021600994,0.0000020563175,0.00017914404,0.0013679869],"genre_scores_gemma":[0.52125263,0.00008228571,0.47844693,0.000099908866,0.000014632206,0.000009862333,0.000035347013,0.000018378241,0.00004003826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781114,0.0002573118,0.00047642793,0.0007939684,0.0002845722,0.00037660796],"domain_scores_gemma":[0.9987909,0.000202442,0.00033383627,0.00046479193,0.00008861218,0.00011942629],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004079912,0.00036478616,0.0005074999,0.00023416909,0.0001691854,0.0008843661,0.0005254286,0.00021193312,0.00008795017],"category_scores_gemma":[0.00022870644,0.00032792543,0.00004793345,0.0004171217,0.00011773479,0.00034531977,0.00096201786,0.0012490752,0.0000016031059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048052834,0.000029195175,0.017200947,0.00035387214,0.000030162531,0.0001321478,0.0012703902,0.9150462,0.0000036378844,0.0015023799,0.0000011676865,0.0644251],"study_design_scores_gemma":[0.0003745403,0.00006392915,0.0031140593,0.0011290001,0.00001954274,0.0001088283,0.00010552836,0.9940725,0.0000057740685,0.0005836401,0.000019722038,0.00040294608],"about_ca_topic_score_codex":0.00008779319,"about_ca_topic_score_gemma":0.00008107511,"teacher_disagreement_score":0.519166,"about_ca_system_score_codex":0.00007756336,"about_ca_system_score_gemma":0.00028064472,"threshold_uncertainty_score":0.99991727},"labels":[],"label_agreement":null},{"id":"W2288769965","doi":"","title":"Variance Reduction via Antithetic Markov Chains","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov chain; Markov chain Monte Carlo; Markov chain mixing time; Variable-order Markov model; Variance reduction; Sampling (signal processing); Importance sampling; Sequence (biology); Computer science; Markov model; Estimator; Slice sampling; Algorithm; Monte Carlo method; Mathematics; Mathematical optimization; Markov process; Markov property; Statistics","score_opus":0.01812839071762434,"score_gpt":0.23811630542863851,"score_spread":0.21998791471101417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288769965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008327999,0.00005279509,0.95867926,0.0026722688,0.00052329194,0.000060804356,2.4271026e-7,0.00019390768,0.036984656],"genre_scores_gemma":[0.84695834,0.000008597504,0.14884332,0.0001933242,0.000082014565,0.000005713436,5.228411e-7,0.000004741881,0.0039034504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991445,0.000023258826,0.00014506058,0.00028912816,0.00019292334,0.00020511789],"domain_scores_gemma":[0.99929494,0.00000617743,0.00005533758,0.00040867136,0.000098658515,0.00013621623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020351974,0.0000908123,0.00008864312,0.000054414864,0.000056910794,0.00014662449,0.00054125336,0.000040494837,0.000033973512],"category_scores_gemma":[0.000019683966,0.00007414029,0.000023612823,0.00038007804,0.000031439893,0.00054316013,0.00011295005,0.000070874594,0.00024400918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068549402,0.000104439234,0.00028835834,0.000022741324,0.000009928215,0.000021365331,0.0007490044,0.000036855203,0.0009904046,0.84120893,0.007155709,0.14940542],"study_design_scores_gemma":[0.0016584692,0.0007460603,0.0048732865,0.00010708027,0.000019775076,0.0014919245,0.00023571763,0.59018683,0.01976587,0.33063874,0.048802823,0.0014734583],"about_ca_topic_score_codex":0.000033844088,"about_ca_topic_score_gemma":0.0000018769869,"teacher_disagreement_score":0.84612554,"about_ca_system_score_codex":0.00002673961,"about_ca_system_score_gemma":0.00010650578,"threshold_uncertainty_score":0.31363258},"labels":[],"label_agreement":null},{"id":"W2290039094","doi":"10.1109/globalsip.2015.7418217","title":"Financial time series volatility analysis using Gaussian process state-space models","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stochastic volatility; Gaussian process; State-space representation; State space; Computer science; Likelihood function; Bayesian inference; Volatility (finance); Inference; Parametric statistics; Nonparametric statistics; Econometrics; Finance; Gaussian; Bayesian probability; Algorithm; Mathematics; Estimation theory; Artificial intelligence; Statistics","score_opus":0.02860749148384076,"score_gpt":0.2638410037462742,"score_spread":0.23523351226243347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290039094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078140065,0.000040496234,0.9143222,0.0005063581,0.00006465718,0.00010663659,0.000006551335,0.00021626541,0.0065968013],"genre_scores_gemma":[0.8953683,0.00000191812,0.10292335,0.000111931586,0.00002994752,0.00000615011,0.0000026515945,0.0000079559595,0.0015478114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818254,0.000051869527,0.00031142353,0.0005725911,0.00045334903,0.00042821176],"domain_scores_gemma":[0.99864024,0.000016604268,0.00013522698,0.0006024454,0.00036581547,0.00023969104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033266743,0.0002241593,0.00034800364,0.0002100708,0.00014893073,0.0004035459,0.00086815224,0.000079509686,0.000053986663],"category_scores_gemma":[0.000058992176,0.0001842609,0.0000982302,0.0021856958,0.000074641335,0.0026885697,0.00022226783,0.00012978158,0.00004786485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035170736,0.0008927232,0.05712998,0.00052296976,0.00094174384,0.000260218,0.03703479,0.29153106,0.00064608135,0.58125323,0.005197975,0.024237541],"study_design_scores_gemma":[0.00013537194,0.00004545315,0.0009739385,0.000008558402,0.000042447376,0.000007553401,0.00004765518,0.8539776,0.00059856306,0.14385478,0.00005193117,0.00025609686],"about_ca_topic_score_codex":0.00013875586,"about_ca_topic_score_gemma":0.00010948155,"teacher_disagreement_score":0.8172282,"about_ca_system_score_codex":0.00006545362,"about_ca_system_score_gemma":0.00069985486,"threshold_uncertainty_score":0.75139415},"labels":[],"label_agreement":null},{"id":"W2294767448","doi":"","title":"Bayesian filtering with online Gaussian process latent variable models","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université Laval","funders":"","keywords":"Gaussian process; Computer science; Latent variable; Machine learning; Mixture model; Artificial intelligence; Bayesian probability; Component (thermodynamics); Process (computing); Representation (politics); Computation; Data mining; Gaussian; Algorithm","score_opus":0.013469328151809415,"score_gpt":0.22323910682690304,"score_spread":0.2097697786750936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294767448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011871114,0.00001293716,0.96009654,0.0011301561,0.000074198884,0.00012453891,0.0000022871477,0.00035265915,0.037019577],"genre_scores_gemma":[0.6958816,0.0000035073963,0.30284774,0.0005461085,0.00005211246,0.000014923217,0.0000037914733,0.0000143158795,0.0006358877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829865,0.000027756316,0.00025654025,0.000588703,0.00033489705,0.0004934814],"domain_scores_gemma":[0.9988709,0.000028764967,0.00010082096,0.00065899506,0.00012616266,0.00021436793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000165807,0.00024119724,0.00023535278,0.00009818426,0.00015583618,0.00035598545,0.0010853771,0.00007006581,0.00008387314],"category_scores_gemma":[0.000011437655,0.00016659933,0.000031105377,0.00056916056,0.000039851788,0.0011861337,0.00016933036,0.00016614844,0.00002275981],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018739434,0.0002722298,0.0007707664,0.0002464734,0.00003415207,0.000020289583,0.0006183391,0.022366598,0.00026667368,0.94754857,0.00022507679,0.027612103],"study_design_scores_gemma":[0.00033863285,0.00020342856,0.00034825533,0.00011403011,0.0000070465007,0.000056935507,0.000022586291,0.906701,0.0007891764,0.09058525,0.00048276942,0.000350845],"about_ca_topic_score_codex":0.000046871308,"about_ca_topic_score_gemma":0.00003334412,"teacher_disagreement_score":0.88433444,"about_ca_system_score_codex":0.00002149555,"about_ca_system_score_gemma":0.00013401633,"threshold_uncertainty_score":0.6793724},"labels":[],"label_agreement":null},{"id":"W2295255295","doi":"10.15607/rss.2014.x.001","title":"Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Academy of Finland","keywords":"Trajectory; Gaussian process; Computer science; Regression; Kriging; Process (computing); Estimation; Gaussian; Artificial intelligence; Statistics; Mathematics; Algorithm; Machine learning; Engineering","score_opus":0.007732345053000385,"score_gpt":0.24623942256439085,"score_spread":0.23850707751139047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295255295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028750546,0.000030570853,0.8826351,0.0018131321,0.00021056015,0.00022046514,7.8888786e-7,0.00056847424,0.085770346],"genre_scores_gemma":[0.9429079,0.000005239579,0.052682422,0.00058648136,0.00008209869,0.000024840636,0.0000054274115,0.000017689821,0.0036878802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980407,0.00008532697,0.00035329707,0.0006305677,0.0004556801,0.00043443334],"domain_scores_gemma":[0.9986713,0.000090185,0.0002050772,0.00066853594,0.0001411922,0.00022371502],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037080774,0.00027021678,0.00029499864,0.0001346681,0.00020333557,0.0003896822,0.0010464124,0.0001334729,0.00039494393],"category_scores_gemma":[0.00014595348,0.00020003458,0.000067504196,0.00042165024,0.000063621825,0.0011580797,0.00011835708,0.00018555076,0.0011728953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058510097,0.0005032999,0.0021058528,0.00040102637,0.000041340983,0.000053596657,0.0028818164,0.00054782577,0.0058507877,0.24883635,0.009245535,0.72947407],"study_design_scores_gemma":[0.001224459,0.0007036928,0.008438782,0.00050172035,0.0000264932,0.00017002304,0.000113348426,0.85288215,0.035717558,0.094000876,0.005019963,0.0012009182],"about_ca_topic_score_codex":0.000034216802,"about_ca_topic_score_gemma":0.0000056742647,"teacher_disagreement_score":0.9141574,"about_ca_system_score_codex":0.000031089916,"about_ca_system_score_gemma":0.00017198216,"threshold_uncertainty_score":0.9996048},"labels":[],"label_agreement":null},{"id":"W2295454748","doi":"10.5555/2615731.2617421","title":"Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Traverse; Tree traversal; Mathematical optimization; Gaussian process; Variety (cybernetics); Enhanced Data Rates for GSM Evolution; Graph; Artificial intelligence; Theoretical computer science; Gaussian; Algorithm; Mathematics; Geography","score_opus":0.0032726986644928315,"score_gpt":0.16534731395812466,"score_spread":0.16207461529363182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295454748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0078522125,0.000059179863,0.9225411,0.005041826,0.00040846245,0.00031421543,0.000005536628,0.00035268237,0.063424826],"genre_scores_gemma":[0.97331256,0.0000055651244,0.024482716,0.0007104534,0.000025687668,0.00001220599,0.000013681013,0.000015616224,0.0014215134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836034,0.000030707866,0.00022076159,0.0005064384,0.00027612317,0.00060565444],"domain_scores_gemma":[0.99880815,0.000035820885,0.00007499501,0.0005241601,0.00015816209,0.000398693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016031213,0.00023457203,0.00020311875,0.00014549535,0.000192697,0.00033522226,0.0009871196,0.00010069421,0.000043952426],"category_scores_gemma":[0.000019616942,0.0001655188,0.000035914196,0.000608988,0.00006313939,0.00047519096,0.00006639131,0.00027025136,0.00007178229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068824947,0.000061232204,0.007951036,0.00004230001,0.000043983786,0.00006323876,0.0006229687,0.00056315446,0.000051023413,0.90849525,0.0014411581,0.08065779],"study_design_scores_gemma":[0.000335167,0.00022997148,0.013334942,0.000074426716,0.000009953926,0.00016051675,0.000030127852,0.9784751,0.00007343808,0.003971009,0.0028443967,0.00046094952],"about_ca_topic_score_codex":0.019061927,"about_ca_topic_score_gemma":0.22543858,"teacher_disagreement_score":0.97791195,"about_ca_system_score_codex":0.00011937161,"about_ca_system_score_gemma":0.0004291941,"threshold_uncertainty_score":0.9874702},"labels":[],"label_agreement":null},{"id":"W2318814119","doi":"10.2514/6.2013-4767","title":"Kriged Kalman Filtering for Predicting the Spatio-Temporal Wildfire Temperature Process Evolution","year":2013,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control (GNC) Conference","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kalman filter; Process (computing); Computer science; Remote sensing; Environmental science; Artificial intelligence; Geology","score_opus":0.006643703563006674,"score_gpt":0.22146514797596467,"score_spread":0.214821444412958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2318814119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20143,0.0007405567,0.79047674,0.004867498,0.0003610722,0.0013708725,0.000020270123,0.00022590824,0.0005070598],"genre_scores_gemma":[0.99479496,0.000023315886,0.0032890337,0.00056276796,0.00020773333,0.0007923655,0.000033605043,0.000018556875,0.00027765863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778306,0.00009037028,0.00057658897,0.0006954844,0.000340486,0.00051399355],"domain_scores_gemma":[0.998015,0.0001728715,0.00037138886,0.00054137124,0.00076387654,0.00013548127],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00042298698,0.00033600169,0.0003350503,0.000078274796,0.000952006,0.0010823967,0.00097266654,0.00016399348,0.000012245828],"category_scores_gemma":[0.00013607818,0.00024835515,0.0000765972,0.00048040666,0.00019263796,0.0016168345,0.00008665567,0.00028634173,0.000011344426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021205073,0.00025563323,0.2934673,0.0014141205,0.00031085953,0.000009815512,0.010179015,0.00040107899,0.012033871,0.58030695,0.004774716,0.096634574],"study_design_scores_gemma":[0.0041359137,0.00047870207,0.27559894,0.001449817,0.00010035436,0.00006113361,0.0011032188,0.46902514,0.0026772418,0.24001293,0.004171937,0.0011846742],"about_ca_topic_score_codex":0.0003231865,"about_ca_topic_score_gemma":0.00006873698,"teacher_disagreement_score":0.79336494,"about_ca_system_score_codex":0.00004938175,"about_ca_system_score_gemma":0.00038314887,"threshold_uncertainty_score":0.99999684},"labels":[],"label_agreement":null},{"id":"W2319263640","doi":"10.2514/6.2012-4682","title":"Planning under Uncertainty using Bayesian Nonparametric Models","year":2012,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Multidisciplinary University Research Initiative","keywords":"Nonparametric statistics; Computer science; Bayesian probability; Measurement uncertainty; Artificial intelligence; Econometrics; Machine learning; Statistics; Mathematics","score_opus":0.03233562728791778,"score_gpt":0.27475064870632887,"score_spread":0.2424150214184111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319263640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050151385,0.0024575517,0.9437545,0.0004370412,0.00032134232,0.00024701856,0.000006393837,0.00014442723,0.0024803577],"genre_scores_gemma":[0.9818095,0.00005306524,0.01701617,0.0008674801,0.00011984145,0.000033727836,0.0000053969984,0.000015969665,0.00007886177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977472,0.00011325543,0.0005124492,0.00054856,0.0003597679,0.00071876514],"domain_scores_gemma":[0.9983642,0.00017412627,0.00027441024,0.0005260087,0.00037585277,0.00028540244],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050586177,0.00032704434,0.00039066572,0.00021437618,0.00043590803,0.0005125972,0.00069923844,0.0001591113,0.000013486232],"category_scores_gemma":[0.0000447106,0.00029959585,0.00006711603,0.0009413698,0.00016233143,0.0021271578,0.00012408548,0.00025486815,0.0000112671505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030215964,0.00011395565,0.029598475,0.00011423041,0.0000892471,0.0000073097317,0.0013710128,0.021352107,0.0014577869,0.9206737,0.00012703253,0.025064893],"study_design_scores_gemma":[0.0010047206,0.00005002145,0.012887452,0.00025295484,0.000041164047,0.000042352738,0.00015218375,0.87154895,0.00014563449,0.11296402,0.0004543472,0.00045617257],"about_ca_topic_score_codex":0.000111001485,"about_ca_topic_score_gemma":0.0000031662212,"teacher_disagreement_score":0.9316581,"about_ca_system_score_codex":0.00007582509,"about_ca_system_score_gemma":0.0002925525,"threshold_uncertainty_score":0.99994564},"labels":[],"label_agreement":null},{"id":"W2344934955","doi":"10.1016/j.jprocont.2016.04.003","title":"Robust Gaussian process modeling using EM algorithm","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Health Solutions","keywords":"Gaussian process; Robust regression; Conjugate gradient method; Algorithm; Stability (learning theory); Kriging; Regression; Computer science; Marginal likelihood; Bayesian linear regression; Convergence (economics); Mathematical optimization; Regression analysis; Mathematics; Gaussian; Bayesian probability; Bayesian inference; Machine learning; Artificial intelligence; Statistics","score_opus":0.02819780691223422,"score_gpt":0.2661950150392292,"score_spread":0.23799720812699499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344934955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016036382,0.00043201036,0.98130745,0.0015604777,0.0003372172,0.0001261079,0.000004087431,0.00005241594,0.00014387348],"genre_scores_gemma":[0.94021934,0.000027228498,0.059044242,0.00024710392,0.0004012787,0.0000052731984,8.2290406e-8,0.000020999101,0.0000344687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974812,0.000055908647,0.00084886723,0.00035620146,0.0007379598,0.0005198585],"domain_scores_gemma":[0.99730146,0.000067108966,0.0008352477,0.00030823544,0.0011961765,0.00029176692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005519392,0.000269574,0.00049777736,0.00028465866,0.00017854254,0.00034545158,0.0015578985,0.000117705735,0.000028595972],"category_scores_gemma":[0.00015719069,0.00016154817,0.0001575681,0.000501689,0.00004533099,0.0026307763,0.000056881374,0.00027397624,0.000010065686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024550353,0.0006369387,0.0022603513,0.00064315833,0.00041689537,0.00066762517,0.0040026046,0.07938567,0.0055475268,0.008177821,0.0001405734,0.8978753],"study_design_scores_gemma":[0.0026296983,0.0002390995,0.00006378168,0.00069958053,0.000056729834,0.0007904441,0.00023138964,0.96479136,0.0014918764,0.028584458,0.000040835846,0.0003807394],"about_ca_topic_score_codex":0.0000021377182,"about_ca_topic_score_gemma":0.0000016063909,"teacher_disagreement_score":0.92418295,"about_ca_system_score_codex":0.00008416493,"about_ca_system_score_gemma":0.0007692126,"threshold_uncertainty_score":0.6587743},"labels":[],"label_agreement":null},{"id":"W2390241580","doi":"","title":"Sequential Inference for Deep Gaussian Process","year":2016,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of Toronto","funders":"","keywords":"Gaussian process; Inference; Computer science; Process (computing); Artificial intelligence; Machine learning; Gaussian","score_opus":0.08939333074622631,"score_gpt":0.3665205894727141,"score_spread":0.2771272587264878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2390241580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004118804,0.000010065239,0.98910993,0.005535972,0.0006685597,0.00022116778,0.00019277282,0.00008429998,0.0037653677],"genre_scores_gemma":[0.93305737,0.00012828525,0.065991975,0.00032656238,0.00014149351,0.000071782146,0.000013567564,0.000011708219,0.00025725938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981113,0.000039546427,0.00047552303,0.00060204975,0.000405266,0.00036632983],"domain_scores_gemma":[0.9981811,0.000510369,0.00018915332,0.00028335824,0.0006664972,0.00016950746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024158388,0.00024014042,0.0002004783,0.00016373945,0.00018116547,0.0005128012,0.000990836,0.000092358074,0.00028855162],"category_scores_gemma":[0.0006426385,0.00017366953,0.00004320716,0.00015179702,0.00021904985,0.0005438414,0.000117050346,0.000110848574,0.0001184573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004360295,0.00004425968,0.00003745598,0.000012684713,0.000012553577,0.000006594724,0.0002191021,0.0000100156285,0.00062263716,0.6529816,0.000044788125,0.34596473],"study_design_scores_gemma":[0.0000807853,0.00029328582,0.00011628163,0.00010945,0.00000696549,0.000013220476,0.0001346913,0.1174789,0.007513685,0.8735382,0.00041555654,0.00029899937],"about_ca_topic_score_codex":0.000011199855,"about_ca_topic_score_gemma":0.000068337016,"teacher_disagreement_score":0.9326455,"about_ca_system_score_codex":0.000048894468,"about_ca_system_score_gemma":0.00022516884,"threshold_uncertainty_score":0.7082038},"labels":[],"label_agreement":null},{"id":"W2397593287","doi":"","title":"Comparing GPLVM approaches for dimensionality reduction in character animation","year":2008,"lang":"en","type":"article","venue":"Digital Library (University of West Bohemia)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Dimensionality reduction; Computer science; Animation; Character (mathematics); Curse of dimensionality; Reduction (mathematics); Artificial intelligence; Process (computing); Task (project management); Computer animation; Computer graphics (images); Mathematics","score_opus":0.0466500042455788,"score_gpt":0.18246421600253748,"score_spread":0.13581421175695868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397593287","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91037655,0.00002580315,0.07748401,0.0011062815,0.000056796445,0.00016909423,0.00003451756,0.000108009495,0.010638909],"genre_scores_gemma":[0.9866936,0.000008095375,0.01277244,0.000012396843,0.000021508464,4.9242834e-7,0.00009231679,0.000004303766,0.00039480467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993872,0.000008993907,0.0001110919,0.00024691506,0.0001109205,0.00013489996],"domain_scores_gemma":[0.9996226,0.000025857848,0.00010236598,0.00017555928,0.000020444846,0.000053207794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031851032,0.000081208615,0.0001455745,0.00009754388,0.00012133665,0.00006469174,0.0003862392,0.000049650385,0.000010778065],"category_scores_gemma":[0.0000052457663,0.00009655749,0.000056298348,0.00029114418,0.000080847996,0.0058849216,0.00018765015,0.00006867994,0.000011964551],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030464496,0.0013315664,0.446141,0.00061924546,0.00006732853,0.0000799055,0.00573029,0.00032769158,0.000942589,0.4994218,0.0064948006,0.03853913],"study_design_scores_gemma":[0.0012507451,0.0001644787,0.91302586,0.00014730127,0.000007699149,0.000097296404,0.00066346134,0.056924637,0.0023992548,0.02060259,0.004192452,0.00052421115],"about_ca_topic_score_codex":0.000005940617,"about_ca_topic_score_gemma":7.273205e-7,"teacher_disagreement_score":0.47881922,"about_ca_system_score_codex":0.000015385369,"about_ca_system_score_gemma":0.000069887195,"threshold_uncertainty_score":0.42664263},"labels":[],"label_agreement":null},{"id":"W2398886555","doi":"10.1017/cbo9781316219232","title":"An Introduction to the Theory of Reproducing Kernel Hilbert Spaces","year":2016,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":499,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reproducing kernel Hilbert space; Kernel (algebra); Computer science; Algebra over a field; Hilbert space; Interpolation (computer graphics); Representation (politics); Theoretical computer science; Mathematics; Calculus (dental); Pure mathematics; Artificial intelligence","score_opus":0.011852772847081482,"score_gpt":0.19938889890856853,"score_spread":0.18753612606148706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398886555","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009570498,0.000081066944,0.31447756,0.0013890896,0.00062496617,0.00039937033,0.000058612386,0.00014848042,0.68272513],"genre_scores_gemma":[0.004083059,0.000023077742,0.0014393731,0.0000991002,0.0008476482,0.0000014033926,0.00000742612,0.000020514282,0.9934784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801576,0.000162171,0.00020471979,0.0010137535,0.0003159622,0.00028765117],"domain_scores_gemma":[0.99687546,0.00009194733,0.00032585405,0.0022772155,0.00028437475,0.0001451475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050091656,0.00027692984,0.00031798668,0.00018060853,0.00021279478,0.00013085335,0.0024948337,0.00017025053,0.0000027107353],"category_scores_gemma":[0.000051772502,0.00020450246,0.00011166477,0.00005067344,0.00019152135,0.00036061442,0.0007011795,0.00027041175,0.000013265888],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036000074,0.000011223662,0.0000020046118,0.00006624373,0.000038673206,0.000014865111,0.0003077017,0.000005084914,0.00017347686,0.87805253,0.1076618,0.013630379],"study_design_scores_gemma":[0.00016899337,0.00013795267,0.0000651059,0.00017671811,0.0000606821,0.000022720038,0.000052309922,0.00008765877,0.0030503876,0.00061391434,0.9951964,0.00036713094],"about_ca_topic_score_codex":0.000046092024,"about_ca_topic_score_gemma":0.000002467462,"teacher_disagreement_score":0.8875346,"about_ca_system_score_codex":0.00015022089,"about_ca_system_score_gemma":0.00038232078,"threshold_uncertainty_score":0.8339368},"labels":[],"label_agreement":null},{"id":"W2411727234","doi":"","title":"Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings","year":2016,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reproducing kernel Hilbert space; Hilbert space; Computer science; Entropy (arrow of time); Kernel (algebra); Mathematics; Theoretical computer science; Pure mathematics; Physics","score_opus":0.10725564927045814,"score_gpt":0.37334982183748744,"score_spread":0.2660941725670293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2411727234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066405204,0.000014561559,0.9776376,0.013003775,0.00037202344,0.00012242215,0.00017186445,0.00006259464,0.0019746586],"genre_scores_gemma":[0.8613233,0.0002456202,0.13712344,0.00027267187,0.0002516068,0.0000042397405,0.000009090921,0.000012857081,0.00075719116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99775606,0.00008358886,0.00047808018,0.0007633106,0.0005321434,0.00038679613],"domain_scores_gemma":[0.99819076,0.00041532083,0.0001901195,0.00039514265,0.00063670526,0.00017198056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033013805,0.00023722938,0.00021140257,0.00028604275,0.00018932602,0.00042354144,0.0007722195,0.00008254641,0.00019466186],"category_scores_gemma":[0.0010607267,0.00017958753,0.00003991724,0.00030676552,0.0002572993,0.00066954177,0.00027813858,0.0002149069,0.00011372122],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034678684,0.00008598246,0.00012363988,0.000006917324,0.000021118265,0.000023676048,0.00063218025,0.000051354327,0.004665838,0.8368392,0.000043219316,0.15747221],"study_design_scores_gemma":[0.000065280976,0.00019383758,0.0003847573,0.00024608796,0.0000072873863,0.000044592674,0.0002630328,0.27679697,0.011243169,0.7101678,0.00026685296,0.00032030282],"about_ca_topic_score_codex":0.00028259138,"about_ca_topic_score_gemma":0.000050828792,"teacher_disagreement_score":0.85468274,"about_ca_system_score_codex":0.00016541357,"about_ca_system_score_gemma":0.00039828432,"threshold_uncertainty_score":0.73233664},"labels":[],"label_agreement":null},{"id":"W2523799825","doi":"10.1016/j.eswa.2016.09.018","title":"An online Bayesian filtering framework for Gaussian process regression: Application to global surface temperature analysis","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Natural Science Foundation of China","keywords":"Computer science; Gaussian process; Kriging; Bayesian probability; Regression; Process (computing); Artificial intelligence; Regression analysis; Machine learning; Data mining; Bayesian linear regression; Gaussian; Pattern recognition (psychology); Bayesian inference; Statistics; Mathematics","score_opus":0.013997732222198038,"score_gpt":0.3108659674084834,"score_spread":0.29686823518628536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523799825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017584438,0.0001974965,0.9907456,0.004839396,0.000069236565,0.0017331765,0.00016712051,0.0003895893,0.00009993056],"genre_scores_gemma":[0.68558353,0.000007658674,0.31131545,0.00023923807,0.00023347078,0.002485129,0.000040912066,0.000018984281,0.00007565291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997535,0.000056393405,0.00043691145,0.0011278004,0.00038554578,0.00045835713],"domain_scores_gemma":[0.9970569,0.000101880185,0.00026561646,0.0017501096,0.00038621013,0.00043925724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018426278,0.00032912384,0.00040104683,0.00012507847,0.00047760532,0.00039631117,0.0015188126,0.00018770449,0.0000070862743],"category_scores_gemma":[0.0000236851,0.00019906193,0.00008491659,0.002611846,0.00004653719,0.0006012608,0.00007298519,0.000110824076,0.000020009777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017843016,0.00123848,0.009751517,0.0005225399,0.0006530862,0.000008113115,0.0035901929,0.008851778,0.026443562,0.8431242,0.001745337,0.103892766],"study_design_scores_gemma":[0.0070632556,0.00438845,0.04430475,0.007989835,0.0012000777,0.0005971058,0.009693449,0.5767031,0.032203577,0.11604058,0.18619084,0.01362499],"about_ca_topic_score_codex":0.00005661007,"about_ca_topic_score_gemma":0.00007249427,"teacher_disagreement_score":0.7270836,"about_ca_system_score_codex":0.00012976283,"about_ca_system_score_gemma":0.00016353739,"threshold_uncertainty_score":0.811751},"labels":[],"label_agreement":null},{"id":"W2526158400","doi":"10.1007/978-3-319-47157-0_25","title":"Learning for Graph-Based Sensorless Freehand 3D Ultrasound","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Decorrelation; Artificial intelligence; Computer vision; Speckle pattern; Trajectory; Graph; Imaging phantom; Gaussian; Motion estimation; Gaussian process; Position (finance); Algorithm; Theoretical computer science","score_opus":0.01280030032652131,"score_gpt":0.23438755569785635,"score_spread":0.22158725537133506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526158400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004117086,0.00024248273,0.99355245,0.0008120188,0.0012111503,0.00048721305,0.000013094324,0.00020845991,0.0034319854],"genre_scores_gemma":[0.289583,0.000045366025,0.70656884,0.0013597091,0.00072112045,0.000040217205,0.000008308338,0.00006933598,0.0016040768],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99572414,0.00003381574,0.0005444631,0.0018807041,0.0008546635,0.00096220535],"domain_scores_gemma":[0.9962475,0.0014751535,0.00041490913,0.0011934448,0.00043270388,0.00023631648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073634204,0.000657787,0.0006094075,0.0008473419,0.0005160786,0.0009260136,0.0033289646,0.00039181553,0.000037922742],"category_scores_gemma":[0.00022725186,0.00051716244,0.00020939979,0.00055426557,0.0008484439,0.00060346525,0.00047083045,0.00069269084,0.000048102902],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019047495,0.000038556722,0.00023094434,0.0002255438,0.000021048854,0.00006693842,0.0003744273,0.017768888,0.00086428376,0.065507405,0.000041434225,0.9148415],"study_design_scores_gemma":[0.0012287395,0.0008149219,0.00019103613,0.0017647361,0.000026745689,0.00012153163,2.5777229e-7,0.3298348,0.007185109,0.6507454,0.006065184,0.002021552],"about_ca_topic_score_codex":0.0000047820654,"about_ca_topic_score_gemma":0.000030898424,"teacher_disagreement_score":0.9128199,"about_ca_system_score_codex":0.00018612549,"about_ca_system_score_gemma":0.00072990626,"threshold_uncertainty_score":0.999728},"labels":[],"label_agreement":null},{"id":"W2529146392","doi":"10.22331/q-2017-04-25-5","title":"QInfer: Statistical inference software for quantum applications","year":2017,"lang":"en","type":"article","venue":"Quantum","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Army Research Office; Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Industry Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Benchmarking; Statistical inference; Robustness (evolution); Software; Inference; Statistical hypothesis testing; Data mining; Theoretical computer science; Data science; Computer engineering; Machine learning; Artificial intelligence; Mathematics; Programming language","score_opus":0.032938277259389255,"score_gpt":0.32701392794459977,"score_spread":0.2940756506852105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529146392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028477315,0.00006239324,0.99578625,0.0020639503,0.00028908512,0.00041169804,0.00008116934,0.00022615693,0.0007945192],"genre_scores_gemma":[0.82321954,0.000029236846,0.17571259,0.00022211157,0.00012880025,0.00039790606,0.000018405179,0.000016524198,0.00025489755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99844605,0.00001844025,0.00029024258,0.00055688067,0.00023436695,0.0004540367],"domain_scores_gemma":[0.9974606,0.00033069326,0.00023586245,0.001503363,0.00028079672,0.00018868526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019935945,0.00019394585,0.00023159289,0.00005940597,0.0009981958,0.00095245044,0.0022807152,0.0000934248,0.00003485884],"category_scores_gemma":[0.0006876924,0.0001740606,0.00005987217,0.000113406684,0.00017450472,0.00073173386,0.0003547977,0.00015113247,0.00021397012],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004238248,0.000052723062,0.0010007673,0.0000682722,0.000007700726,0.0000035819728,0.00005395446,0.0000023054436,0.000033539443,0.95425767,0.0021370684,0.042378172],"study_design_scores_gemma":[0.00043759003,0.00016466591,0.013608909,0.00004871695,0.000015389858,0.000011600416,0.000013740946,0.079347685,0.00023987326,0.84755677,0.05810764,0.00044739794],"about_ca_topic_score_codex":0.00003024353,"about_ca_topic_score_gemma":0.000017930397,"teacher_disagreement_score":0.82293475,"about_ca_system_score_codex":0.000024481553,"about_ca_system_score_gemma":0.00027989852,"threshold_uncertainty_score":0.9184502},"labels":[],"label_agreement":null},{"id":"W2544432336","doi":"10.48550/arxiv.1310.6007","title":"Efficient Optimization for Sparse Gaussian Process Regression","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperparameter; Gaussian process; Computer science; Kriging; Gaussian; Regression; Set (abstract data type); Mathematical optimization; Algorithm; Process (computing); Linear regression; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.05389550652292334,"score_gpt":0.20513171407577713,"score_spread":0.15123620755285377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544432336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037066385,0.00005241556,0.95941037,0.00028323336,0.0005530298,0.0007972025,0.000015229853,0.0002887746,0.0015333715],"genre_scores_gemma":[0.96658194,0.0000534035,0.032303907,0.00007847789,0.00008431698,0.000013281318,0.000029251962,0.00002564269,0.00082975975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976662,0.000058711066,0.00027796044,0.0013840393,0.00013408624,0.00047903202],"domain_scores_gemma":[0.9977124,0.000060985385,0.000462627,0.0011069109,0.00042278145,0.00023433572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017807828,0.00040830992,0.00036496608,0.0002733523,0.0002709016,0.00034115577,0.0020461557,0.00036920904,0.0000710017],"category_scores_gemma":[0.0000503939,0.00038424693,0.00018354722,0.00061034865,0.00008866086,0.00030690042,0.0009799476,0.00037455276,0.00005646024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001780823,0.0001025518,0.00013065353,0.0003349829,0.000022163078,0.000025841004,0.00020061071,0.89349717,0.0000048030706,0.10441193,0.0002765072,0.00097497477],"study_design_scores_gemma":[0.00040218033,0.000059724123,0.0001097792,0.00033780598,0.00003845034,0.0000038914654,0.00004895864,0.95705914,0.0001448654,0.041235734,0.000089684836,0.00046980652],"about_ca_topic_score_codex":0.000026778,"about_ca_topic_score_gemma":0.0000036015522,"teacher_disagreement_score":0.9295156,"about_ca_system_score_codex":0.00013765397,"about_ca_system_score_gemma":0.0003722266,"threshold_uncertainty_score":0.99986094},"labels":[],"label_agreement":null},{"id":"W2564136532","doi":"10.1109/dsaa.2016.67","title":"Informative Priors and Bayesian Computation","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Prior probability; Computer science; Prior information; Inference; Bayesian probability; Bayesian inference; Machine learning; Computation; Artificial intelligence; Algorithm","score_opus":0.006763566499875171,"score_gpt":0.22178224796658683,"score_spread":0.21501868146671166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564136532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036551382,0.000010375241,0.9788973,0.0027090663,0.000044169912,0.00005176316,4.4803787e-7,0.00010598387,0.0145258065],"genre_scores_gemma":[0.91989595,0.000012016188,0.07953686,0.00027721142,0.000008287053,0.0000026670255,1.4742638e-7,0.0000017666341,0.00026509733],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951434,0.000010151414,0.000115975396,0.0001368558,0.00009714805,0.00012551853],"domain_scores_gemma":[0.9996852,0.0000489385,0.000045619163,0.000109512664,0.000044730114,0.00006598148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067110625,0.00006558448,0.00006267156,0.000051419716,0.0000576024,0.00010939264,0.00019254327,0.000023934732,0.00001894677],"category_scores_gemma":[0.00001456549,0.000036271158,0.000011069703,0.00013163227,0.0000374005,0.0010384374,0.00010599163,0.000023735234,0.000051641637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001395296,0.000007688596,0.0013211486,0.000011932099,0.000004123892,0.0000017659019,0.0007528608,8.855505e-7,0.00012351843,0.42403176,0.00046767562,0.57327527],"study_design_scores_gemma":[0.0030031956,0.0007369129,0.22977133,0.0003535676,0.00001236986,0.00022471811,0.00045363497,0.16280341,0.014061262,0.578172,0.0089304885,0.0014771096],"about_ca_topic_score_codex":0.000003951945,"about_ca_topic_score_gemma":0.0000026912262,"teacher_disagreement_score":0.9162408,"about_ca_system_score_codex":0.0000102102595,"about_ca_system_score_gemma":0.00003346497,"threshold_uncertainty_score":0.14790949},"labels":[],"label_agreement":null},{"id":"W2573582133","doi":"","title":"Gaze Following as Goal Inference: A Bayesian Model","year":2011,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Bayesian inference; Artificial intelligence; Inference; Computer science; Probabilistic logic; Bayesian probability; Graphical model; Machine learning; Psychology; Cognitive science","score_opus":0.019812650765468138,"score_gpt":0.22744851230192897,"score_spread":0.20763586153646083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573582133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04448578,0.00017169068,0.7194773,0.0005933956,0.00026509058,0.00031429707,0.00021779067,0.0013819495,0.23309274],"genre_scores_gemma":[0.9443532,0.000011324164,0.05299364,0.0011526257,0.0000608689,0.00003428199,0.00004870303,0.000060802362,0.001284536],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99697757,0.00005302333,0.00058370834,0.00091725495,0.00059561915,0.00087281334],"domain_scores_gemma":[0.99806553,0.00008462355,0.00017993277,0.0009855968,0.00005119644,0.00063309266],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017298809,0.00048502436,0.0003827597,0.0002515413,0.00025621036,0.0029527226,0.0026021723,0.00023181905,0.00035758488],"category_scores_gemma":[0.0002241008,0.00043505774,0.00031597333,0.00093233795,0.000088916386,0.013093796,0.0009561419,0.00058488053,0.0033169799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012632433,0.0009280623,0.13474567,0.00018567966,0.00018639243,0.00083134905,0.0012203174,0.000055829893,0.00025671095,0.7719198,0.002418988,0.087124884],"study_design_scores_gemma":[0.000888684,0.00030599636,0.003213562,0.0002787247,0.00002810419,0.0000690899,0.00004819188,0.043595213,0.0044510937,0.9355749,0.010020185,0.0015262594],"about_ca_topic_score_codex":0.0000053744243,"about_ca_topic_score_gemma":8.090415e-7,"teacher_disagreement_score":0.8998674,"about_ca_system_score_codex":0.000030935178,"about_ca_system_score_gemma":0.0005051986,"threshold_uncertainty_score":0.9998101},"labels":[],"label_agreement":null},{"id":"W2574023156","doi":"10.22360/springsim.2016.tmsdevs.040","title":"An Aspect Oriented Framework to Applying Markov Chain Monte Carlo Methods with Dynamic Models","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Probabilistic logic; Graphical model; Monte Carlo method; Bayesian probability; Data mining; Calibration; Machine learning; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.012638159550610633,"score_gpt":0.2997048039026276,"score_spread":0.28706664435201695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574023156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037279788,0.000030710635,0.9907263,0.0018931137,0.00013423484,0.00033737108,0.0000030050014,0.00037901048,0.0027682732],"genre_scores_gemma":[0.47237393,0.00000469633,0.526815,0.0003579787,0.000017490673,0.000063345164,1.4762911e-7,0.000012116335,0.00035527503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802035,0.00011571634,0.00024043796,0.00078908604,0.00031803866,0.0005163704],"domain_scores_gemma":[0.9982343,0.00014787231,0.00008167244,0.0010723434,0.00014196413,0.00032188848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044376537,0.00024830856,0.00025274916,0.00015584385,0.00014283264,0.00021956528,0.0011887597,0.000095912656,0.000043638436],"category_scores_gemma":[0.000042891927,0.00014050397,0.000043269018,0.00081406505,0.00004376684,0.0010206104,0.000226766,0.0001425511,0.000028075008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034523382,0.000067059154,0.00028728598,0.000020282743,0.00002670205,0.000024894158,0.0008848405,0.00069919706,0.0014752742,0.44588825,0.00005817023,0.55053353],"study_design_scores_gemma":[0.00045951206,0.0006605173,0.0010660583,0.00033961746,0.000013453479,0.000046652487,0.00017388715,0.8757786,0.0023677498,0.11761007,0.0006650561,0.0008187998],"about_ca_topic_score_codex":0.0000747433,"about_ca_topic_score_gemma":0.00007136803,"teacher_disagreement_score":0.87507945,"about_ca_system_score_codex":0.00007807608,"about_ca_system_score_gemma":0.000111497175,"threshold_uncertainty_score":0.5729585},"labels":[],"label_agreement":null},{"id":"W2574090101","doi":"","title":"Distance-preserving probabilistic embeddings with side information: variational Bayesian multidimensional scaling Gaussian process","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Embedding; Markov chain Monte Carlo; Probabilistic logic; Leverage (statistics); Gaussian process; Dimensionality reduction; Bayesian probability; Artificial intelligence; Posterior probability; Nonlinear dimensionality reduction; Bayesian inference; Gaussian; Algorithm; Machine learning; Theoretical computer science","score_opus":0.0351644273836407,"score_gpt":0.2815418654416019,"score_spread":0.24637743805796122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574090101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024848308,0.00000459016,0.9704209,0.015452658,0.00065349817,0.00033946565,0.00003722179,0.00023518961,0.010371661],"genre_scores_gemma":[0.96038693,0.000011709496,0.038551856,0.0005783873,0.00017010771,0.00011171948,0.000018485189,0.000017078963,0.00015373067],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99629927,0.00006718829,0.0009610059,0.00078259484,0.0013432704,0.000546693],"domain_scores_gemma":[0.9970297,0.0002675631,0.00051114225,0.00057957007,0.001346562,0.0002654121],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040099196,0.00041161405,0.0003045568,0.0003566334,0.00030401174,0.00077263947,0.0015785375,0.00012347891,0.00063398114],"category_scores_gemma":[0.0005691685,0.0002793027,0.00009631376,0.00051993656,0.00024805262,0.003125705,0.00026638128,0.0002836332,0.0004624376],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010649062,0.00012958358,0.0001282508,0.000030274372,0.000034361314,0.000021619446,0.00072432816,0.0015823729,0.00068542,0.91205305,0.00004259109,0.08446164],"study_design_scores_gemma":[0.00023678107,0.0002688258,0.0013043743,0.0012924599,0.000012938401,0.000087764965,0.00035475596,0.4961033,0.017712943,0.48087484,0.0009022151,0.0008488018],"about_ca_topic_score_codex":0.000043468845,"about_ca_topic_score_gemma":0.000064735024,"teacher_disagreement_score":0.9579021,"about_ca_system_score_codex":0.00021504803,"about_ca_system_score_gemma":0.0005560268,"threshold_uncertainty_score":0.9999659},"labels":[],"label_agreement":null},{"id":"W2576239431","doi":"","title":"Action selection for hammer shots in curling","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Delaunay triangulation; Curling; Hammer; Computer science; Action (physics); Selection (genetic algorithm); Artificial intelligence; State space; Triangulation; Computer vision; Mathematical optimization; Algorithm; Mathematics; Engineering; Geometry; Statistics; Structural engineering; Mechanical engineering; Physics","score_opus":0.17443978938971277,"score_gpt":0.359484580898046,"score_spread":0.18504479150833325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576239431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008219464,0.0000045668335,0.9761778,0.011261648,0.0013488205,0.00024267478,0.000007795823,0.00011059197,0.0026266244],"genre_scores_gemma":[0.9871933,0.000054694072,0.011811567,0.00030184214,0.00019450512,0.000100810015,0.000002667587,0.000010735417,0.00032989267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99810225,0.000039201477,0.0005392948,0.00061140384,0.00036340408,0.0003444385],"domain_scores_gemma":[0.9988975,0.00017031375,0.00018711452,0.00023282884,0.00042715305,0.00008508457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033610847,0.00019516407,0.00017294385,0.00036352852,0.00009368579,0.0002907247,0.0007606802,0.000097131924,0.00024159117],"category_scores_gemma":[0.00029054246,0.00015093664,0.0000858426,0.00030796163,0.00006359111,0.0008362359,0.00008793987,0.00014844396,0.00026907155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036577996,0.000076834825,0.00014060961,0.0000051272223,0.0000064501332,0.0000018927972,0.000107924694,0.00008195061,0.017852873,0.5899582,0.000047303853,0.3916843],"study_design_scores_gemma":[0.00011084197,0.00022771073,0.0008452256,0.00032605542,0.0000029745793,0.000014824021,0.000091112255,0.21482764,0.24951582,0.5321574,0.0015153434,0.0003650164],"about_ca_topic_score_codex":0.000064506545,"about_ca_topic_score_gemma":0.0003666283,"teacher_disagreement_score":0.9789738,"about_ca_system_score_codex":0.00018660376,"about_ca_system_score_gemma":0.00015920428,"threshold_uncertainty_score":0.61550176},"labels":[],"label_agreement":null},{"id":"W2585861133","doi":"10.1109/glocom.2016.7841857","title":"Gaussian Process Regression Based Traffic Modeling and Prediction in High-Speed Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kriging; Computer science; Gaussian process; Hurst exponent; Traffic generation model; Ground-penetrating radar; Range (aeronautics); Data mining; Covariance; Artificial intelligence; Machine learning; Data modeling; Covariance function; Gaussian; Algorithm; Real-time computing; Covariance matrix; Radar; Engineering; Mathematics; Statistics","score_opus":0.011620507397249034,"score_gpt":0.22908442824816264,"score_spread":0.2174639208509136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585861133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14811684,0.000058984435,0.8488172,0.0021869608,0.00010874015,0.000113813476,9.0366825e-7,0.00017576592,0.00042077573],"genre_scores_gemma":[0.9915317,0.00002823541,0.008107128,0.00014675542,0.00004537789,0.000010480953,0.000001210987,0.000008392473,0.00012071563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873906,0.00003417923,0.00025633138,0.0004749866,0.0001944981,0.00030095587],"domain_scores_gemma":[0.9994543,0.000039947274,0.00006348079,0.0002744126,0.000055127955,0.00011269707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019990912,0.00015326243,0.00015262222,0.00014184599,0.00008508071,0.00012493656,0.00035490774,0.00014774797,0.000029212159],"category_scores_gemma":[0.000020612071,0.00008728087,0.000020002544,0.00038728493,0.0000319396,0.0007794871,0.00006262521,0.00014007685,0.000005950729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013412935,0.00033284051,0.014483961,0.00024285096,0.000015366764,0.00006119314,0.00077176164,0.2748447,0.0012244984,0.04569105,0.00047691035,0.6617207],"study_design_scores_gemma":[0.0006827051,0.000063531195,0.0024406188,0.00033186446,0.0000021034775,0.0000064134983,0.00001418446,0.9937733,0.00020922965,0.0023219585,0.000009629875,0.00014445985],"about_ca_topic_score_codex":0.000011988637,"about_ca_topic_score_gemma":0.000023278506,"teacher_disagreement_score":0.84341484,"about_ca_system_score_codex":0.000029945588,"about_ca_system_score_gemma":0.00007783792,"threshold_uncertainty_score":0.35592103},"labels":[],"label_agreement":null},{"id":"W2595654587","doi":"10.1088/1367-2630/14/10/103013","title":"Robust online Hamiltonian learning","year":2012,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":207,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"","keywords":"Physics; Hamiltonian (control theory); Monte Carlo method; Bayesian probability; Hybrid Monte Carlo; Algorithm; Machine learning; Artificial intelligence; Markov chain Monte Carlo; Computer science; Mathematical optimization","score_opus":0.037562227503923136,"score_gpt":0.2561692132671552,"score_spread":0.21860698576323206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595654587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013256356,0.00038800712,0.98418146,0.00074589433,0.00042553624,0.000016416152,3.0360465e-7,0.000020608963,0.0009653953],"genre_scores_gemma":[0.8583314,0.0000448047,0.13964395,0.00018673475,0.0014849058,9.237189e-8,3.3973632e-7,0.000007205553,0.0003005529],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916494,0.00002721694,0.00022578791,0.00007531269,0.0002610505,0.00024571165],"domain_scores_gemma":[0.9991801,0.00003217002,0.000300909,0.00014406991,0.0001218795,0.00022087281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015760874,0.000092156064,0.00015307142,0.000039174916,0.000067887835,0.000094742005,0.0005629038,0.000031462172,0.000015403379],"category_scores_gemma":[0.000031971776,0.000073567666,0.00008103691,0.00026275867,0.000016142265,0.001195604,0.000083326406,0.00034104157,0.000025229441],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008931734,0.00036795816,0.013206601,0.000033149907,0.000050965344,0.000022149059,0.0025102417,0.003497912,0.0017615553,0.11058714,0.0044610663,0.8634923],"study_design_scores_gemma":[0.0076033277,0.00377778,0.26060614,0.0016301736,0.00031256108,0.0031738125,0.0012495094,0.08158963,0.04342018,0.2444392,0.34882185,0.0033758548],"about_ca_topic_score_codex":0.0000048293846,"about_ca_topic_score_gemma":9.0242156e-7,"teacher_disagreement_score":0.8601165,"about_ca_system_score_codex":0.000023903796,"about_ca_system_score_gemma":0.00017102389,"threshold_uncertainty_score":0.30000022},"labels":[],"label_agreement":null},{"id":"W2618421303","doi":"10.48550/arxiv.1705.09279","title":"Filtering Variational Objectives","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; European Commission","keywords":"Estimator; Variance (accounting); Upper and lower bounds; Computer science; Particle filter; Latent variable; Maximum likelihood; Filter (signal processing); Mathematics; Applied mathematics; Mathematical optimization; Statistics; Economics","score_opus":0.06642101412482661,"score_gpt":0.19533269830031194,"score_spread":0.12891168417548532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618421303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008335315,0.000036833735,0.97286046,0.00019112867,0.0006728863,0.00011674896,0.000014932478,0.00018539547,0.017586278],"genre_scores_gemma":[0.9876782,0.00007457878,0.010197073,0.000047062873,0.00011213234,9.590359e-7,0.0000073703104,0.000009942936,0.0018727332],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99848825,0.000047569916,0.0001327542,0.0009593114,0.00008874643,0.00028335003],"domain_scores_gemma":[0.9980258,0.000052612177,0.0003167641,0.0013325422,0.00015099153,0.00012132024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013749581,0.0002473715,0.00023673911,0.00015451299,0.00033916268,0.0004911329,0.0028651566,0.00020647941,0.000051891828],"category_scores_gemma":[0.00005016171,0.0002795719,0.0001382699,0.00015340895,0.00008664895,0.00070807495,0.0025992808,0.00042291515,0.000099825505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009210539,0.000056090463,0.0018101286,0.00010657065,0.000077133845,0.000249561,0.0002796299,0.022604289,0.000028947734,0.9728396,0.00019709727,0.0017417514],"study_design_scores_gemma":[0.0002621328,0.000032061067,0.016207548,0.00015462186,0.000027750539,0.000009966366,0.000016101794,0.46333304,0.00012155556,0.5187501,0.0005634203,0.00052170665],"about_ca_topic_score_codex":0.00007915318,"about_ca_topic_score_gemma":0.000015404306,"teacher_disagreement_score":0.9793428,"about_ca_system_score_codex":0.00010606492,"about_ca_system_score_gemma":0.0003834619,"threshold_uncertainty_score":0.99996567},"labels":[],"label_agreement":null},{"id":"W2619965968","doi":"10.1007/978-3-319-59050-9_18","title":"Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Surrogate model; Sampling (signal processing); Posterior probability; Gaussian process; Importance sampling; Algorithm; Monte Carlo method; Slice sampling; Identifiability; Uncertainty quantification; Mathematical optimization; Probabilistic logic; Gaussian; Bayesian probability; Statistics; Artificial intelligence; Machine learning; Mathematics","score_opus":0.04504832461782304,"score_gpt":0.2983332651976994,"score_spread":0.2532849405798764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619965968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063379453,0.00014024101,0.9908449,0.0009991917,0.0002738203,0.0010553739,0.000010536194,0.00010984243,0.00022813378],"genre_scores_gemma":[0.79751587,0.000014485673,0.20134127,0.0009101332,0.0001007169,0.00007054904,0.0000033443407,0.00003050042,0.000013133611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949496,0.000092393166,0.0006258942,0.0022908938,0.0009830296,0.001058218],"domain_scores_gemma":[0.9963466,0.00021252334,0.0004622436,0.0024261074,0.00027925216,0.0002733],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0011525677,0.0007139522,0.00077576446,0.0006200769,0.00067977386,0.0011043565,0.0058521065,0.00039166198,9.766931e-7],"category_scores_gemma":[0.00008008154,0.0005214159,0.00016596408,0.0006164937,0.0006156816,0.001027493,0.00071910676,0.0010582161,0.0000037944408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002311649,0.00002263373,0.000036357153,0.000043169966,0.000004621205,0.000012407905,0.00058186695,0.88798076,0.0003831333,0.0068044835,7.1000125e-7,0.10410672],"study_design_scores_gemma":[0.0001266196,0.0001755345,0.00009408457,0.00029922277,0.000008004041,0.00000624746,8.677423e-7,0.8678449,0.00025551143,0.13057886,0.0000052302626,0.00060490903],"about_ca_topic_score_codex":0.00033587252,"about_ca_topic_score_gemma":0.00041626216,"teacher_disagreement_score":0.7911779,"about_ca_system_score_codex":0.0004603171,"about_ca_system_score_gemma":0.0012844095,"threshold_uncertainty_score":0.9999326},"labels":[],"label_agreement":null},{"id":"W2622730215","doi":"","title":"Discovering and Exploiting Additive Structure for Bayesian Optimization","year":2017,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Bayesian probability; Bayesian optimization; Artificial intelligence; Data mining; Machine learning","score_opus":0.074198638650729,"score_gpt":0.3368367995968381,"score_spread":0.2626381609461091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622730215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068251503,0.0000072145567,0.99500865,0.0016611901,0.00038008497,0.00014022074,0.0003908032,0.000025101357,0.0017042451],"genre_scores_gemma":[0.7770833,0.00011529057,0.22251877,0.00009786808,0.00008976611,0.000013376032,0.00002573492,0.0000062357462,0.000049692982],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989836,0.00001644947,0.00024592175,0.00038247544,0.00019006542,0.00018147944],"domain_scores_gemma":[0.99900454,0.00018684274,0.00021783682,0.000228529,0.00027840535,0.00008382262],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00010994665,0.0001489277,0.00013366062,0.00007199556,0.00051897473,0.0016444223,0.00056234753,0.000053731692,0.000065711436],"category_scores_gemma":[0.00053840614,0.0001393455,0.000017403408,0.000029235962,0.00016491806,0.00068067235,0.00016800479,0.00010531138,0.0000026173093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002033594,0.000011260704,0.000045166904,0.000009853128,0.000009537784,0.0000042722204,0.0003255765,0.00038104912,0.00011191536,0.75006866,0.000019650843,0.24899274],"study_design_scores_gemma":[0.000032271917,0.00008702507,0.00021506434,0.000058543854,0.000004043278,0.0000069729213,0.00023590121,0.64188623,0.0014977809,0.35578427,0.00005530527,0.00013656935],"about_ca_topic_score_codex":0.000029800776,"about_ca_topic_score_gemma":0.000075915515,"teacher_disagreement_score":0.77640074,"about_ca_system_score_codex":0.000020021524,"about_ca_system_score_gemma":0.00006416276,"threshold_uncertainty_score":0.999392},"labels":[],"label_agreement":null},{"id":"W2625071797","doi":"","title":"Adjusting for Selection Bias Using Gaussian Process Models","year":2014,"lang":"en","type":"article","venue":"TSpace (University of Toronto)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaussian process; Selection (genetic algorithm); Process (computing); Computer science; Gaussian; Econometrics; Artificial intelligence; Statistics; Machine learning; Data mining; Mathematics; Physics","score_opus":0.04407215040584895,"score_gpt":0.2635499700571142,"score_spread":0.21947781965126528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625071797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028260676,0.000043743854,0.9567669,0.00031779357,0.00007833387,0.00013191695,0.0000013667077,0.00009670645,0.0143025415],"genre_scores_gemma":[0.84362036,0.000007832971,0.15604132,0.000018953633,0.00003158955,2.674064e-7,6.654293e-7,0.000005687316,0.00027333264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918103,0.000029453591,0.00008080864,0.0003031756,0.00016180248,0.00024374349],"domain_scores_gemma":[0.9993099,0.00004093872,0.0001772117,0.00020419729,0.00018254554,0.000085225736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001845593,0.00011028145,0.00017398552,0.00003966407,0.00028324756,0.000043289874,0.0005699556,0.000071661365,0.00012668515],"category_scores_gemma":[0.000023965638,0.00012885506,0.00006597151,0.0001348526,0.00003934593,0.0015783822,0.000096089745,0.000054682292,0.000001895181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015136489,0.00020816985,0.00221823,0.0012910593,0.000111355526,0.00000515696,0.035415344,0.026751267,0.004103029,0.39835194,0.0003804937,0.5310126],"study_design_scores_gemma":[0.0003520579,0.00012641188,0.0052445116,0.000066607536,0.000022206425,0.0000061468345,0.0011445082,0.9889073,0.0002988484,0.0035693215,0.00008376345,0.0001783223],"about_ca_topic_score_codex":0.009351693,"about_ca_topic_score_gemma":0.003832304,"teacher_disagreement_score":0.962156,"about_ca_system_score_codex":0.000100681544,"about_ca_system_score_gemma":0.000107981636,"threshold_uncertainty_score":0.99724513},"labels":[],"label_agreement":null},{"id":"W2733020375","doi":"10.1109/icra.2017.7989358","title":"Coupling conditionally independent submaps for large-scale 2.5D mapping with Gaussian Markov Random Fields","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Melbourne Water; Monash University; University of Technology Sydney; City West Water; Sydney Water Corporation; Hunter Water Corporation; South East Water","keywords":"Computer science; Gaussian process; Conditional independence; Covariance; Scale (ratio); Algorithm; Gaussian; Independence (probability theory); Markov process; Spatial analysis; Markov random field; Random field; Data mining; Artificial intelligence; Mathematics; Image (mathematics); Image segmentation; Statistics","score_opus":0.017497604957535506,"score_gpt":0.24766837224458993,"score_spread":0.23017076728705443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733020375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007366057,0.000027303737,0.9760939,0.0036977027,0.00023537825,0.00035266732,0.000015201866,0.00011006347,0.012101733],"genre_scores_gemma":[0.89103,0.000009156499,0.10661979,0.0004252581,0.00011205716,0.00008090482,0.000011058779,0.00001168759,0.0017000942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984302,0.0000092163855,0.00025819842,0.00050745497,0.00032623226,0.00046870313],"domain_scores_gemma":[0.99856734,0.000101318765,0.00023549874,0.0007815022,0.00016844677,0.00014588921],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00040977824,0.00019397342,0.0002504807,0.000078285324,0.0009985836,0.0010470182,0.0013471724,0.00010782104,0.000115507726],"category_scores_gemma":[0.000045378838,0.00014609883,0.000083408144,0.000077510755,0.000051790583,0.00092291797,0.00025680126,0.00016653973,0.00002170774],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004523751,0.0005039957,0.044246342,0.0006839332,0.00030038413,0.00017719659,0.0024763125,0.0006343042,0.0006725884,0.92486167,0.009391534,0.015599382],"study_design_scores_gemma":[0.019758254,0.0006502488,0.14430226,0.0007346045,0.00006693651,0.00021888872,0.00079894956,0.73450845,0.004936594,0.07997066,0.01197667,0.0020774626],"about_ca_topic_score_codex":0.000024030845,"about_ca_topic_score_gemma":0.0002503897,"teacher_disagreement_score":0.88366395,"about_ca_system_score_codex":0.000024603996,"about_ca_system_score_gemma":0.00017343833,"threshold_uncertainty_score":0.99999},"labels":[],"label_agreement":null},{"id":"W2740764198","doi":"","title":"Adapting Kernel Representations Online Using Submodular Maximization","year":2017,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Submodular set function; Computer science; Kernel (algebra); Maximization; Artificial intelligence; Mathematical optimization; Mathematics; Combinatorics","score_opus":0.08188702752310942,"score_gpt":0.35447475725112726,"score_spread":0.2725877297280178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740764198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045255475,0.000021087046,0.92788446,0.005787937,0.00059815816,0.000105834515,0.000013295443,0.00017632295,0.0201574],"genre_scores_gemma":[0.94981503,0.00004118955,0.048767086,0.00014802424,0.00015924362,0.0000053089416,0.000043202806,0.000014439794,0.0010064644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984256,0.000071257135,0.00029432838,0.00050432637,0.00046480633,0.00023966863],"domain_scores_gemma":[0.9984419,0.000047261103,0.00048154945,0.0005940154,0.00034771813,0.0000875604],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00022653994,0.00018256456,0.00015533397,0.00016915757,0.0008118201,0.001349424,0.0018104936,0.000063960746,0.00020111694],"category_scores_gemma":[0.00059843814,0.00017901012,0.00006609837,0.00009332204,0.00006397936,0.0011546845,0.00045116665,0.0004259051,0.000046292284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044363394,0.00018620535,0.059770767,0.00002176141,0.00007911052,0.000086563974,0.0006416642,0.030915339,0.004433374,0.8214109,0.000031198357,0.08237877],"study_design_scores_gemma":[0.000312516,0.000046131532,0.02268019,0.00010187118,0.0000055038377,0.000026045558,0.00005791793,0.96492875,0.00026841107,0.010908083,0.00046096955,0.00020361601],"about_ca_topic_score_codex":0.00041959752,"about_ca_topic_score_gemma":0.000058301226,"teacher_disagreement_score":0.9340134,"about_ca_system_score_codex":0.00006216693,"about_ca_system_score_gemma":0.00010784496,"threshold_uncertainty_score":0.99968725},"labels":[],"label_agreement":null},{"id":"W2748326678","doi":"10.4230/lipics.tqc.2013.106","title":"Robust Online Hamiltonian Learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Canada Excellence Research Chairs, Government of Canada","keywords":"Hamiltonian (control theory); Computer science; Qubit; Quantum decoherence; Algorithm; Ising model; Quantum; Artificial intelligence; Statistical physics; Physics; Mathematical optimization; Mathematics; Quantum mechanics","score_opus":0.035256437876873804,"score_gpt":0.24073344691068713,"score_spread":0.20547700903381333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748326678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00777379,0.00013510478,0.9581256,0.0010979401,0.00020594725,0.000032387812,2.9438303e-7,0.00027036702,0.032358564],"genre_scores_gemma":[0.8190192,0.000011130799,0.1775755,0.0003938327,0.000107042026,0.0000017528224,0.0000013048809,0.000004469337,0.002885758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992355,0.000019557478,0.000110922156,0.00015872356,0.00013325179,0.0003420508],"domain_scores_gemma":[0.9995373,0.000022322218,0.00003759585,0.00023005539,0.000033493947,0.0001392584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012326513,0.00008535024,0.000079437836,0.00004652628,0.00010217325,0.000108887754,0.00047135993,0.0000363205,0.00014208675],"category_scores_gemma":[0.000025958097,0.00006682233,0.000028287115,0.0002544122,0.000017209148,0.0007574981,0.0001690017,0.00014097808,0.00026185618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017509833,0.00029280022,0.06495191,0.000034092325,0.000014780408,0.000007423634,0.0012336392,0.00030898588,0.0004335562,0.72577304,0.0023371477,0.20461085],"study_design_scores_gemma":[0.00093861856,0.0003533945,0.4766036,0.00010312219,0.000019636,0.0002690088,0.0005436621,0.22530405,0.004221559,0.009797212,0.28015563,0.0016904975],"about_ca_topic_score_codex":0.00001532901,"about_ca_topic_score_gemma":0.0000073416736,"teacher_disagreement_score":0.81124544,"about_ca_system_score_codex":0.000012675639,"about_ca_system_score_gemma":0.000029879235,"threshold_uncertainty_score":0.33657187},"labels":[],"label_agreement":null},{"id":"W2767149636","doi":"10.1016/j.ifacol.2017.08.1991","title":"Constrained Bayesian Optimization with Particle Swarms for Safe Adaptive Controller Tuning","year":2017,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian optimization; Particle swarm optimization; Controller (irrigation); Computer science; Discretization; Process (computing); Heuristic; Control theory (sociology); Adaptive control; Fine-tuning; Mathematical optimization; Bayesian probability; Field (mathematics); Control engineering; Control (management); Artificial intelligence; Algorithm; Engineering; Mathematics","score_opus":0.01999078508450606,"score_gpt":0.2552093204145486,"score_spread":0.23521853533004253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767149636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013471396,0.0000536896,0.9911204,0.004405695,0.00011263707,0.00043161184,0.000025136198,0.00012109196,0.0023825758],"genre_scores_gemma":[0.4854351,0.0000063334905,0.51393265,0.00025945264,0.000073533454,0.000034284774,0.0000049292657,0.000011124101,0.00024257782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985454,0.00002557111,0.00025075555,0.00050365145,0.00022979277,0.0004448253],"domain_scores_gemma":[0.9986058,0.00010198251,0.00029814214,0.0005741686,0.0002411689,0.00017874425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020248185,0.0002307808,0.00029646946,0.0000417813,0.0007191681,0.0005424332,0.00086847885,0.00008094947,0.00004115661],"category_scores_gemma":[0.00014059784,0.00017583188,0.00007333575,0.00011262448,0.0001890277,0.0009076429,0.000105252315,0.00012133878,0.000010867409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002129653,0.0010745364,0.007254531,0.00029613735,0.0007655484,0.00024319375,0.0065584052,0.06848817,0.010209036,0.54516363,0.00008815561,0.357729],"study_design_scores_gemma":[0.0027895623,0.00046338554,0.0009097052,0.00008309257,0.000028221357,0.000022454282,0.00018409581,0.9931297,0.000923053,0.0010387253,0.000111253205,0.00031677217],"about_ca_topic_score_codex":0.000026358954,"about_ca_topic_score_gemma":0.00003130084,"teacher_disagreement_score":0.9246415,"about_ca_system_score_codex":0.000031037627,"about_ca_system_score_gemma":0.00016553284,"threshold_uncertainty_score":0.7170216},"labels":[],"label_agreement":null},{"id":"W2770512359","doi":"10.1093/mnras/sty664","title":"Cylinders out of a top hat: counts-in-cells for projected densities","year":2018,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Science and Technology Facilities Council; Korea Astronomy and Space Science Institute; Agence Nationale de la Recherche","keywords":"Physics; Astrophysics; Astronomy","score_opus":0.012014969825773011,"score_gpt":0.2252909594108244,"score_spread":0.21327598958505137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770512359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9651161,0.000043520995,0.0312242,0.00067086914,0.0005970456,0.00034234746,0.000027346381,0.000026676293,0.0019518876],"genre_scores_gemma":[0.9644015,6.9467745e-8,0.035239078,0.000043365202,0.00006312289,0.0000066064626,5.723106e-7,0.000004462258,0.00024126493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930173,0.00001657025,0.00021010549,0.00017629402,0.00011018899,0.00018512557],"domain_scores_gemma":[0.9994378,0.00006990377,0.00014710742,0.00023521743,0.00008303598,0.000026950904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013241923,0.00008544062,0.00016274091,0.000010916121,0.00004760649,0.000030436699,0.0006185623,0.00005859065,0.000011967558],"category_scores_gemma":[0.000017123755,0.000061323924,0.00015158863,0.000072537805,0.00020484763,0.00007869257,0.00015800585,0.00007269954,0.000006430969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010873141,0.003960634,0.18120943,0.0036225314,0.0017510599,0.0000018624198,0.08036372,0.4258614,0.004855546,0.07363008,0.10001378,0.123642646],"study_design_scores_gemma":[0.0004207742,0.00014480054,0.015954599,0.000060873204,0.000017513757,6.72067e-9,0.00023891061,0.9733037,0.008118632,0.00039684869,0.0012127497,0.00013059148],"about_ca_topic_score_codex":0.000054970755,"about_ca_topic_score_gemma":0.000031831158,"teacher_disagreement_score":0.5474423,"about_ca_system_score_codex":0.000033693097,"about_ca_system_score_gemma":0.000102999635,"threshold_uncertainty_score":0.2500717},"labels":[],"label_agreement":null},{"id":"W2770668236","doi":"10.1109/ipin.2017.8115955","title":"Efficient Wi-Fi signal strength maps using sparse Gaussian process models","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hyperparameter; Computer science; Gaussian process; Parametric statistics; Grid; Algorithm; Computation; Hyperparameter optimization; Kriging; Parametric model; Gaussian; Pattern recognition (psychology); Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.04307493720552165,"score_gpt":0.2846515216329861,"score_spread":0.2415765844274645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770668236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043861333,0.000040216455,0.9238968,0.0007639245,0.00023405798,0.00017837611,0.000007441428,0.00018749204,0.030830344],"genre_scores_gemma":[0.93738335,0.0000034130992,0.062020365,0.00013730838,0.00008884078,0.000008864518,0.0000013566174,0.000014753889,0.00034175726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787235,0.000025364414,0.00031298003,0.00069942913,0.0004963601,0.0005935415],"domain_scores_gemma":[0.9980218,0.000021578639,0.00028871326,0.0012572712,0.00015692186,0.0002537534],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00022146318,0.00027417723,0.0002541349,0.000106342464,0.00088856096,0.0013823903,0.0025354645,0.00009859742,0.000081498925],"category_scores_gemma":[0.000022563194,0.00021874999,0.0000797835,0.00018303072,0.00014900061,0.00093226146,0.00050205115,0.00019544005,0.000060333845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026481932,0.0005880496,0.0012818844,0.00025981214,0.000055941306,0.00018781815,0.001934985,0.07696131,0.0007291484,0.86387444,0.00052786706,0.05357229],"study_design_scores_gemma":[0.0003084128,0.000050096955,0.0003772194,0.00009116123,0.00000937251,0.000036472044,0.000055290282,0.95279145,0.0025081064,0.0433452,0.00007362204,0.0003536104],"about_ca_topic_score_codex":0.000092142065,"about_ca_topic_score_gemma":0.000019770345,"teacher_disagreement_score":0.893522,"about_ca_system_score_codex":0.000042950196,"about_ca_system_score_gemma":0.00031454203,"threshold_uncertainty_score":0.9996543},"labels":[],"label_agreement":null},{"id":"W2790642433","doi":"10.1109/globalsip.2017.8309099","title":"Multi-modal fetal ECG extraction using multi-kernel Gaussian processes","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Gaussian process; Gaussian function; Kernel (algebra); Computer science; Parametric statistics; Pattern recognition (psychology); Gaussian; Artificial intelligence; Fetal monitoring; Modal; Mathematics; Fetus; Statistics; Physics; Pregnancy","score_opus":0.06308147519137552,"score_gpt":0.33634997478697126,"score_spread":0.27326849959559574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790642433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012004887,0.00007548958,0.984083,0.000648178,0.0004950359,0.00017750698,0.000002922821,0.00020628163,0.0023066888],"genre_scores_gemma":[0.60133266,0.000015152021,0.39751378,0.00007416964,0.000056789795,0.000007738098,8.9235914e-7,0.00001143781,0.0009873812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982048,0.000023518789,0.0003014053,0.0006797264,0.00030498227,0.00048554744],"domain_scores_gemma":[0.9981581,0.000028611072,0.00035001375,0.0010563463,0.00021219935,0.00019472824],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00017590231,0.00026762715,0.00022522459,0.00010116374,0.0010735256,0.0016465647,0.0018021476,0.00013083218,0.000058258927],"category_scores_gemma":[0.00019395853,0.00022130681,0.00007353552,0.00017480814,0.00012813025,0.0033479244,0.00042326827,0.00021561455,0.00012926971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001354933,0.005379347,0.23961048,0.0027906974,0.0003714147,0.0011055153,0.005591657,0.00095280167,0.15302098,0.0487749,0.00083014363,0.54143655],"study_design_scores_gemma":[0.0009871859,0.000061770545,0.07330822,0.00011885688,0.00001646328,0.00017706616,0.000085380314,0.9072924,0.015841132,0.0009776545,0.0005368386,0.0005969946],"about_ca_topic_score_codex":0.00041459897,"about_ca_topic_score_gemma":0.00030667204,"teacher_disagreement_score":0.90633965,"about_ca_system_score_codex":0.000052648906,"about_ca_system_score_gemma":0.00034708364,"threshold_uncertainty_score":0.9993898},"labels":[],"label_agreement":null},{"id":"W2793406240","doi":"10.1002/sam.11371","title":"Informative priors in Bayesian inference and computation","year":2018,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Prior probability; Inference; Computer science; Bayesian inference; Prior information; Bayesian probability; Machine learning; Artificial intelligence; Computation; Algorithm","score_opus":0.052607974915139445,"score_gpt":0.37107473242317524,"score_spread":0.31846675750803577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793406240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018185973,0.000043023578,0.98022145,0.0010289487,0.00006391743,0.00004906713,0.00020287387,0.000010928268,0.00019380491],"genre_scores_gemma":[0.72022116,0.000084311956,0.27943537,0.00016442135,0.000033796838,4.9713253e-7,0.000056727866,0.0000016680376,0.0000020666605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782217,0.000086622385,0.00047248992,0.0006526428,0.0005861868,0.00037991512],"domain_scores_gemma":[0.9976391,0.00047647388,0.0002565113,0.0012022203,0.00018620242,0.00023945062],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0037316724,0.00013799744,0.0002420517,0.0004245253,0.0008492647,0.00229678,0.004185725,0.000028177046,0.000022264032],"category_scores_gemma":[0.0013314049,0.000085280124,0.000010238142,0.0026648103,0.001415993,0.006119389,0.003930528,0.00025423858,0.0000049148],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026041533,0.00007893035,0.09578594,0.000027648519,0.00015804377,0.000048670223,0.007995263,0.000041766714,0.00004298741,0.08829005,0.00146281,0.80604184],"study_design_scores_gemma":[0.00012043601,0.00007440014,0.21002942,0.000027028065,0.000063143,0.000064883585,0.0006361881,0.7813615,0.000004389785,0.0073941136,0.00010323928,0.00012121939],"about_ca_topic_score_codex":0.000114793336,"about_ca_topic_score_gemma":0.00032478638,"teacher_disagreement_score":0.8059206,"about_ca_system_score_codex":0.000019838537,"about_ca_system_score_gemma":0.00036581708,"threshold_uncertainty_score":0.99873894},"labels":[],"label_agreement":null},{"id":"W2793458543","doi":"10.1002/cjs.11570","title":"Weighted Bayesian bootstrap for scalable posterior distributions","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Scalability; Posterior probability; Sampling (signal processing); Uncertainty quantification; Bayesian inference; Statistical learning; Bayesian statistics","score_opus":0.02668371465638351,"score_gpt":0.23966291750359572,"score_spread":0.21297920284721222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793458543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035566234,0.00013552219,0.989625,0.008297432,0.00026766417,0.000091350754,0.0010049951,0.00001001686,0.00021239473],"genre_scores_gemma":[0.6252163,0.0000074089835,0.3737725,0.0007993112,0.00013575617,0.0000020953096,0.000015415046,0.00000905113,0.000042140025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99899143,0.00001777434,0.00037283613,0.00014519149,0.00013289446,0.0003398975],"domain_scores_gemma":[0.99804544,0.00007412675,0.00021368921,0.00013470148,0.0004450767,0.0010869899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009624854,0.00011257647,0.00019553879,0.000088119305,0.00020158652,0.00031031016,0.0006773148,0.00004839989,0.000080340775],"category_scores_gemma":[0.00020016023,0.00010515378,0.000056008408,0.0002942513,0.00006732246,0.0003009052,0.000017579747,0.00015202879,0.000010775204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002548635,0.000030435069,0.002270907,0.00022677827,0.000091639406,0.00068503525,0.001390555,0.000031022653,0.00028111422,0.7712667,0.1257913,0.097909],"study_design_scores_gemma":[0.0037162823,0.0038735468,0.016900357,0.00045125722,0.0002520522,0.0015041761,0.00037282173,0.19078273,0.0034400062,0.38003635,0.39693773,0.0017326957],"about_ca_topic_score_codex":0.0001564629,"about_ca_topic_score_gemma":0.00085024355,"teacher_disagreement_score":0.62486064,"about_ca_system_score_codex":0.000073834315,"about_ca_system_score_gemma":0.0022520255,"threshold_uncertainty_score":0.42880467},"labels":[],"label_agreement":null},{"id":"W2796253126","doi":"10.1007/978-3-319-89656-4_18","title":"Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Smoothness; Computer science; Mathematical optimization; Constraint (computer-aided design); Bayesian optimization; Gaussian process; Bayesian probability; Gaussian; Optimization problem; Constrained optimization; Process (computing); Algorithm; Artificial intelligence; Mathematics","score_opus":0.013107237312951025,"score_gpt":0.2267050679848338,"score_spread":0.2135978306718828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796253126","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000055686664,0.000102191465,0.99125963,0.00067368394,0.0006835573,0.0012103633,0.000025525465,0.00022847355,0.005810975],"genre_scores_gemma":[0.07686686,0.0000220134,0.92148924,0.0009352269,0.00035013296,0.000050468738,0.000017998527,0.000052677497,0.00021536527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99551344,0.000022882603,0.0006400147,0.0019766572,0.0009233781,0.00092361704],"domain_scores_gemma":[0.9968291,0.00030688115,0.0005271686,0.0012176101,0.0008324104,0.00028686045],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00070689985,0.00072562206,0.00065236277,0.0006851537,0.00039858618,0.0010459822,0.0029445393,0.00042373562,0.00017179357],"category_scores_gemma":[0.000086870656,0.0005905748,0.00011739792,0.0007152549,0.002739517,0.0008493775,0.00049261993,0.000537731,0.000017503018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041162246,0.000099476274,0.00005772353,0.00044777914,0.000051494222,0.00008844386,0.00174547,0.1333849,0.000052181247,0.10986785,0.00009694228,0.7540666],"study_design_scores_gemma":[0.0007630732,0.00079528283,0.000014362985,0.0010297252,0.000018943003,0.00014648354,6.057132e-7,0.857844,0.00033218626,0.13739134,0.00071968185,0.0009443086],"about_ca_topic_score_codex":0.0000058381715,"about_ca_topic_score_gemma":0.00004346256,"teacher_disagreement_score":0.75312227,"about_ca_system_score_codex":0.00019146728,"about_ca_system_score_gemma":0.0017753828,"threshold_uncertainty_score":0.999991},"labels":[],"label_agreement":null},{"id":"W2802162151","doi":"10.1049/iet-ipr.2018.0043","title":"Entropy‐based variational Bayes learning framework for data clustering","year":2018,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Taif University; National Natural Science Foundation of China; National Science Foundation","keywords":"Cluster analysis; Bayes' theorem; Computer science; Artificial intelligence; Entropy (arrow of time); Naive Bayes classifier; Machine learning; Pattern recognition (psychology); Bayesian probability; Physics","score_opus":0.035700315458359444,"score_gpt":0.317467405218294,"score_spread":0.28176708975993453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802162151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011129435,0.00012487588,0.9968644,0.0016964944,0.00025562916,0.00014128238,0.000009189467,0.00028028374,0.00051659916],"genre_scores_gemma":[0.33921644,0.0000019768543,0.6598915,0.00040137803,0.00039183823,0.000016589998,0.000021796832,0.000015500274,0.000043025677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824727,0.000034787437,0.00028256507,0.00070123415,0.0002967629,0.00043736],"domain_scores_gemma":[0.9985177,0.0001949071,0.00022841123,0.000612497,0.00035132654,0.000095144875],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00043290795,0.00018602615,0.00017187436,0.00009326379,0.0006377703,0.0013162263,0.0016932712,0.00008854067,0.0000516379],"category_scores_gemma":[0.00059250795,0.00017545653,0.000036974685,0.0004194387,0.000103547805,0.0020101694,0.0005194238,0.00022415453,0.00003742891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115101626,0.0002595326,0.0021357583,0.0014238685,0.000055768567,0.000023273275,0.0026702895,0.00038873052,0.008256355,0.103931695,0.0018355737,0.87890404],"study_design_scores_gemma":[0.00026239306,0.00008677567,0.0002703433,0.00023267086,0.000010563822,0.0000097103,0.000024691162,0.9487464,0.0017009573,0.04541395,0.0029972275,0.00024431074],"about_ca_topic_score_codex":0.000004957799,"about_ca_topic_score_gemma":0.0000028021404,"teacher_disagreement_score":0.9483577,"about_ca_system_score_codex":0.000029571187,"about_ca_system_score_gemma":0.00036412507,"threshold_uncertainty_score":0.9997205},"labels":[],"label_agreement":null},{"id":"W2803376244","doi":"10.48550/arxiv.1807.02125","title":"Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kernel (algebra); Tensor product; Marginal likelihood; Cartesian product; Gaussian process; Grid; Computer science; Algorithm; Mathematics; Theoretical computer science; Mathematical optimization; Bayesian probability; Gaussian; Artificial intelligence; Discrete mathematics; Pure mathematics","score_opus":0.030027760226950525,"score_gpt":0.16753997703500828,"score_spread":0.13751221680805775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803376244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07214019,0.000031786178,0.9078464,0.00040635743,0.00033297774,0.00016986496,0.000008461073,0.00038174432,0.018682212],"genre_scores_gemma":[0.98971087,0.000030067613,0.0073577035,0.00021761068,0.00017426776,0.0000011785099,0.000003826577,0.000014420484,0.002490041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99843633,0.000033446704,0.00013826977,0.00080239377,0.00011716205,0.00047239495],"domain_scores_gemma":[0.9982091,0.00004810551,0.00013040144,0.0009883264,0.0003918352,0.00023223182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007445148,0.00024178813,0.00018856485,0.000170216,0.00053411635,0.0002186465,0.0015355357,0.00009805485,0.00019307298],"category_scores_gemma":[0.00005469868,0.00020585005,0.000046695008,0.0023249602,0.0003079735,0.0012538165,0.00030693243,0.00016385081,0.0002455079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020064662,0.0003090808,0.03583742,0.00032922972,0.00018465485,0.00046927703,0.0014243254,0.0025874483,0.00038160756,0.9484203,0.0069077075,0.0029483435],"study_design_scores_gemma":[0.008120092,0.005714963,0.09150187,0.0010248366,0.00054150296,0.000841916,0.0016658783,0.19504328,0.03542136,0.5598905,0.09391333,0.006320445],"about_ca_topic_score_codex":0.000062386105,"about_ca_topic_score_gemma":0.00040219977,"teacher_disagreement_score":0.9175707,"about_ca_system_score_codex":0.00006376264,"about_ca_system_score_gemma":0.00052720105,"threshold_uncertainty_score":0.8394321},"labels":[],"label_agreement":null},{"id":"W2810483590","doi":"10.48550/arxiv.1806.10234","title":"Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Army Research Office; Office of Naval Research; Engineering and Physical Sciences Research Council; National Science Foundation","keywords":"Pointwise; Divergence (linguistics); Gaussian process; Mathematics; Prior probability; Inference; Nonparametric statistics; Applied mathematics; Algorithm; Computer science; Gaussian; Mathematical optimization; Bayesian probability; Statistics; Artificial intelligence","score_opus":0.06587888656795729,"score_gpt":0.21321336665386456,"score_spread":0.14733448008590727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810483590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052107327,0.00013047073,0.9430486,0.00027143184,0.00021072282,0.00033195692,0.000068405876,0.000303421,0.003527666],"genre_scores_gemma":[0.98828834,0.00029791103,0.010469711,0.0001331627,0.00008934262,0.0000022573115,0.0000330199,0.000026059619,0.0006601951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996475,0.000100993035,0.00026903482,0.0023609116,0.0001976282,0.000596458],"domain_scores_gemma":[0.9958329,0.00016562315,0.00040711107,0.00294207,0.00037340407,0.00027890055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033492155,0.0005351722,0.0004990611,0.00023914539,0.000327272,0.0006395305,0.0051858784,0.00034683192,0.00005004356],"category_scores_gemma":[0.00009569835,0.00048973586,0.000048184764,0.0009914886,0.00047703646,0.0015931524,0.004075547,0.0007182234,0.000067484165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005700208,0.0010866461,0.07715285,0.005402968,0.000943506,0.003098366,0.0066571156,0.07211682,0.000034519824,0.81957465,0.0016637893,0.011698747],"study_design_scores_gemma":[0.00072338217,0.00025687713,0.002877193,0.0010408874,0.00011623036,0.000033724795,0.00013857786,0.87287277,0.00011515875,0.120276734,0.00039908473,0.0011493949],"about_ca_topic_score_codex":0.00013867859,"about_ca_topic_score_gemma":0.0002413986,"teacher_disagreement_score":0.936181,"about_ca_system_score_codex":0.000057122525,"about_ca_system_score_gemma":0.000773206,"threshold_uncertainty_score":0.99975544},"labels":[],"label_agreement":null},{"id":"W2810504233","doi":"10.1007/s42484-019-00004-7","title":"Bayesian deep learning on a quantum computer","year":2019,"lang":"en","type":"article","venue":"Quantum Machine Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Creative Destruction Lab; University of Toronto; Perimeter Institute","funders":"Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Generalitat de Catalunya; Ministry of Education - Singapore; National Research Foundation Singapore; Ministère du Développement Économique, de l’Innovation et de l’Exportation; “la Caixa” Foundation; Fundación Cellex","keywords":"Gaussian process; Deep learning; Quantum computer; Robustness (evolution); Leverage (statistics); Bayesian optimization; Gaussian; Bayesian probability","score_opus":0.010387022624984123,"score_gpt":0.24877034322287925,"score_spread":0.23838332059789513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810504233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008196214,0.00021864312,0.9842509,0.0011635442,0.0011586401,0.00026992802,0.000001788883,0.00045943414,0.0042809215],"genre_scores_gemma":[0.984744,0.00008275206,0.013634438,0.00090365775,0.00012792952,0.000014953887,0.0000058060223,0.00003328605,0.00045318043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969789,0.00014536946,0.000544737,0.0010315332,0.0005817675,0.0007176901],"domain_scores_gemma":[0.9980789,0.00029962425,0.00024121569,0.0010150045,0.00012741194,0.00023788247],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040295455,0.00042753638,0.0004067043,0.00026439354,0.0002229579,0.000438033,0.0020861526,0.00013306744,0.0004199018],"category_scores_gemma":[0.00006395065,0.0003631017,0.00016271583,0.0007866232,0.000074692456,0.0005866719,0.00042685124,0.00076334,0.0036384265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002555528,0.00014428863,0.003055142,0.00007978865,0.000022516546,0.00005680851,0.0007083754,0.007510319,0.00011864847,0.77738804,0.00012981863,0.21076073],"study_design_scores_gemma":[0.0001094057,0.00087680534,0.00094987836,0.00010805758,0.0000040192667,0.000060658345,0.00003893902,0.95017725,0.0009411773,0.04218897,0.0040503326,0.00049452786],"about_ca_topic_score_codex":0.000084674924,"about_ca_topic_score_gemma":0.000012613748,"teacher_disagreement_score":0.9765478,"about_ca_system_score_codex":0.00006004561,"about_ca_system_score_gemma":0.00007962018,"threshold_uncertainty_score":0.9998821},"labels":[],"label_agreement":null},{"id":"W2884515556","doi":"10.48550/arxiv.1807.07706","title":"Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Lawrence Berkeley National Laboratory; Multidisciplinary University Research Initiative; Engineering and Physical Sciences Research Council; Defense Advanced Research Projects Agency; National Energy Research Scientific Computing Center; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Inference; Computer science; Probabilistic logic; Markov chain Monte Carlo; Approximate inference; Theoretical computer science; Machine learning; Artificial intelligence; Bayesian probability","score_opus":0.06835931264488022,"score_gpt":0.21732084316492217,"score_spread":0.14896153052004196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884515556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043500055,0.000023481867,0.9529603,0.0007640107,0.00023675675,0.0008728797,0.000053292708,0.00007253751,0.0015166905],"genre_scores_gemma":[0.9952238,0.0000206176,0.004245428,0.00027626357,0.00008224216,0.000015222523,0.0000068487266,0.000012094417,0.000117467964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980709,0.00012454356,0.0002292753,0.00097067194,0.00017465357,0.00042996442],"domain_scores_gemma":[0.9973768,0.00044453464,0.00025240565,0.0015210122,0.00033687564,0.00006834534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063712883,0.0003164401,0.00028050726,0.0000777417,0.00030343526,0.00035358788,0.0038548696,0.00016817731,0.0000033737952],"category_scores_gemma":[0.0001383886,0.0002125286,0.00015045925,0.00063397165,0.00032310115,0.00013976757,0.0012743699,0.000531279,0.0000126225905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014654388,0.000053160587,0.000120615885,0.00007648753,0.000008334262,0.000007781193,0.0007035308,0.4548344,8.4580046e-7,0.543756,0.000117648975,0.0003065287],"study_design_scores_gemma":[0.00015553444,0.000053912885,0.000093530594,0.0000493921,0.000021107779,7.011358e-7,0.000038528015,0.5888162,0.00001140977,0.41052076,0.00006584899,0.00017306859],"about_ca_topic_score_codex":0.000030072413,"about_ca_topic_score_gemma":0.00007782442,"teacher_disagreement_score":0.95172375,"about_ca_system_score_codex":0.0001628253,"about_ca_system_score_gemma":0.0007171223,"threshold_uncertainty_score":0.86666644},"labels":[],"label_agreement":null},{"id":"W2890985400","doi":"10.1214/18-aoas1138","title":"A frequency-calibrated Bayesian search for new particles","year":2018,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"False discovery rate; Computer science; Inference; Bayesian probability; Bayesian inference; Statistical inference; Data mining; Bayesian hierarchical modeling; Nuisance parameter; Function (biology); Machine learning; Artificial intelligence; Statistics; Mathematics","score_opus":0.08347519627341762,"score_gpt":0.33547479827747323,"score_spread":0.2519996020040556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890985400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015605785,0.000026196885,0.9928924,0.0028252366,0.0000561592,0.00024529983,0.0000703577,0.00005499309,0.0022687782],"genre_scores_gemma":[0.64211184,0.000015183596,0.35709462,0.0005891349,0.00009207091,0.000012169558,0.0000041178396,0.000009515906,0.00007133642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998852,0.00002136451,0.0002817403,0.00024227533,0.00023493903,0.0003676676],"domain_scores_gemma":[0.9987335,0.00023894304,0.00012051933,0.000504339,0.00027345202,0.00012923454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003210875,0.00012311542,0.00016993086,0.00004341239,0.00016893941,0.0001323567,0.0009951331,0.000042995296,0.00005081603],"category_scores_gemma":[0.000039577804,0.00008668608,0.000030239698,0.00037630045,0.00022464448,0.000115605406,0.000124771,0.00007758745,0.000039324343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002834492,0.000027769698,0.000027795226,0.00004054858,0.000020558253,8.5180955e-7,0.00099832,0.000011793888,0.0024452507,0.9373116,0.011564365,0.0475228],"study_design_scores_gemma":[0.00021726251,0.00022009229,0.00057001685,0.000016502821,0.0000085057645,0.0000016822463,0.000053592586,0.037546948,0.11525399,0.8452438,0.0007274527,0.0001401612],"about_ca_topic_score_codex":0.000049319075,"about_ca_topic_score_gemma":0.000019500976,"teacher_disagreement_score":0.64055127,"about_ca_system_score_codex":0.0000043151767,"about_ca_system_score_gemma":0.00030086414,"threshold_uncertainty_score":0.35349557},"labels":[],"label_agreement":null},{"id":"W2891148009","doi":"10.48550/arxiv.1809.04279","title":"Discretely Relaxing Continuous Variables for tractable Variational Inference","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Estimator; Latent variable; Computer science; Kronecker delta; Prior probability; Algorithm; Parameterized complexity; Bayesian inference; Approximate inference; Approximate Bayesian computation; Markov chain Monte Carlo; Mathematical optimization; Bayesian probability; Mathematics; Artificial intelligence; Statistics","score_opus":0.046971772438187356,"score_gpt":0.1945821069918637,"score_spread":0.14761033455367634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891148009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013434711,0.000009733203,0.9773202,0.0001705027,0.00024536607,0.00015197405,0.0000105334775,0.00014506721,0.0085118925],"genre_scores_gemma":[0.9634842,0.000009508756,0.03467929,0.00012952538,0.0001106688,0.0000016034575,0.0000044748235,0.000008173726,0.0015725341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880654,0.00003283352,0.0001548521,0.00057297054,0.00007110248,0.00036172514],"domain_scores_gemma":[0.9987936,0.00024182396,0.00014124469,0.00041483072,0.000289558,0.00011895649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021212429,0.00015288519,0.00016646979,0.00009945331,0.00034741286,0.00018704495,0.0009270392,0.00009096161,0.00007519493],"category_scores_gemma":[0.00013010393,0.00015702547,0.0000670068,0.00060551445,0.00010550075,0.0011752164,0.00017937357,0.000101926846,0.00007066243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002094171,0.000041506242,0.0024305852,0.000015404301,0.000019944637,0.000011989299,0.00014592434,0.0005487033,0.00014753403,0.9953328,0.000272833,0.0010118659],"study_design_scores_gemma":[0.00080403115,0.00030720528,0.0048929313,0.000051573024,0.00003058154,0.000010210078,0.00005690244,0.619366,0.0006288481,0.368424,0.005014368,0.00041339692],"about_ca_topic_score_codex":0.00004464061,"about_ca_topic_score_gemma":0.000022240634,"teacher_disagreement_score":0.9500495,"about_ca_system_score_codex":0.00005268282,"about_ca_system_score_gemma":0.00020518004,"threshold_uncertainty_score":0.64033127},"labels":[],"label_agreement":null},{"id":"W2897326341","doi":"10.1109/ivs.2018.8500614","title":"Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment$\\star$","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Trajectory; Computer science; Kriging; Gaussian process; Cluster analysis; Process (computing); Ground-penetrating radar; Real-time computing; Vehicle dynamics; Artificial intelligence; Collision avoidance; Kinematics; Data modeling; Collision; Gaussian; Machine learning; Engineering; Radar; Telecommunications","score_opus":0.00913459223447122,"score_gpt":0.21927526571206368,"score_spread":0.21014067347759247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897326341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81401896,0.00005808869,0.17451762,0.0014583551,0.000136688,0.0003476063,0.0000041341737,0.00037967885,0.009078841],"genre_scores_gemma":[0.9911178,0.000013102286,0.008241753,0.0002347344,0.000077048055,0.00003329044,0.000003568308,0.000014446815,0.0002643086],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99827886,0.000068047,0.00026809712,0.0006096551,0.0003681851,0.00040715415],"domain_scores_gemma":[0.9991788,0.000034435932,0.000107296,0.0004791345,0.00006069613,0.00013960709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018326973,0.00020479006,0.00017711437,0.00014369782,0.00019852592,0.00013994088,0.0005899205,0.000100314835,0.00021076766],"category_scores_gemma":[0.000016621383,0.00014290343,0.000023969606,0.0005955706,0.00015404016,0.0010102306,0.00008927406,0.00022262549,0.000112216374],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015981437,0.0044815214,0.49936485,0.0010528462,0.00021870008,0.0006543713,0.038803346,0.00075028953,0.12630288,0.089354545,0.0073718224,0.23004669],"study_design_scores_gemma":[0.004486693,0.0031291228,0.5460194,0.00069332414,0.00002328637,0.000094232055,0.0009135911,0.30338195,0.12829193,0.009280401,0.0024289945,0.0012571096],"about_ca_topic_score_codex":0.000043102544,"about_ca_topic_score_gemma":0.000091112546,"teacher_disagreement_score":0.30263165,"about_ca_system_score_codex":0.00006614324,"about_ca_system_score_gemma":0.00012195294,"threshold_uncertainty_score":0.5827433},"labels":[],"label_agreement":null},{"id":"W2906066094","doi":"10.1002/env.2631","title":"Detecting British Columbia coastal rainfall patterns by clustering Gaussian processes","year":2020,"lang":"en","type":"preprint","venue":"Environmetrics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Cluster analysis; Exploratory data analysis; Covariance; Cluster (spacecraft); Extreme value theory; Generalization; Geography; Kernel (algebra); Computer science; Mathematics; Statistics; Combinatorics","score_opus":0.015295177995166848,"score_gpt":0.21064557182387936,"score_spread":0.1953503938287125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906066094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018185392,0.0010176065,0.97745645,0.0004910422,0.000628601,0.00044124763,0.0003603549,0.00046153425,0.00095775374],"genre_scores_gemma":[0.95948416,0.0010527086,0.037628576,0.0005481779,0.00026752517,0.00008323243,0.000109391396,0.000086597545,0.00073965115],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99589294,0.000087262415,0.0007082056,0.001706044,0.00082200865,0.0007835618],"domain_scores_gemma":[0.99775803,0.0001932523,0.0006107654,0.00094336004,0.000067219844,0.0004273891],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00028282826,0.00046389835,0.0006372211,0.0001692601,0.0003701596,0.0047525256,0.002843752,0.00048389274,0.00014680992],"category_scores_gemma":[0.0006414531,0.00074196974,0.00016852739,0.00131648,0.00009351207,0.00052054046,0.0051506,0.0014070251,0.0000630804],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012721056,0.0006455542,0.14085871,0.01036976,0.0003087804,0.0014843034,0.0026992508,0.0013696544,0.0005186693,0.00006441629,0.02518893,0.81647927],"study_design_scores_gemma":[0.0070495335,0.0026863273,0.36876374,0.009405997,0.0007103127,0.0023595938,0.0013255337,0.35555705,0.005653871,0.0410008,0.18181935,0.023667866],"about_ca_topic_score_codex":0.0053898594,"about_ca_topic_score_gemma":0.009839035,"teacher_disagreement_score":0.9412987,"about_ca_system_score_codex":0.00012713225,"about_ca_system_score_gemma":0.00029844762,"threshold_uncertainty_score":0.99950314},"labels":[],"label_agreement":null},{"id":"W2910221532","doi":"10.1109/iros.2018.8593882","title":"Experience-Based Model Selection to Enable Long-Term, Safe Control for Repetitive Tasks Under Changing Conditions","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Computer science; Process (computing); Controller (irrigation); Computation; Gaussian process; Control (management); Control engineering; Artificial intelligence; Selection (genetic algorithm); Control theory (sociology); Gaussian; Engineering","score_opus":0.022582700373043416,"score_gpt":0.2944127447288588,"score_spread":0.27183004435581537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910221532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007902102,0.000004405719,0.98606354,0.0017816084,0.0001615433,0.00046706953,0.000013762086,0.00018161598,0.0034243315],"genre_scores_gemma":[0.9333422,5.388185e-7,0.060358696,0.0041713105,0.000121467885,0.0003143143,0.0000054248585,0.000009946703,0.0016760783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864966,0.00001766753,0.0001959509,0.0004924321,0.00017054807,0.00047376342],"domain_scores_gemma":[0.9990323,0.00006672932,0.000070912465,0.0002953348,0.00038767688,0.00014702581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001373816,0.00015039074,0.00014889713,0.00020478363,0.00047118156,0.00024579267,0.00041118552,0.00006164792,0.00015225947],"category_scores_gemma":[0.000037200527,0.00013420632,0.000058488058,0.00055042136,0.00006747935,0.0005355203,0.00005823712,0.000056929715,0.000060295508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009412424,0.00022552855,0.0014026134,0.00007540617,0.000046266414,0.0000047834437,0.003788343,0.0127329985,0.015783165,0.9591954,0.0026416057,0.004009778],"study_design_scores_gemma":[0.000787714,0.00039767177,0.0009218969,0.0000615583,0.000010838805,0.000008888334,0.000113740214,0.93253124,0.048978817,0.015718246,0.00014886732,0.00032050515],"about_ca_topic_score_codex":0.000013573066,"about_ca_topic_score_gemma":0.00008954315,"teacher_disagreement_score":0.94347715,"about_ca_system_score_codex":0.00007506193,"about_ca_system_score_gemma":0.00020933889,"threshold_uncertainty_score":0.5472775},"labels":[],"label_agreement":null},{"id":"W2923999921","doi":"","title":"Scalable Data Augmentation for Deep Learning","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Scalability; Computer science; Backtracking; Stochastic gradient descent; Inference; Artificial intelligence; Deep learning; Machine learning; Artificial neural network; Logit; Deep neural networks; Algorithm; Database","score_opus":0.04929999962796778,"score_gpt":0.3068788782314723,"score_spread":0.25757887860350454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923999921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014337523,0.0001663853,0.99019504,0.000774939,0.00074129604,0.0004296719,0.000012027902,0.00020226138,0.00733503],"genre_scores_gemma":[0.30983067,0.00009985462,0.6843975,0.0002948689,0.00015063277,0.000072218834,0.00051455724,0.000021061322,0.0046186056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837106,0.0000261423,0.00023714265,0.0009010339,0.00020649655,0.00025810438],"domain_scores_gemma":[0.99802566,0.000088079854,0.00019303634,0.0015205351,0.00011582816,0.00005684346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031899253,0.00017753303,0.00020620447,0.0000751745,0.000101269165,0.00070008717,0.003053161,0.00014837547,0.00006661454],"category_scores_gemma":[0.00006582941,0.0001586003,0.00004633227,0.00011175234,0.000011311759,0.0006256892,0.0037822942,0.0002951397,0.00014692832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022926984,0.00015494529,0.0029030396,0.0027861025,0.00015226446,0.000005453199,0.00080210244,0.027240707,0.00014272322,0.22077873,0.016950637,0.72806036],"study_design_scores_gemma":[0.00019639434,0.000042462587,0.00032678546,0.00007479915,0.000012745868,0.0000022671388,0.000021256608,0.9653277,0.00023433892,0.025494667,0.008009741,0.00025684646],"about_ca_topic_score_codex":0.000039436658,"about_ca_topic_score_gemma":0.00001461562,"teacher_disagreement_score":0.938087,"about_ca_system_score_codex":0.000033722416,"about_ca_system_score_gemma":0.0002087027,"threshold_uncertainty_score":0.6750957},"labels":[],"label_agreement":null},{"id":"W2928368762","doi":"","title":"Gaussian Process Modeling and Supervised Dimensionality Reduction Algorithms via Stiefel Manifold Learning","year":2018,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute","funders":"","keywords":"Stiefel manifold; Dimensionality reduction; Algorithm; Nonlinear dimensionality reduction; Manifold alignment; Reduction (mathematics); Artificial intelligence; Gaussian process; Computer science; Process (computing); Curse of dimensionality; Machine learning; Mathematics; Gaussian; Pattern recognition (psychology); Physics; Pure mathematics","score_opus":0.014148178203926748,"score_gpt":0.2473327249085955,"score_spread":0.23318454670466876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928368762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3479019,0.0017638936,0.6139783,0.0010003182,0.0011331002,0.00059703935,0.000010884718,0.0004087163,0.03320581],"genre_scores_gemma":[0.9644108,0.00031629732,0.031147325,0.0000075121075,0.00007425322,7.125504e-7,0.00007908598,0.000018622502,0.00394539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983444,0.000054413398,0.00016934649,0.00071180455,0.0004275251,0.00029247065],"domain_scores_gemma":[0.9987657,0.000013223967,0.00031737523,0.00035210056,0.00039535842,0.00015624218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018817185,0.00028469865,0.00037868798,0.00007938216,0.0005012301,0.000076068165,0.0006810849,0.000392118,0.00067523564],"category_scores_gemma":[0.000012226734,0.00033956047,0.00009074918,0.00013675219,0.000057633224,0.0013389318,0.00014317784,0.00036102455,0.000009671133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008168508,0.00061397813,0.00035042225,0.005288766,0.0006195713,0.00011864985,0.25993818,0.0010586445,0.0067533157,0.022335954,0.00044190782,0.70166373],"study_design_scores_gemma":[0.0010457429,0.00052833365,0.017512262,0.00086207903,0.0002020176,0.000048196587,0.04309019,0.9320408,0.00059064507,0.002680494,0.00019155955,0.0012076868],"about_ca_topic_score_codex":0.019467436,"about_ca_topic_score_gemma":0.0037721484,"teacher_disagreement_score":0.9309822,"about_ca_system_score_codex":0.00013113726,"about_ca_system_score_gemma":0.00020623286,"threshold_uncertainty_score":0.99990565},"labels":[],"label_agreement":null},{"id":"W2937826639","doi":"","title":"Comparing statistical methods for analyzing human limb trajectories of goal-directed movements","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Trajectory; Computer science; Bayesian probability; Artificial intelligence; Action (physics); Gaussian process; Movement (music); Machine learning; Regression; Prior information; Process (computing); Cognitive psychology; Psychology; Statistics; Gaussian; Mathematics","score_opus":0.060219487224885106,"score_gpt":0.39486677199703013,"score_spread":0.334647284772145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937826639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007277359,0.000022208575,0.9873977,0.00007195493,0.00014261772,0.00012519606,0.000004514308,0.0000972891,0.004861161],"genre_scores_gemma":[0.5233857,0.0000014614077,0.47641587,0.000012176309,0.000015017512,0.000008309039,0.0000017559358,0.0000034674633,0.00015621763],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990107,0.00003857145,0.00029127748,0.00029952513,0.00011815914,0.00024177377],"domain_scores_gemma":[0.9987883,0.00017486536,0.00022547072,0.00058502785,0.00015057427,0.00007578131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035137575,0.000116729265,0.00027818631,0.00006042982,0.00044518284,0.00036561303,0.0010912558,0.000038088463,0.000024226634],"category_scores_gemma":[0.00021897953,0.00009817748,0.0000522962,0.000080549275,0.00008999771,0.0004145411,0.0002405399,0.000064374726,0.0000014756165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013786037,0.00010731663,0.038374145,0.00017460996,0.000072981165,0.0000018431551,0.00036875776,0.0000067162005,0.010837739,0.8775432,0.00022611082,0.072272785],"study_design_scores_gemma":[0.0012635449,0.00047645695,0.5024696,0.00011496025,0.00004079624,0.0000030097012,0.00005989251,0.23498975,0.08210169,0.17720523,0.00070691394,0.00056813256],"about_ca_topic_score_codex":0.00015082036,"about_ca_topic_score_gemma":0.000051956136,"teacher_disagreement_score":0.700338,"about_ca_system_score_codex":0.000015853127,"about_ca_system_score_gemma":0.000043402375,"threshold_uncertainty_score":0.40035614},"labels":[],"label_agreement":null},{"id":"W2941442828","doi":"10.48550/arxiv.1904.10939","title":"Horseshoe Regularization for Machine Learning in Complex and Deep Models","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Horseshoe (symbol); Regularization (linguistics); Computer science; Artificial intelligence; Machine learning; Gaussian; Computation; Bayesian probability; Multivariate statistics; Algorithm","score_opus":0.07327616003506653,"score_gpt":0.18966228000322474,"score_spread":0.11638611996815822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941442828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014364737,0.00010637471,0.98336554,0.00014001464,0.00009784879,0.0003665863,0.000008027167,0.00010319952,0.0014477007],"genre_scores_gemma":[0.98755974,0.00021588513,0.011268952,0.0000585153,0.00001740112,0.0000015322988,0.000049953804,0.000013394905,0.0008146023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861056,0.0000616831,0.00016396768,0.00084782596,0.000056977584,0.00025896868],"domain_scores_gemma":[0.999099,0.0000646376,0.00018647216,0.00046816637,0.00010443943,0.000077259174],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016937125,0.0002169013,0.00028006226,0.00024272886,0.00010169789,0.00015585663,0.00079183874,0.00020342557,0.00000838151],"category_scores_gemma":[0.00002282312,0.00024844464,0.0000669466,0.00034321178,0.00004727738,0.0004708163,0.000987699,0.00035716195,0.0000053554254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017680755,0.000028516497,0.0033363474,0.00020293819,0.000014660475,0.000017965085,0.00022630757,0.54876196,0.000015105158,0.44546723,0.000013405422,0.0018978979],"study_design_scores_gemma":[0.00035911842,0.00003952111,0.0011324178,0.00005391381,0.00001253753,0.000002183508,0.0000185032,0.7899679,0.000010677338,0.20806311,0.00011431459,0.00022580771],"about_ca_topic_score_codex":0.00006307529,"about_ca_topic_score_gemma":0.000063126914,"teacher_disagreement_score":0.973195,"about_ca_system_score_codex":0.00007551331,"about_ca_system_score_gemma":0.0001109078,"threshold_uncertainty_score":0.9999968},"labels":[],"label_agreement":null},{"id":"W2943666166","doi":"10.48550/arxiv.1906.03772","title":"Multimodal Data Fusion of Non-Gaussian Spatial Fields in Sensor Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua","funders":"","keywords":"Sensor fusion; Gaussian; Metric (unit); Marginal distribution; Spatial correlation; Pearson product-moment correlation coefficient; Gaussian process; Computer science; Correlation; Algorithm; Copula (linguistics); Mathematics; Data mining; Artificial intelligence; Statistics; Econometrics; Random variable; Physics","score_opus":0.05075697810459639,"score_gpt":0.2040747149134575,"score_spread":0.1533177368088611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943666166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07404481,0.000029855664,0.9233719,0.00011668,0.0006862682,0.00029187324,0.00003672353,0.0000518922,0.0013699784],"genre_scores_gemma":[0.9925368,0.00017425523,0.006767304,0.000053916094,0.00008310674,4.0018043e-7,0.00006684003,0.000013067822,0.00030433896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977747,0.000088224144,0.00032405643,0.0013196251,0.0001108531,0.00038252177],"domain_scores_gemma":[0.99666643,0.000092616385,0.00036483753,0.002662223,0.000097878605,0.000116015566],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024616305,0.00030608685,0.0004629864,0.00026458377,0.00005471292,0.00008483202,0.0039829058,0.0004947351,0.000038658123],"category_scores_gemma":[0.000027536897,0.0003262974,0.000103144084,0.0005262171,0.00007867458,0.0004476087,0.005801574,0.00078601355,0.000029699382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099407545,0.00030537072,0.045646194,0.0005073119,0.00007169496,0.00054026145,0.0004681468,0.9285378,0.000031172036,0.011917865,0.00046447234,0.0114103425],"study_design_scores_gemma":[0.0005023718,0.000057507226,0.021732638,0.00026801205,0.000020998605,0.0000027928222,0.000029861478,0.97480845,0.000041371906,0.0021429753,0.000043918026,0.00034909695],"about_ca_topic_score_codex":0.001906859,"about_ca_topic_score_gemma":0.0007000661,"teacher_disagreement_score":0.91849196,"about_ca_system_score_codex":0.0000659307,"about_ca_system_score_gemma":0.0003140787,"threshold_uncertainty_score":0.99991894},"labels":[],"label_agreement":null},{"id":"W2948340529","doi":"10.48550/arxiv.1906.03329","title":"Sparse Variational Inference: Bayesian Coresets from Scratch","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Inference; Scalability; Automatic summarization; Bayesian inference; Bayesian probability; Approximate inference; Machine learning; Algorithm; Artificial intelligence; Mathematical optimization; Mathematics; Database","score_opus":0.05448680894328504,"score_gpt":0.20069550489213242,"score_spread":0.1462086959488474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948340529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03065425,0.000045353598,0.96093446,0.00047157018,0.0011741302,0.00031133104,0.0001323794,0.00026370457,0.0060128346],"genre_scores_gemma":[0.98650527,0.00012107211,0.011474382,0.00036591632,0.00015720578,0.00000184931,0.000116088755,0.000022330374,0.0012358758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970335,0.00013803317,0.00032802677,0.0017355102,0.0002453071,0.00051965204],"domain_scores_gemma":[0.99682754,0.0002491161,0.00043879464,0.0018833847,0.00032045785,0.00028071346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019273274,0.0004873013,0.0004946026,0.0002786345,0.00018197013,0.00049473264,0.0035468242,0.00052900997,0.00041214566],"category_scores_gemma":[0.00007267185,0.0005407643,0.00021124324,0.0007029303,0.00009317847,0.00078098715,0.0028607296,0.0009013822,0.00072763435],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040276434,0.00020370093,0.028367339,0.00012646773,0.0001904186,0.00038860168,0.00041719328,0.12278339,0.000033840104,0.8438778,0.002313715,0.0012572714],"study_design_scores_gemma":[0.00037494476,0.000036641788,0.009218764,0.00013111648,0.000039746756,9.952612e-7,0.000018883844,0.590707,0.000039470367,0.39866653,0.0002377605,0.00052817544],"about_ca_topic_score_codex":0.00062535657,"about_ca_topic_score_gemma":0.000109679524,"teacher_disagreement_score":0.955851,"about_ca_system_score_codex":0.00019619294,"about_ca_system_score_gemma":0.0011527986,"threshold_uncertainty_score":0.99970436},"labels":[],"label_agreement":null},{"id":"W2949496227","doi":"","title":"Functional Variational Bayesian Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Infimum and supremum; Estimator; Bayesian probability; Computer science; Gaussian; Applied mathematics; Upper and lower bounds; Mathematical optimization; Artificial neural network; Inference; Mathematics; Divergence (linguistics); Algorithm; Artificial intelligence; Statistics; Physics; Mathematical analysis","score_opus":0.04334265676423611,"score_gpt":0.16992479984644196,"score_spread":0.12658214308220583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949496227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002249379,0.000056348283,0.9911626,0.00040103376,0.0019344684,0.00020462295,0.000012560045,0.00023636958,0.0037426015],"genre_scores_gemma":[0.9940297,0.00003911719,0.0031402167,0.00032853644,0.00027345945,0.000001268979,0.00005405943,0.000017248609,0.0021164035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978518,0.000092596456,0.00022981652,0.0012505758,0.00014559182,0.0004296411],"domain_scores_gemma":[0.99812484,0.000108889355,0.00029090184,0.0010741595,0.000215633,0.00018557001],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016295587,0.00035404667,0.00030626517,0.00020826007,0.0001844647,0.00030489868,0.0018149016,0.00037126098,0.00022580316],"category_scores_gemma":[0.00001828829,0.0003944831,0.00022209379,0.00057600846,0.00007011027,0.0005984408,0.0017116051,0.00078600424,0.00015641654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011753011,0.000037680406,0.0026866063,0.000031950163,0.000040309103,0.00005800292,0.000021290514,0.63139856,7.9175044e-7,0.36442527,0.0007157405,0.0005720616],"study_design_scores_gemma":[0.00028195235,0.0000318864,0.008805276,0.000034681307,0.000028664908,0.000011564327,0.0000061481906,0.9022448,0.0000024288247,0.08799254,0.00016094757,0.00039910982],"about_ca_topic_score_codex":0.000039321476,"about_ca_topic_score_gemma":0.000010519056,"teacher_disagreement_score":0.9917803,"about_ca_system_score_codex":0.00014880508,"about_ca_system_score_gemma":0.0003778973,"threshold_uncertainty_score":0.9998507},"labels":[],"label_agreement":null},{"id":"W2949593890","doi":"","title":"Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations.","year":2019,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Exponential family; Simple (philosophy); Inference; Applied mathematics; Gradient descent; Approximations of π; Mathematics; Convergence (economics); Representation (politics); Exponential function; Bayesian inference; Approximate inference; Computer science; Algorithm; Mathematical optimization; Bayesian probability; Artificial neural network; Artificial intelligence; Mathematical analysis; Statistics","score_opus":0.009866456545828465,"score_gpt":0.24522240259646963,"score_spread":0.23535594605064117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949593890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23225015,0.00011645271,0.7436512,0.0026601127,0.00045106842,0.0003358676,0.000024495554,0.00014821361,0.020362446],"genre_scores_gemma":[0.9829517,0.000035943733,0.016218923,0.00017137131,0.00003090234,0.000013638564,0.000058913076,0.000009607194,0.00050897076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984415,0.00006473138,0.0002883916,0.00045258217,0.0005568254,0.00019597217],"domain_scores_gemma":[0.9988765,0.00013907319,0.00029472727,0.00024263887,0.00037755308,0.00006948993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017152984,0.00020104824,0.00020970461,0.00021252953,0.00009614745,0.0002523704,0.0006842506,0.000056965964,0.00020831032],"category_scores_gemma":[0.00008413608,0.00016021621,0.000039115388,0.00023687632,0.0000602403,0.00057051954,0.00020146137,0.0004116918,0.000036383488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055713637,0.000080563834,0.032195207,0.000046243833,0.000058899772,0.000005185329,0.0006442879,0.0022823713,0.0033540572,0.94486946,0.00001609366,0.016391905],"study_design_scores_gemma":[0.0008908894,0.00040124348,0.06859043,0.00020189886,0.000009443956,0.000029986617,0.000110948415,0.91059834,0.0005291203,0.01757359,0.0007047566,0.00035934657],"about_ca_topic_score_codex":0.000068104295,"about_ca_topic_score_gemma":0.000014512691,"teacher_disagreement_score":0.92729586,"about_ca_system_score_codex":0.0000309596,"about_ca_system_score_gemma":0.00012944096,"threshold_uncertainty_score":0.6533427},"labels":[],"label_agreement":null},{"id":"W2949908882","doi":"10.48550/arxiv.1808.03351","title":"Exploiting Structure for Fast Kernel Learning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Computer science; Preconditioner; Scalability; Kernel (algebra); Kronecker delta; Gaussian process; Algorithm; Artificial intelligence; Machine learning; Theoretical computer science; Gaussian; Mathematics; Iterative method","score_opus":0.05417632118871135,"score_gpt":0.19009200703558984,"score_spread":0.1359156858468785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949908882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096943334,0.000029425071,0.900139,0.00008052795,0.00054329995,0.00024999672,0.000021704893,0.00030417513,0.0016885577],"genre_scores_gemma":[0.98236346,0.000034069883,0.015703235,0.000075391,0.00026669385,0.0000016277822,0.000021518628,0.000025361784,0.0015086333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978767,0.00006122416,0.00020597325,0.001275181,0.00009098842,0.0004899178],"domain_scores_gemma":[0.9982688,0.000092552684,0.0003664704,0.0008036749,0.00030536499,0.000163144],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015277918,0.00035324064,0.00032879345,0.00018592176,0.0003614614,0.0003422321,0.0021396459,0.0003287667,0.00004967875],"category_scores_gemma":[0.00007656449,0.00038818814,0.0001924345,0.00039738227,0.00010675,0.00047402867,0.0018204004,0.0006413156,0.000040059553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008013558,0.00011265573,0.010244515,0.0012660876,0.00027503492,0.00026077277,0.0023146078,0.11952832,0.00034807855,0.8479994,0.0015163166,0.016054064],"study_design_scores_gemma":[0.0004964677,0.0001401743,0.0007316568,0.00026788464,0.00005653856,0.00001073882,0.00020965525,0.7255026,0.0006742595,0.26942053,0.0016994564,0.00079001643],"about_ca_topic_score_codex":0.000027509439,"about_ca_topic_score_gemma":0.000016284728,"teacher_disagreement_score":0.88542014,"about_ca_system_score_codex":0.0001104545,"about_ca_system_score_gemma":0.0002507465,"threshold_uncertainty_score":0.999857},"labels":[],"label_agreement":null},{"id":"W2950182411","doi":"","title":"Practical Bayesian Optimization of Machine Learning Algorithms","year":2012,"lang":"en","type":"preprint","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":994,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Bayesian optimization; Machine learning; Computer science; Artificial intelligence; Hyperparameter; Leverage (statistics); Gaussian process; Algorithm; Inference; Gaussian","score_opus":0.038336209685975085,"score_gpt":0.2755650074667962,"score_spread":0.2372287977808211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950182411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046024756,0.000026305728,0.9747795,0.0007061369,0.00066102605,0.00074358966,0.00028189097,0.0005172115,0.01768185],"genre_scores_gemma":[0.8811822,0.00016032833,0.114895575,0.00025126457,0.00019782699,0.000010825111,0.00040458306,0.00009586176,0.002801517],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.995197,0.00028887356,0.0007431281,0.0016072221,0.0011341965,0.0010296096],"domain_scores_gemma":[0.9955515,0.00025393776,0.0009632619,0.0014446926,0.0008188622,0.0009677137],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005537511,0.00082328886,0.00093524234,0.0011894152,0.00046108922,0.0034784426,0.005032984,0.0006835745,0.0006028077],"category_scores_gemma":[0.00067089987,0.00091027236,0.00042614606,0.0018704252,0.00016669132,0.0116419615,0.011794356,0.001883082,0.00083839206],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018900246,0.0050539053,0.42736068,0.005305382,0.0027897211,0.0030130034,0.0041729533,0.19584078,0.0005028899,0.18625827,0.0051348773,0.16267751],"study_design_scores_gemma":[0.0046924865,0.0012267105,0.04139734,0.0033222004,0.0010162803,0.00047152073,0.0002279742,0.28353813,0.009093882,0.008021011,0.6372306,0.009761884],"about_ca_topic_score_codex":0.000047299873,"about_ca_topic_score_gemma":0.00001965964,"teacher_disagreement_score":0.87657976,"about_ca_system_score_codex":0.0005922859,"about_ca_system_score_gemma":0.00062199985,"threshold_uncertainty_score":0.99993956},"labels":[],"label_agreement":null},{"id":"W2950456782","doi":"10.1101/055483","title":"Inferring time-derivatives, including cell growth rates, using Gaussian processes","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Medical Research Council; Canadian Institutes of Health Research; Wellcome Trust","keywords":"Variable (mathematics); Gaussian; Range (aeronautics); Interpolation (computer graphics); Population; Algorithm; Parametric statistics; Statistical inference; Computer science; Applied mathematics; Mathematics; Biological system; Statistics; Biology; Artificial intelligence; Chemistry; Mathematical analysis","score_opus":0.02225623519711155,"score_gpt":0.23916931672569122,"score_spread":0.2169130815285797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950456782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12655513,0.0021239514,0.8656883,0.0009468836,0.0013402624,0.0010664499,0.000127771,0.0017880072,0.000363222],"genre_scores_gemma":[0.925519,0.0002550032,0.073175624,0.00022948555,0.0005226944,0.000112174814,1.8227847e-7,0.00016570181,0.000020169447],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9945363,0.00019930991,0.0009835432,0.0021928602,0.0007751986,0.0013128091],"domain_scores_gemma":[0.9949024,0.00018346774,0.0011020125,0.0019977733,0.0012728943,0.0005414984],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006815916,0.0011455051,0.0009795218,0.0006479058,0.00060623535,0.0015555706,0.00339052,0.0006789954,0.000049032653],"category_scores_gemma":[0.00046626697,0.0010405856,0.00017914963,0.0016932542,0.00021242772,0.0016645377,0.003575387,0.0009922106,0.00018813065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025476887,0.00033227273,0.015586509,0.0059354994,0.00024001506,0.0002820551,0.00014606028,0.000078606055,0.9578851,0.018951656,0.000522366,0.000014417725],"study_design_scores_gemma":[0.0010137241,0.000103443235,0.014188335,0.004984427,0.00012221657,2.8568434e-7,0.000004038828,0.008399235,0.96544975,0.0007360803,0.0014193548,0.0035791297],"about_ca_topic_score_codex":0.000042802945,"about_ca_topic_score_gemma":0.0000013813203,"teacher_disagreement_score":0.79896384,"about_ca_system_score_codex":0.0004877439,"about_ca_system_score_gemma":0.0031593947,"threshold_uncertainty_score":0.9994809},"labels":[],"label_agreement":null},{"id":"W2950540981","doi":"10.48550/arxiv.0912.1586","title":"Dynamic Trees for Learning and Design","year":2009,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Tree (set theory); Machine learning; Inference; Multinomial distribution; Artificial intelligence; Regression; Partition (number theory); Decision tree; Data mining; Mathematics; Econometrics; Statistics","score_opus":0.05200129096357902,"score_gpt":0.19622853507400764,"score_spread":0.14422724411042862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950540981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061324593,0.00016891534,0.99227536,0.00016690294,0.0001112386,0.00028309156,0.0000029102846,0.00019514094,0.00066396146],"genre_scores_gemma":[0.93505496,0.00039111247,0.06317072,0.00004657762,0.000016426735,0.0000012937317,0.0000044592125,0.000010005226,0.0013044672],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859273,0.00007205313,0.00012494806,0.0008700997,0.000046965397,0.00029317153],"domain_scores_gemma":[0.9991088,0.00012370176,0.00017047,0.00038629206,0.00009378704,0.00011698233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001923774,0.00023975017,0.00025068078,0.00016361168,0.00019360494,0.00022450894,0.0009169499,0.0001987634,0.0000051183683],"category_scores_gemma":[0.000034902412,0.00026263652,0.000094268034,0.00024296992,0.000060530318,0.0002759037,0.0005994155,0.00035125375,0.000008511099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009791313,0.00014609858,0.0014823655,0.00043281916,0.00013398414,0.00027413585,0.0005979119,0.23188256,0.0001439022,0.6939994,0.000316742,0.07049219],"study_design_scores_gemma":[0.00025032056,0.00013018165,0.0007919962,0.00007934454,0.000029435265,0.0000050155845,0.000022060342,0.7527249,0.000038036287,0.2453261,0.0003119176,0.0002907092],"about_ca_topic_score_codex":0.000010525521,"about_ca_topic_score_gemma":0.000012250598,"teacher_disagreement_score":0.9291047,"about_ca_system_score_codex":0.00006546355,"about_ca_system_score_gemma":0.0001557574,"threshold_uncertainty_score":0.9999826},"labels":[],"label_agreement":null},{"id":"W2950838826","doi":"10.48550/arxiv.1809.04279","title":"Discretely Relaxing Continuous Variables for tractable Variational\\n Inference","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Estimator; Latent variable; Computer science; Kronecker delta; Prior probability; Algorithm; Parameterized complexity; Bayesian inference; Approximate Bayesian computation; Markov chain Monte Carlo; Approximate inference; Importance sampling; Monte Carlo method; Bayesian probability; Mathematics; Artificial intelligence; Statistics","score_opus":0.06669854540801469,"score_gpt":0.20707924076949316,"score_spread":0.14038069536147846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950838826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0111377705,0.00010818769,0.9745865,0.00033137077,0.0020461897,0.0012353902,0.00021927951,0.00027737382,0.010057921],"genre_scores_gemma":[0.94924164,0.00041243483,0.043125276,0.00017499436,0.00052413996,0.00001483646,0.00007894948,0.00006418973,0.006363531],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935403,0.00026406208,0.0009574144,0.003459312,0.00030860744,0.0014703141],"domain_scores_gemma":[0.9928232,0.0012491328,0.0015010106,0.0021876076,0.0016607553,0.0005783103],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011338459,0.0010717178,0.0011638943,0.00046204423,0.0012431848,0.0013193749,0.0046164477,0.0009818426,0.00050644134],"category_scores_gemma":[0.00065670104,0.0012327702,0.0005637914,0.0015776069,0.0005367745,0.002325472,0.0024662488,0.0010215472,0.00028060813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021245025,0.00035068672,0.003831766,0.00061519374,0.00034525525,0.00011000906,0.00076638063,0.033785626,0.000069974594,0.957118,0.0007140597,0.0020806186],"study_design_scores_gemma":[0.0011239112,0.00039390693,0.0021779053,0.0006339947,0.0002798761,0.00001622508,0.00010533954,0.633603,0.00014946921,0.35624903,0.0040007588,0.0012666297],"about_ca_topic_score_codex":0.0003125469,"about_ca_topic_score_gemma":0.00006317415,"teacher_disagreement_score":0.93810385,"about_ca_system_score_codex":0.0004341251,"about_ca_system_score_gemma":0.0021669855,"threshold_uncertainty_score":0.99971735},"labels":[],"label_agreement":null},{"id":"W2951494873","doi":"","title":"LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Differentiable function; Computer science; Inference; Piecewise; Probabilistic logic; Classification of discontinuities; Exploit; Theoretical computer science; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.06308179251578344,"score_gpt":0.19475609969972663,"score_spread":0.13167430718394318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951494873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04030471,0.000045044457,0.9561667,0.00010635372,0.00040386614,0.0015154785,0.00006023308,0.00026217723,0.0011354412],"genre_scores_gemma":[0.95936394,0.00003275458,0.03711015,0.000060342725,0.00007313258,0.000026577223,0.000049143193,0.000040573173,0.0032433816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716556,0.000036874026,0.00029786464,0.001630418,0.00014542128,0.00072386645],"domain_scores_gemma":[0.99737805,0.0001328708,0.00034215755,0.0015504118,0.00039252287,0.00020396775],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019042859,0.00050671876,0.0005410377,0.00023861794,0.00024261996,0.00045210516,0.0025504634,0.00037939378,0.000026727925],"category_scores_gemma":[0.00004964014,0.0005251684,0.00026002777,0.0006651511,0.00009251426,0.00062913937,0.0018842875,0.0004952043,0.00007077117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115110626,0.00082474516,0.0011148999,0.0074115293,0.00033391968,0.00022224833,0.002721083,0.5857726,0.000045680852,0.39367282,0.0010962083,0.0066691847],"study_design_scores_gemma":[0.00066123635,0.00008687792,0.0001236134,0.00042892556,0.00007770079,0.0000038431403,0.00008067851,0.892747,0.00004856092,0.10488638,0.00021248907,0.0006427017],"about_ca_topic_score_codex":0.00020850051,"about_ca_topic_score_gemma":0.00016806585,"teacher_disagreement_score":0.9190592,"about_ca_system_score_codex":0.00018870283,"about_ca_system_score_gemma":0.00055549445,"threshold_uncertainty_score":0.99972},"labels":[],"label_agreement":null},{"id":"W2952165242","doi":"","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","year":2016,"lang":"en","type":"preprint","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":622,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Random variable; Random graph; Computation; Applied mathematics; Discrete-time stochastic process; Estimator; Mathematics; Differentiable function; Computer science; Mathematical optimization; Graph; Continuous-time stochastic process; Stochastic optimization; Algorithm; Discrete mathematics; Statistics; Mathematical analysis","score_opus":0.01725181474429018,"score_gpt":0.24263039573127584,"score_spread":0.22537858098698565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952165242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076712715,0.00014147973,0.95392215,0.0047848206,0.00020837282,0.0008892478,0.0010629417,0.00009648762,0.03122324],"genre_scores_gemma":[0.9658543,0.007005023,0.021257063,0.000010986058,0.000081127124,0.0000013696779,0.00029747718,0.000025885549,0.005466753],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957025,0.0009774067,0.00034890458,0.00093137164,0.0011800983,0.00085972674],"domain_scores_gemma":[0.99445456,0.0015528598,0.0008555763,0.0015394676,0.0012688227,0.00032868664],"candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0017168585,0.0003549109,0.00072130497,0.0004432724,0.0015071458,0.00015743062,0.005632397,0.00030733566,0.000055038334],"category_scores_gemma":[0.00033296167,0.00031617936,0.00042611404,0.00091050373,0.0021167353,0.0008066462,0.006656668,0.0011551742,0.0000055495793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028091564,0.00008867053,0.004030807,0.0007591151,0.00067402766,0.00027193426,0.002895844,0.000079253674,0.00078622985,0.9501547,0.011150242,0.026299983],"study_design_scores_gemma":[0.008825388,0.001400818,0.014754302,0.002360446,0.00025426495,0.00003642489,0.0067538954,0.02988855,0.00055769295,0.34323752,0.5904488,0.0014818801],"about_ca_topic_score_codex":0.00053757004,"about_ca_topic_score_gemma":0.00020712563,"teacher_disagreement_score":0.95818305,"about_ca_system_score_codex":0.00028296342,"about_ca_system_score_gemma":0.0013527023,"threshold_uncertainty_score":0.999929},"labels":[],"label_agreement":null},{"id":"W2952208847","doi":"10.1109/cdc.2017.8264427","title":"Stable Gaussian process based tracking control of Lagrangian systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control theory (sociology); Feed forward; Stability (learning theory); Tracking (education); Compensation (psychology); Process (computing); Bounded function; Control (management); Gaussian process","score_opus":0.01827914506424886,"score_gpt":0.2617862379669671,"score_spread":0.24350709290271824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952208847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022457582,0.00009538562,0.96829313,0.0010973208,0.00023827024,0.00021201254,0.0000064633246,0.000106225925,0.027705451],"genre_scores_gemma":[0.994995,0.0000019905528,0.0045055863,0.00010428341,0.000039751103,0.00001764822,6.1661865e-7,0.000008554871,0.000326575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986832,0.000028046328,0.00029758413,0.00034037154,0.00031757125,0.00033320163],"domain_scores_gemma":[0.99820167,0.00004389033,0.00037658014,0.0010324297,0.00022637159,0.00011908586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030807007,0.00015022254,0.00028012827,0.0000803511,0.00034540807,0.0008595967,0.0019122977,0.000069973416,0.000033646236],"category_scores_gemma":[0.000071025155,0.00011549638,0.0000622437,0.00012495286,0.00008088314,0.0011269334,0.00006093739,0.00009462883,0.000017666156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056321674,0.00044736682,0.05559298,0.0021733053,0.00010224652,0.00008709429,0.0009342275,0.0016628569,0.0037160884,0.8911,0.000500105,0.043627433],"study_design_scores_gemma":[0.0032893925,0.00030665428,0.042968754,0.0007224555,0.000041146384,0.000028952842,0.00022672005,0.9171231,0.025380384,0.0074331574,0.001622319,0.0008569684],"about_ca_topic_score_codex":0.00019373055,"about_ca_topic_score_gemma":0.00002677992,"teacher_disagreement_score":0.9927492,"about_ca_system_score_codex":0.000013626206,"about_ca_system_score_gemma":0.00020571274,"threshold_uncertainty_score":0.82891107},"labels":[],"label_agreement":null},{"id":"W2952300048","doi":"10.48550/arxiv.1603.04733","title":"Structured and Efficient Variational Deep Learning with Matrix Gaussian\\n Posteriors","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Gaussian; Matrix (chemical analysis); Applied mathematics; Computer science; Artificial intelligence; Gaussian process; Statistical physics; Mathematics; Mathematical optimization; Machine learning; Physics; Materials science; Quantum mechanics","score_opus":0.016893451073602853,"score_gpt":0.1713345360625139,"score_spread":0.15444108498891104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952300048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11877881,0.000064373875,0.8792505,0.00021042838,0.00018765761,0.00016884615,0.000009033085,0.00015228787,0.0011780132],"genre_scores_gemma":[0.9910281,0.000045410987,0.008183184,0.000033608318,0.000052491057,8.2713456e-7,0.000006563781,0.000016377848,0.0006334764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981973,0.00008694015,0.00016937012,0.0010556093,0.00013949584,0.0003512599],"domain_scores_gemma":[0.9987069,0.00007280172,0.0003052421,0.00056697143,0.00015699635,0.00019111446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012303892,0.0003261452,0.00027928266,0.00021924819,0.00024219137,0.00028720294,0.0009733002,0.00021980502,0.000043000327],"category_scores_gemma":[0.000018854389,0.0002636897,0.0000670222,0.00034559116,0.00013024722,0.00025155285,0.0012499889,0.00045010293,0.000023472947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009006781,0.000050217706,0.009834826,0.00022258509,0.00013360944,0.00039351298,0.0007524293,0.13102047,0.00009982584,0.8543196,0.000013436378,0.0030694588],"study_design_scores_gemma":[0.0012165249,0.00021467943,0.032698862,0.0003842824,0.00009687699,0.00009627528,0.00009484232,0.86117226,0.00010256713,0.102652684,0.00022877281,0.00104136],"about_ca_topic_score_codex":0.000016991333,"about_ca_topic_score_gemma":0.000011974882,"teacher_disagreement_score":0.87224925,"about_ca_system_score_codex":0.00010548397,"about_ca_system_score_gemma":0.00023511016,"threshold_uncertainty_score":0.9999815},"labels":[],"label_agreement":null},{"id":"W2953298772","doi":"10.48550/arxiv.1412.0630","title":"Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smoothing; Mathematics; Trajectory; Gaussian process; Stochastic differential equation; Nonlinear system; Covariance; Applied mathematics; Mathematical optimization; Gaussian; Statistics","score_opus":0.030444901269095773,"score_gpt":0.20544880574621782,"score_spread":0.17500390447712205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953298772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22256432,0.00004912224,0.76682097,0.00030604357,0.0005059805,0.00047639722,0.000015328445,0.0005896361,0.008672194],"genre_scores_gemma":[0.985089,0.000065237225,0.011444598,0.00016659776,0.0001532544,0.0000036609515,0.00005312192,0.00004150718,0.0029829931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966994,0.00019697091,0.00042352965,0.001792036,0.00027142756,0.00061661395],"domain_scores_gemma":[0.99683243,0.000112149675,0.00071997923,0.0016257142,0.00033617733,0.0003735385],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003749001,0.00063623965,0.0006833357,0.00037294207,0.00029237577,0.0004030753,0.0028083911,0.0005878052,0.00015848916],"category_scores_gemma":[0.000107928056,0.000629312,0.0002430281,0.000675097,0.00017216064,0.0008859294,0.0010570667,0.00089954445,0.00078083685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073512166,0.0025241217,0.009223814,0.0056175115,0.0007638456,0.004228231,0.0060664294,0.6295311,0.0011153148,0.25976658,0.005738027,0.07468992],"study_design_scores_gemma":[0.00060026674,0.00016414071,0.00081054564,0.00077578553,0.000082352744,0.000029315854,0.000053807868,0.9458938,0.0009056558,0.049335733,0.00045857715,0.0008900241],"about_ca_topic_score_codex":0.000095859556,"about_ca_topic_score_gemma":0.00001238914,"teacher_disagreement_score":0.7625247,"about_ca_system_score_codex":0.00017497539,"about_ca_system_score_gemma":0.000728658,"threshold_uncertainty_score":0.9999972},"labels":[],"label_agreement":null},{"id":"W2958216351","doi":"10.1109/msp.2019.2927685","title":"A Fast, Accurate, and Separable Method for Fitting a Gaussian Function [Tips &amp; Tricks]","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; University of Waterloo; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Gaussian; Noise (video); Limit (mathematics); Function (biology); Interpretation (philosophy); Dispersion (optics); Probability density function; Gaussian noise; SIGNAL (programming language)","score_opus":0.020692706076222513,"score_gpt":0.2852510568491806,"score_spread":0.2645583507729581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958216351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028875,0.0004626835,0.9910665,0.0006454924,0.00024122739,0.0004401942,0.000005709843,0.00024291371,0.004007747],"genre_scores_gemma":[0.6566661,0.000010622343,0.337085,0.00055906054,0.00020209116,0.000062618536,0.000007593547,0.00003420265,0.0053727054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977655,0.00006380061,0.0004337255,0.00084444945,0.0003078216,0.0005847188],"domain_scores_gemma":[0.99870783,0.00019914929,0.0003236574,0.00033662425,0.00026112027,0.00017159445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069149456,0.00031601405,0.00038090593,0.00020841033,0.00032027616,0.0008129977,0.0005286404,0.00013305107,0.00005248305],"category_scores_gemma":[0.00004233239,0.00027239532,0.00008156267,0.00081977004,0.000040845804,0.0014587294,0.00010647777,0.00024868865,0.00013528745],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018278975,0.00012823068,0.00059425377,0.0014241781,0.000046636505,0.000007456007,0.0009993843,0.0006518893,0.03283329,0.0040127714,0.0040842337,0.9550349],"study_design_scores_gemma":[0.0025268614,0.00074661116,0.0014105506,0.00061361416,0.00008737593,0.0002690069,0.000075493015,0.8877165,0.0033890347,0.034663867,0.0673677,0.0011334388],"about_ca_topic_score_codex":0.000007834698,"about_ca_topic_score_gemma":0.0000071537715,"teacher_disagreement_score":0.95390147,"about_ca_system_score_codex":0.000036801044,"about_ca_system_score_gemma":0.00031972036,"threshold_uncertainty_score":0.9999728},"labels":[],"label_agreement":null},{"id":"W2962717636","doi":"10.1007/978-3-030-40245-7_9","title":"Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in physics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Extrapolation; Generalization; Observable; Interpolation (computer graphics); Quantum; Statistical physics; Physical system; Range (aeronautics); Gaussian; Mathematics; Phase space; Gaussian process; Physics; Computer science; Quantum mechanics; Mathematical analysis; Artificial intelligence","score_opus":0.021268660038168203,"score_gpt":0.25459286701270295,"score_spread":0.23332420697453474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962717636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010837247,0.0003988336,0.9869211,0.00114659,0.00011018337,0.0003031774,0.00004033928,0.000112243135,0.00013033269],"genre_scores_gemma":[0.96361285,0.00007775554,0.03559365,0.00019504191,0.00028149996,0.0000406906,0.00016447529,0.000032339165,0.000001686566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998064,0.000065176944,0.0003420017,0.000785473,0.00045165056,0.0002917403],"domain_scores_gemma":[0.99851775,0.00012397807,0.00047007966,0.000581941,0.00023160763,0.00007464768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000518363,0.00041144504,0.00053260854,0.00005948596,0.000055919267,0.00019427881,0.000994992,0.00020620374,0.0000023361042],"category_scores_gemma":[0.00008600584,0.0003318005,0.00007228841,0.0011257237,0.00008723964,0.0003891,0.00034443286,0.0005373757,0.00000224115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019564586,0.00204103,0.013292675,0.016774386,0.00033571376,0.000048819413,0.02740347,0.41496813,0.034975093,0.33086777,0.00057731976,0.15851992],"study_design_scores_gemma":[0.00023478258,0.00016130754,0.00039312447,0.00053107465,0.000030755265,0.0000019033793,0.000004448737,0.44401634,0.07894314,0.47515014,0.00005295475,0.00048005395],"about_ca_topic_score_codex":0.00008500629,"about_ca_topic_score_gemma":0.000030552434,"teacher_disagreement_score":0.9527756,"about_ca_system_score_codex":0.000046141355,"about_ca_system_score_gemma":0.00053064176,"threshold_uncertainty_score":0.9999134},"labels":[],"label_agreement":null},{"id":"W2963827215","doi":"","title":"Differentiable Compositional Kernel Learning for Gaussian Processes","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kernel (algebra); Extrapolation; Differentiable function; Generalization; Computer science; Artificial intelligence; Artificial neural network; Gaussian process; Mathematics; Gaussian; Algorithm; Pattern recognition (psychology); Discrete mathematics; Statistics; Pure mathematics; Mathematical analysis","score_opus":0.02641541766885436,"score_gpt":0.29616444822546417,"score_spread":0.2697490305566098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963827215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009447845,0.000041690786,0.9332802,0.0052168057,0.00052644213,0.00021963898,0.000014445472,0.00038549272,0.050867386],"genre_scores_gemma":[0.97734076,0.0000342148,0.016619144,0.00040898426,0.0004158416,0.00006561954,0.00010713352,0.000023629736,0.0049846442],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997983,0.00007448754,0.00033854382,0.00066071196,0.00052972493,0.00041356322],"domain_scores_gemma":[0.99833524,0.00018570476,0.00026391802,0.00021774221,0.0008640552,0.00013331638],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023230552,0.00028044856,0.0002276183,0.00023241469,0.00057899125,0.00076748984,0.001301064,0.00008956126,0.0007076408],"category_scores_gemma":[0.0003417601,0.00025333543,0.000084589235,0.00029012046,0.00011648961,0.0006059737,0.00023990753,0.00049340347,0.00022876597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014020827,0.00020272301,0.010443052,0.00010272905,0.00009731917,0.000010022939,0.0007004408,0.0006117923,0.0025763381,0.9650436,0.00045015136,0.01962161],"study_design_scores_gemma":[0.0012948764,0.0013058502,0.0051171947,0.00038301773,0.000016539729,0.00005715833,0.0000964088,0.89609206,0.004940758,0.056525346,0.033454552,0.0007162661],"about_ca_topic_score_codex":0.00003232285,"about_ca_topic_score_gemma":0.000025559591,"teacher_disagreement_score":0.96789294,"about_ca_system_score_codex":0.00006182768,"about_ca_system_score_gemma":0.00021784907,"threshold_uncertainty_score":0.9999919},"labels":[],"label_agreement":null},{"id":"W2964052793","doi":"","title":"Deep Neural Networks as Gaussian Processes","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":318,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Gaussian process; Computer science; Covariance; Artificial intelligence; Covariance function; Kriging; Global Positioning System; Algorithm; MNIST database; Gaussian; Deep learning; Kernel (algebra); Inference; Stochastic neural network; Machine learning; Pattern recognition (psychology); Recurrent neural network; Mathematics; Covariance matrix; Statistics","score_opus":0.030888280003430524,"score_gpt":0.1793273458303514,"score_spread":0.14843906582692087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964052793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031006852,0.000059180064,0.953164,0.0002599973,0.00027704265,0.00009638572,4.6619132e-7,0.00027342615,0.01486269],"genre_scores_gemma":[0.99702245,0.00003678281,0.00138902,0.0004857824,0.0001742468,5.176706e-7,0.0000013612106,0.000011194695,0.00087863806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857587,0.000041345476,0.00013502307,0.0006940752,0.00008046867,0.0004732086],"domain_scores_gemma":[0.99876285,0.00005193951,0.00011285406,0.00061219657,0.00023865436,0.00022149691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008686789,0.00020237302,0.00016021296,0.00011842789,0.00036064297,0.00020265707,0.0014750721,0.00010028972,0.00012505417],"category_scores_gemma":[0.000045255,0.00019888711,0.000058899917,0.0016052362,0.000209089,0.0010819612,0.0003397154,0.00016695213,0.00030026233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006022154,0.00016161984,0.009254214,0.000084060965,0.000049644656,0.00059527095,0.00066746975,0.016959496,0.000024478164,0.9577668,0.00057561503,0.013801135],"study_design_scores_gemma":[0.0003018573,0.00024249802,0.0014661301,0.00002178551,0.000015254586,0.00004106142,0.000067632456,0.9647033,0.00019153804,0.03181063,0.00080390373,0.00033439434],"about_ca_topic_score_codex":0.00004103502,"about_ca_topic_score_gemma":0.00009996031,"teacher_disagreement_score":0.9660156,"about_ca_system_score_codex":0.00003954119,"about_ca_system_score_gemma":0.00011429741,"threshold_uncertainty_score":0.8110381},"labels":[],"label_agreement":null},{"id":"W2965168881","doi":"10.57702/37nl6e9a","title":"Comparing EM with GD in Mixture Models of Two Components","year":2024,"lang":"en","type":"article","venue":"TIB Data Manager","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mixture model; Maxima and minima; Expectation–maximization algorithm; Component (thermodynamics); Gradient descent; Cluster (spacecraft); Gaussian; Entropy (arrow of time); Statistical physics; Cluster analysis; Mathematics; Applied mathematics; Computer science; Physics; Maximum likelihood; Statistics; Artificial intelligence; Thermodynamics; Artificial neural network; Mathematical analysis","score_opus":0.06737411279423766,"score_gpt":0.28234003601149094,"score_spread":0.21496592321725327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965168881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02625628,0.0004480859,0.96664,0.0003922239,0.0001190936,0.00011582385,0.000024370307,0.00011466672,0.0058894753],"genre_scores_gemma":[0.96788305,0.000016572547,0.03173615,0.000067974535,0.00001851596,0.0000027140707,0.00004884208,0.000008432956,0.00021775025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891204,0.000019797564,0.00018331972,0.00046573507,0.00022178717,0.00019730876],"domain_scores_gemma":[0.998789,0.000025860907,0.000039971033,0.0010842128,0.000019103327,0.00004185699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017550818,0.00011487695,0.00017598999,0.00014255613,0.000024044191,0.0002706455,0.0018736129,0.000024486844,0.000010620453],"category_scores_gemma":[0.0000027856254,0.0000878102,0.00001361296,0.00054435915,0.000030861585,0.0016605329,0.0009879495,0.00015174577,0.00003105531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031375464,0.00023944098,0.0052436986,0.0014825709,0.00012798513,0.0007905688,0.0024896949,0.0051459195,0.0005434266,0.9146349,0.0095072035,0.05976323],"study_design_scores_gemma":[0.00022240609,0.000018225393,0.0033892265,0.00037436612,0.00000966398,0.000015833872,0.000021250024,0.98051935,0.000067051216,0.013946513,0.0012629387,0.00015317369],"about_ca_topic_score_codex":0.00014059278,"about_ca_topic_score_gemma":0.00014931193,"teacher_disagreement_score":0.97537345,"about_ca_system_score_codex":0.000012978977,"about_ca_system_score_gemma":0.00003394596,"threshold_uncertainty_score":0.35807958},"labels":[],"label_agreement":null},{"id":"W2978369754","doi":"10.1109/iros40897.2019.8968107","title":"Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse dynamics; Generalization; Context (archaeology); Computer science; Parametric statistics; Robot; Inverse; Curse of dimensionality; Gaussian process; Euler's formula; Artificial intelligence; Dimensionality reduction; Gaussian; Algorithm; Control theory (sociology); Mathematics; Control (management)","score_opus":0.04582442589541233,"score_gpt":0.2952060314646872,"score_spread":0.24938160556927488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978369754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015416514,0.00044330163,0.9873495,0.0030931472,0.0011312627,0.0010910733,0.00010399436,0.0005687108,0.006064821],"genre_scores_gemma":[0.6962421,0.00013400144,0.2976647,0.00027578525,0.0002155715,0.00016305916,0.00081474526,0.00006681673,0.0044232025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960088,0.000055231063,0.00062139507,0.002099634,0.0004579166,0.0007569899],"domain_scores_gemma":[0.9957394,0.0002527484,0.0005451023,0.0028123,0.0004586824,0.00019176611],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00054578617,0.00057291496,0.0006504953,0.0002270136,0.00026091875,0.0013289325,0.0065802406,0.00044487228,0.00002485368],"category_scores_gemma":[0.0003946648,0.00048258217,0.00012491719,0.0004889307,0.000065027794,0.00042747622,0.0066157123,0.00092458964,0.00008305172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006873945,0.0008713727,0.0028779777,0.017407922,0.00044166972,0.000045983095,0.002157484,0.35889867,0.000060788487,0.38792762,0.008123594,0.22111818],"study_design_scores_gemma":[0.00026912257,0.00009111008,0.00020205334,0.00033786954,0.000037675945,0.00002145321,0.00005997326,0.9892887,0.00016895597,0.0061575123,0.0026369693,0.00072863983],"about_ca_topic_score_codex":0.00009678349,"about_ca_topic_score_gemma":0.000111596004,"teacher_disagreement_score":0.69608796,"about_ca_system_score_codex":0.00014849655,"about_ca_system_score_gemma":0.0017842393,"threshold_uncertainty_score":0.9997626},"labels":[],"label_agreement":null},{"id":"W2991472473","doi":"10.1177/0278364920937608","title":"Exactly sparse Gaussian variational inference with application to derivative-free batch nonlinear state estimation","year":2020,"lang":"en","type":"preprint","venue":"The International Journal of Robotics Research","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Institute for Christian Studies; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Mathematics; Maximum a posteriori estimation; Gaussian; Covariance matrix; Mathematical optimization; Nonlinear system; Algorithm; Estimation of covariance matrices; Inference; Applied mathematics; Computer science; Artificial intelligence; Statistics","score_opus":0.06681865901031304,"score_gpt":0.37321802186024255,"score_spread":0.3063993628499295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991472473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078851555,0.000021162785,0.76235944,0.23594622,0.00025750228,0.00033448183,0.000024173689,0.000024530875,0.00024395424],"genre_scores_gemma":[0.49347225,0.00006114336,0.5037937,0.0020322488,0.0004967704,0.00003270076,0.000021745202,0.000024786725,0.00006461746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943496,0.00027995463,0.0008006362,0.0005066012,0.0036676533,0.00039557586],"domain_scores_gemma":[0.9924681,0.0008193208,0.0008199278,0.0008968296,0.0047332323,0.00026255622],"candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0018549868,0.00027724262,0.00033994205,0.00054318743,0.00019169158,0.0013755284,0.008563682,0.00013580479,0.00001869302],"category_scores_gemma":[0.0013001938,0.00019082917,0.000096463424,0.0007937115,0.00015394467,0.0005738613,0.0032102847,0.0022593911,0.000067340196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029334996,0.00020963725,0.00045396914,0.00009853052,0.0003973788,0.00022381067,0.0029425232,0.8710716,0.0005201778,0.09593753,0.004209098,0.02364242],"study_design_scores_gemma":[0.00044676085,0.00035906117,0.0031344683,0.00037261192,0.00001705194,0.000118415206,0.000050090082,0.7357468,0.000828389,0.25777072,0.0008911568,0.0002644787],"about_ca_topic_score_codex":0.00008992071,"about_ca_topic_score_gemma":0.000035309356,"teacher_disagreement_score":0.49268374,"about_ca_system_score_codex":0.0003709058,"about_ca_system_score_gemma":0.0024152626,"threshold_uncertainty_score":0.99966115},"labels":[],"label_agreement":null},{"id":"W2999823193","doi":"10.1016/j.idm.2019.12.007","title":"Bayesian inference for dynamical systems","year":2020,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov chain Monte Carlo; Bayesian inference; Inference; Bayesian probability; Fiducial inference; Bayesian statistics; Computer science; Statistical inference; Frequentist inference; Mathematics; Machine learning; Data mining; Algorithm; Artificial intelligence; Statistics","score_opus":0.02568718585408232,"score_gpt":0.2502482133426714,"score_spread":0.22456102748858908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999823193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023379228,0.0002567706,0.9942909,0.0011910623,0.00029186817,0.00042466598,0.000020003796,0.0005733227,0.0006134795],"genre_scores_gemma":[0.9863327,0.000025282716,0.012617692,0.00064263045,0.0002037857,0.00012678564,0.000008410444,0.000021918855,0.000020754733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983369,0.00003676262,0.00032353058,0.00063332345,0.0002487889,0.00042067043],"domain_scores_gemma":[0.99858767,0.00013005639,0.00011141777,0.00037790264,0.00016824409,0.0006246962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000097645934,0.00022951182,0.00024032337,0.00007054296,0.00022970507,0.00053839665,0.000656782,0.00007151874,0.000006522787],"category_scores_gemma":[0.00009295929,0.00022118115,0.0001313229,0.00037510798,0.00003644903,0.00056704174,0.00013479363,0.00015756623,0.000041681797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026385718,0.00007968742,0.0015325132,0.00037971383,0.000026215415,0.000022016806,0.00031080196,0.6785889,0.000028909933,0.31593993,0.00013731317,0.002927617],"study_design_scores_gemma":[0.00029195027,0.00008709194,0.000037169528,0.000049971924,0.00001925815,0.0000032299472,0.0000071509644,0.968539,0.000015285874,0.0299057,0.0007573708,0.00028682177],"about_ca_topic_score_codex":0.000025284427,"about_ca_topic_score_gemma":0.0000014339473,"teacher_disagreement_score":0.98399484,"about_ca_system_score_codex":0.000046957346,"about_ca_system_score_gemma":0.0002331197,"threshold_uncertainty_score":0.90195054},"labels":[],"label_agreement":null},{"id":"W3002270926","doi":"10.1109/lra.2020.2969153","title":"A Data-Driven Motion Prior for Continuous-Time Trajectory Estimation on <i>SE(3)</i>","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jerk; Trajectory; Odometry; Computer science; Acceleration; White noise; Context (archaeology); Artificial intelligence; Noise (video); Computer vision; Robot; Control theory (sociology); Mobile robot; Geography; Physics","score_opus":0.032000275251698886,"score_gpt":0.2572834345899069,"score_spread":0.225283159338208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002270926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007900867,0.000009427788,0.96316266,0.028185414,0.00015712231,0.00029392884,0.00003136412,0.00022545167,0.00003376874],"genre_scores_gemma":[0.64874786,0.0000058996643,0.34245026,0.008506299,0.00015484997,0.000018783065,0.00008423189,0.000016027932,0.000015769694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989744,0.00002718407,0.0002363503,0.00041016747,0.00017979754,0.00017211448],"domain_scores_gemma":[0.9993235,0.00008765664,0.00015419579,0.00029944337,0.000043311986,0.00009187651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011034138,0.00013588033,0.00016444983,0.000053157662,0.00013702245,0.00028425912,0.00043931557,0.000047232275,0.0000034588506],"category_scores_gemma":[0.0000522955,0.00012837723,0.000030731866,0.00014393058,0.000028713366,0.0006791314,0.00005416402,0.00007655046,0.000036661855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004862019,0.00020521208,0.00015403157,0.00072450214,0.000097115735,0.000018255805,0.0026709354,0.59820646,0.054031413,0.02963436,0.044032723,0.27017638],"study_design_scores_gemma":[0.00037440672,0.00010112017,0.0006281796,0.00003821243,0.000014397765,0.0000035543585,0.000003948543,0.99717957,0.0007899802,0.00026609786,0.0004431381,0.00015742527],"about_ca_topic_score_codex":0.0000015043429,"about_ca_topic_score_gemma":4.712084e-7,"teacher_disagreement_score":0.640847,"about_ca_system_score_codex":0.000018099969,"about_ca_system_score_gemma":0.000030777504,"threshold_uncertainty_score":0.5235072},"labels":[],"label_agreement":null},{"id":"W3025410163","doi":"","title":"Variational Inference as Iterative Projection in a Bayesian Hilbert Space","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Bayesian inference; Kullback–Leibler divergence; Divergence (linguistics); Subspace topology; Bayesian probability; Hilbert space; Inference; Applied mathematics; Projection (relational algebra); Artificial intelligence; Mathematical optimization; Algorithm; Computer science; Mathematical analysis; Statistics","score_opus":0.05089584329753272,"score_gpt":0.20841458252272574,"score_spread":0.15751873922519302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025410163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010040672,0.0000187728,0.9772618,0.0017795558,0.00034508703,0.00040606852,0.000012001261,0.00019654974,0.0099395085],"genre_scores_gemma":[0.9898774,0.000059659607,0.00898274,0.00025655664,0.00008106506,0.0000051987536,0.000020942249,0.000014213744,0.00070223614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975922,0.00016741258,0.00027312146,0.0014421017,0.00015933426,0.0003658331],"domain_scores_gemma":[0.99849707,0.00012936121,0.0003120185,0.0006648713,0.00020649962,0.00019019814],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001569585,0.00037737752,0.0003659112,0.00037242725,0.00012757407,0.00034638136,0.0015277753,0.0003133733,0.0000713917],"category_scores_gemma":[0.00014291197,0.000430372,0.00012442785,0.0013919482,0.000072717856,0.0008594403,0.0014402384,0.0008708305,0.00013776161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038600705,0.00011215598,0.0058804955,0.000117975294,0.000041794196,0.00036805414,0.0016189006,0.017513048,0.000027640175,0.97357005,0.00011164819,0.0005996139],"study_design_scores_gemma":[0.0003604379,0.00009268105,0.005189901,0.00017168389,0.000016545322,0.000008150855,0.000058909634,0.6334769,0.00007949976,0.3599283,0.00017137162,0.0004456058],"about_ca_topic_score_codex":0.00055255863,"about_ca_topic_score_gemma":0.00019704079,"teacher_disagreement_score":0.9798367,"about_ca_system_score_codex":0.0003017283,"about_ca_system_score_gemma":0.0010948727,"threshold_uncertainty_score":0.9998148},"labels":[],"label_agreement":null},{"id":"W3033768482","doi":"10.48550/arxiv.2006.03015","title":"Quadruply Stochastic Gaussian Processes","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Estimator; Gaussian process; Gaussian; Computer science; Kernel (algebra); Upper and lower bounds; Mathematics; Mathematical optimization; Applied mathematics; Algorithm; Artificial intelligence; Statistics; Combinatorics","score_opus":0.07200270670629584,"score_gpt":0.19067611599449494,"score_spread":0.1186734092881991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033768482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045470353,0.00014761166,0.98820484,0.0012973514,0.00046345638,0.00031877242,0.000023239485,0.0006257335,0.0043719695],"genre_scores_gemma":[0.99476993,0.000091366936,0.003914302,0.000337693,0.00014087826,0.0000028385625,0.000015045969,0.000029099701,0.0006988563],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971536,0.00007354891,0.0002863823,0.0017767878,0.0001673208,0.00054239365],"domain_scores_gemma":[0.9975856,0.000100699945,0.000371684,0.0012516285,0.0002765924,0.0004138093],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011254774,0.00050967035,0.0004916174,0.00022795441,0.00021226003,0.00043192971,0.0036984466,0.0003201521,0.000056950543],"category_scores_gemma":[0.00013456981,0.0005490831,0.00017783356,0.0013761015,0.00013980942,0.0006300739,0.0026082525,0.00083463703,0.00036972106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055782777,0.00023594455,0.0005874145,0.0019360508,0.00018944904,0.0012785967,0.0013490274,0.0833822,0.000032790005,0.9071285,0.0016081098,0.0022161233],"study_design_scores_gemma":[0.00060963474,0.00018345112,0.0008405816,0.0005345963,0.00013070791,0.000027216938,0.00015431512,0.53178734,0.00015699986,0.46324477,0.0008290898,0.0015013175],"about_ca_topic_score_codex":0.000068911715,"about_ca_topic_score_gemma":0.000037999092,"teacher_disagreement_score":0.9902229,"about_ca_system_score_codex":0.00012446588,"about_ca_system_score_gemma":0.0012336622,"threshold_uncertainty_score":0.9996961},"labels":[],"label_agreement":null},{"id":"W3034920557","doi":"","title":"Scalable Exact Inference in Multi-Output Gaussian Processes","year":2020,"lang":"en","type":"article","venue":"Aaltodoc (Aalto University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Invenia (Canada)","funders":"Natural Environment Research Council; Engineering and Physical Sciences Research Council; DeepMind","keywords":"Computer science; Inference; Scalability; Gaussian process; Gaussian; Artificial intelligence; Database; Physics","score_opus":0.038268157967115045,"score_gpt":0.2367218225741761,"score_spread":0.19845366460706104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034920557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014218147,0.00018127116,0.95247585,0.0063709347,0.00014712565,0.00036873535,0.000010858912,0.0005347944,0.02569227],"genre_scores_gemma":[0.9767105,0.00015900792,0.020515338,0.0008435956,0.000036335143,0.0000017397776,0.000003817214,0.000015030744,0.0017146285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979703,0.00007824186,0.00025997867,0.0008081326,0.00029269987,0.0005906634],"domain_scores_gemma":[0.9986969,0.00014399824,0.00014971715,0.0004448952,0.00016638587,0.0003980517],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000108023116,0.00028071477,0.0003239305,0.00034362098,0.00017792944,0.00022166087,0.0019370071,0.00012714586,0.00007573441],"category_scores_gemma":[0.00028528686,0.00029202775,0.00006309172,0.003176972,0.00009620973,0.0017796068,0.00056322746,0.00034228718,0.0002979733],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003818239,0.0020624588,0.19595243,0.003595441,0.00019790765,0.006080293,0.024559624,0.0033070913,0.0023974197,0.64622253,0.0054037776,0.10983918],"study_design_scores_gemma":[0.014749134,0.0024821165,0.107744776,0.0018009809,0.0001405981,0.00010061549,0.0042490517,0.24472497,0.015811889,0.009898264,0.5911518,0.007145832],"about_ca_topic_score_codex":0.00016599071,"about_ca_topic_score_gemma":0.00019184715,"teacher_disagreement_score":0.96249235,"about_ca_system_score_codex":0.00009759232,"about_ca_system_score_gemma":0.00066913967,"threshold_uncertainty_score":0.9999532},"labels":[],"label_agreement":null},{"id":"W3037907432","doi":"","title":"Safe-Bayesian Generalized Linear Regression","year":2020,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Generalized linear model; Bayesian linear regression; Bayes' theorem; Bayesian inference; Bayes factor; Mathematics; Bayesian probability; Lasso (programming language); Prior probability; Maximum a posteriori estimation; Applied mathematics; Logistic regression; Statistics; Computer science; Maximum likelihood","score_opus":0.027631465228368235,"score_gpt":0.2560761134889826,"score_spread":0.2284446482606144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037907432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032568775,0.00080297276,0.95887643,0.0045800945,0.00022052183,0.00017221221,0.00008452052,0.0003494392,0.002345003],"genre_scores_gemma":[0.85074115,0.0006807628,0.14431715,0.0031463015,0.00062308594,0.0000074204854,0.0003726123,0.000027685694,0.000083860075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978763,0.000134048,0.0003266524,0.00094011845,0.00027310182,0.00044981806],"domain_scores_gemma":[0.9981377,0.00009686432,0.00015898919,0.0012131026,0.000026983736,0.00036636053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024267427,0.00027700828,0.00029779997,0.000048768983,0.00035335263,0.00034759485,0.003330325,0.000066607194,0.00001626435],"category_scores_gemma":[0.00001320881,0.00021108722,0.00003818853,0.00044800845,0.000060961538,0.00089933537,0.0022510793,0.00026101567,0.000029031715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047210642,0.0003135709,0.045753125,0.003274502,0.0003225998,0.00053447107,0.041537803,0.0030992697,0.009462519,0.043838233,0.014485376,0.83690643],"study_design_scores_gemma":[0.00035703523,0.000070492664,0.0037090508,0.00026365212,0.000017117174,0.000024013716,0.00008696022,0.97818893,0.00014087431,0.0019463405,0.014875703,0.00031985046],"about_ca_topic_score_codex":0.00011723852,"about_ca_topic_score_gemma":0.00008970883,"teacher_disagreement_score":0.97508967,"about_ca_system_score_codex":0.0000066990046,"about_ca_system_score_gemma":0.000051987015,"threshold_uncertainty_score":0.86078864},"labels":[],"label_agreement":null},{"id":"W3045212685","doi":"10.48550/arxiv.2007.10417","title":"Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Softmax function; Overfitting; Artificial intelligence; Bayesian probability; Classifier (UML); Computer science; Gaussian process; Machine learning; Gaussian; One shot; Posterior probability; Single shot; Shot (pellet); Pattern recognition (psychology); Deep learning; Physics","score_opus":0.11208096910584989,"score_gpt":0.2043536822460003,"score_spread":0.09227271314015043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045212685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009583072,0.00007312286,0.9745954,0.0029867496,0.00024213802,0.000685767,0.00003188288,0.0007573547,0.011044481],"genre_scores_gemma":[0.9886044,0.00023347468,0.00957652,0.00035262294,0.00013808827,0.000010719697,0.00008802428,0.000053960757,0.00094215217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995838,0.00015372358,0.0004410706,0.0024997524,0.00034302555,0.00072444347],"domain_scores_gemma":[0.9963742,0.000087510925,0.0007139188,0.0017799741,0.0005036048,0.0005408171],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018365406,0.00072915666,0.0006791664,0.00037553514,0.00036997927,0.00066562864,0.0035779073,0.0004550029,0.00008259484],"category_scores_gemma":[0.00006969142,0.0007375475,0.00016660246,0.002183952,0.00024485108,0.0010322207,0.0014453741,0.0010759666,0.00015727921],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008944249,0.0015551463,0.021466961,0.0060361917,0.0009995644,0.0020856832,0.0033753794,0.015676467,0.000592609,0.93816835,0.002287884,0.006861315],"study_design_scores_gemma":[0.0034211944,0.0013927105,0.026280658,0.0025959534,0.00072028686,0.00008774451,0.00087964494,0.80385137,0.0027508128,0.1486765,0.0045938375,0.00474931],"about_ca_topic_score_codex":0.00009233801,"about_ca_topic_score_gemma":0.0001453163,"teacher_disagreement_score":0.9790214,"about_ca_system_score_codex":0.00028998262,"about_ca_system_score_gemma":0.0015271165,"threshold_uncertainty_score":0.99950755},"labels":[],"label_agreement":null},{"id":"W3048046124","doi":"10.1109/tie.2020.3013798","title":"Learning-Based Terrain Identification With Proprioceptive Sensors for Mobile Robots","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for Central Universities of the Central South University; National Natural Science Foundation of China","keywords":"Terrain; Computer science; Artificial intelligence; Torque; Mobile robot; Gaussian process; Identification (biology); SIGNAL (programming language); Robot; Hyperparameter; Machine learning; Computer vision; Gaussian; Control theory (sociology); Simulation","score_opus":0.024191664211596742,"score_gpt":0.24267485595651583,"score_spread":0.2184831917449191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048046124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007866116,0.000012940577,0.98870265,0.0020959799,0.00014800183,0.0008716463,0.000011803562,0.00025026134,0.000040582247],"genre_scores_gemma":[0.99537116,0.000005298343,0.0037952373,0.00019109967,0.00009309611,0.0003420639,0.0000057474904,0.000020593929,0.00017570595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998443,0.00007156073,0.00027246217,0.0005370441,0.00027527372,0.00040065934],"domain_scores_gemma":[0.9991976,0.000098711294,0.00016476592,0.0002451486,0.0001523827,0.00014139243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015354968,0.00020789461,0.0001972061,0.00009380168,0.00035475942,0.00020364691,0.00043763575,0.00017008296,0.000019449193],"category_scores_gemma":[0.000016232869,0.00018334697,0.00009168374,0.0007083193,0.00005076291,0.00030822685,0.0000014399064,0.0006572609,0.00002464263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005621197,0.00026451316,0.000007047706,0.000038713926,0.000084619765,0.000004176606,0.001253647,0.8856207,0.0030413847,0.0012641967,0.00031568162,0.10754321],"study_design_scores_gemma":[0.0035962767,0.0096326275,0.000007478281,0.00006511533,0.00008791958,0.000010865083,0.00016606101,0.68613124,0.2907534,0.00052054145,0.008358079,0.0006703675],"about_ca_topic_score_codex":0.000010420098,"about_ca_topic_score_gemma":0.000018849065,"teacher_disagreement_score":0.987505,"about_ca_system_score_codex":0.00012551356,"about_ca_system_score_gemma":0.0006160449,"threshold_uncertainty_score":0.74766725},"labels":[],"label_agreement":null},{"id":"W3081341002","doi":"10.1109/mis.2022.3169036","title":"Fast Approximate Multioutput Gaussian Processes","year":2022,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaussian process; Eigenvalues and eigenvectors; Hyperparameter; Covariance matrix; Covariance; Kernel (algebra); Mathematics; Kriging; Algorithm; Computational complexity theory; Inverse; Gaussian; Kernel method; Applied mathematics; Artificial intelligence; Computer science; Machine learning; Statistics; Discrete mathematics; Support vector machine","score_opus":0.023967515693635897,"score_gpt":0.2454106282888358,"score_spread":0.22144311259519991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081341002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022977726,0.0009774388,0.9865554,0.0004298802,0.0031955724,0.0005765032,0.000026627451,0.0004984479,0.0054423697],"genre_scores_gemma":[0.9937303,0.000042730368,0.0022756364,0.00018139057,0.00019310854,0.0004636451,0.000007259193,0.000028017983,0.0030778954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972163,0.00013771078,0.0005841754,0.00073268946,0.0007519462,0.00057716575],"domain_scores_gemma":[0.9984728,0.000077331926,0.00030037327,0.00078665616,0.00017453992,0.00018828861],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042484692,0.00029071697,0.00032414394,0.00022712612,0.0005648898,0.0005529534,0.00207167,0.000056011533,0.00007740426],"category_scores_gemma":[0.000034070803,0.00026266964,0.00008889444,0.0010618564,0.000051259856,0.00047174896,0.0003791366,0.00031563462,0.0002346352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015989486,0.0033722064,0.008825117,0.008894373,0.00061060244,0.0012601425,0.043711253,0.12230488,0.005319171,0.61534303,0.048941307,0.14125803],"study_design_scores_gemma":[0.0011202595,0.0012071341,0.000269338,0.00058113306,0.000056614448,0.002043664,0.0063572978,0.65637875,0.031726047,0.0088312905,0.28824723,0.0031812345],"about_ca_topic_score_codex":0.00014813474,"about_ca_topic_score_gemma":0.000009098383,"teacher_disagreement_score":0.99143255,"about_ca_system_score_codex":0.00015997919,"about_ca_system_score_gemma":0.00029216197,"threshold_uncertainty_score":0.99998254},"labels":[],"label_agreement":null},{"id":"W3084829941","doi":"10.1016/j.cma.2021.114230","title":"Learning Quantities of Interest from dynamical systems for observation-consistent inversion","year":2021,"lang":"en","type":"preprint","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Dynamical systems theory; Series (stratigraphy); Observable; Computer science; Measure (data warehouse); Variety (cybernetics); Probability distribution; Algorithm; Statistical physics; Mathematics; Data mining; Artificial intelligence; Physics; Statistics","score_opus":0.0697067160064772,"score_gpt":0.2914683086390259,"score_spread":0.2217615926325487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084829941","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01035029,0.00083334773,0.986868,0.00003793465,0.0015290222,0.00028242674,0.0000063950647,0.000080105674,0.00001247266],"genre_scores_gemma":[0.22515261,0.00020947793,0.7744558,0.000015148846,0.00006132333,0.00005811362,0.000028016608,0.000017069051,0.0000024714489],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847823,0.00006122588,0.0005243669,0.000603945,0.000106692256,0.00022551534],"domain_scores_gemma":[0.9987842,0.0004606668,0.0002169344,0.00038251976,0.00008571452,0.000069973976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057775597,0.00026572443,0.0005996928,0.00018502577,0.000038279697,0.00029881933,0.00055221515,0.0002625157,7.707561e-7],"category_scores_gemma":[0.000050605795,0.00028391858,0.00008808754,0.00017447477,0.000008592415,0.000078855825,0.0014200959,0.00049934856,1.19068645e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063507596,0.000032108415,0.000026646117,0.0021755607,0.00007664664,0.000004869291,0.00096086576,0.13330944,0.005189765,0.8181335,0.0000066279576,0.040077645],"study_design_scores_gemma":[0.00020071048,0.000035573146,0.00006700602,0.00087512244,0.000015763835,0.0000019624367,0.00011919995,0.9770851,0.000849124,0.02037165,0.0001109508,0.00026783565],"about_ca_topic_score_codex":0.00004989905,"about_ca_topic_score_gemma":0.0000037485433,"teacher_disagreement_score":0.84377563,"about_ca_system_score_codex":0.000061986044,"about_ca_system_score_gemma":0.000074316325,"threshold_uncertainty_score":0.9999613},"labels":[],"label_agreement":null},{"id":"W3089728503","doi":"10.1073/pnas.2020397118","title":"Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes","year":2021,"lang":"en","type":"preprint","venue":"Proceedings of the National Academy of Sciences","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; National Science Foundation","keywords":"Ode; Gaussian process; Inference; Computer science; Manifold (fluid mechanics); Nonlinear system; Bayesian inference; Ordinary differential equation; Mathematical optimization; Statistical inference; Gaussian; Constraint (computer-aided design); Algorithm; Applied mathematics; Bayesian probability; Mathematics; Artificial intelligence; Differential equation","score_opus":0.05720060513015881,"score_gpt":0.3083602427655091,"score_spread":0.2511596376353503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089728503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94138265,0.010747577,0.025422482,0.0103151025,0.000600799,0.002029417,0.0015978573,0.00017911245,0.007725013],"genre_scores_gemma":[0.97001535,0.00041249522,0.02939764,0.00006551748,0.00005217163,0.000017403316,0.000004941135,0.000007404966,0.000027068088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957732,0.000020617234,0.00094581814,0.0011704649,0.0018095713,0.00028034273],"domain_scores_gemma":[0.9964343,0.0003192214,0.0020153918,0.00011823089,0.0010226949,0.00009017607],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0012924671,0.0003176586,0.0006159706,0.00028272104,0.00017984604,0.0003933465,0.007281069,0.000292964,0.000007995692],"category_scores_gemma":[0.0010458545,0.00023020417,0.00007305735,0.0012750926,0.0011127414,0.0016079632,0.005043804,0.00050023064,5.3089923e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074041134,0.0010942041,0.03853915,0.030818405,0.0008082828,0.0000014686321,0.0070084813,0.0013990175,0.22398914,0.6726705,0.0006039443,0.02299339],"study_design_scores_gemma":[0.0006381046,0.00016430726,0.11053826,0.009621459,0.00018581099,0.000107444765,0.0012414452,0.41472402,0.058858756,0.4026708,0.00006111862,0.0011884574],"about_ca_topic_score_codex":0.0001414572,"about_ca_topic_score_gemma":0.0000026367484,"teacher_disagreement_score":0.413325,"about_ca_system_score_codex":0.000036412217,"about_ca_system_score_gemma":0.0007269064,"threshold_uncertainty_score":0.99809},"labels":[],"label_agreement":null},{"id":"W3098079921","doi":"","title":"Machine Learning Corrected Quantum Dynamics Calculations","year":2020,"lang":"en","type":"article","venue":"Digital Scholarship - UNLV (University of Nevada Reno)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration; National Science Foundation","keywords":"Observable; Hamiltonian (control theory); Statistical physics; Quantum; Quantum dynamics; Computer science; Scattering; Physics; Quantum mechanics; Mathematics; Mathematical optimization","score_opus":0.020043188713543218,"score_gpt":0.19430127513159345,"score_spread":0.17425808641805024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098079921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08906492,0.00014357778,0.89576745,0.0040684477,0.00011388281,0.00015293293,0.00009285703,0.00040824877,0.010187673],"genre_scores_gemma":[0.99610394,0.000016108192,0.0032957343,0.00010944552,0.0000135628825,9.7467336e-8,0.000089160305,0.00000921105,0.00036271277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988374,0.000046524678,0.0001486525,0.00039054986,0.00032142858,0.00025542846],"domain_scores_gemma":[0.99910164,0.000067906876,0.00016843264,0.00025152898,0.00015849872,0.0002520067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077858655,0.00015512975,0.00021372712,0.00009352008,0.00029549896,0.00029184856,0.0010327213,0.00009112086,0.000059934806],"category_scores_gemma":[0.00016254395,0.00018219539,0.00009673642,0.0009192191,0.00008610495,0.0025333026,0.00043052275,0.00041761392,0.00009088764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017383716,0.00039660482,0.10678435,0.00026787564,0.00020113977,0.00043333686,0.006319549,0.0018502044,0.000805466,0.6338002,0.00052105205,0.24844638],"study_design_scores_gemma":[0.0010970155,0.0004983028,0.048460186,0.0001052497,0.000045809156,0.000036981393,0.0011449993,0.9227871,0.000057733312,0.010759931,0.014277234,0.00072947313],"about_ca_topic_score_codex":0.000101734324,"about_ca_topic_score_gemma":0.000065093525,"teacher_disagreement_score":0.9209369,"about_ca_system_score_codex":0.000057040168,"about_ca_system_score_gemma":0.00011666848,"threshold_uncertainty_score":0.74297124},"labels":[],"label_agreement":null},{"id":"W3106482969","doi":"10.48550/arxiv.2011.06058","title":"Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Staffing; Computer science; Emergency department; Capacity planning; Operations management; Health care; Operations research; Medical emergency; Medicine; Engineering; Economics; Nursing","score_opus":0.1582888165620912,"score_gpt":0.21568984797445873,"score_spread":0.05740103141236752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106482969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038079597,0.000020956168,0.95832133,0.0004048947,0.0006585568,0.00058752607,0.0000981313,0.00020781657,0.0016211943],"genre_scores_gemma":[0.97556525,0.00004058294,0.0239879,0.00011784434,0.00010165287,0.0000058401197,0.00004427749,0.000014558331,0.00012211698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979972,0.000060980758,0.00030348112,0.0011481976,0.00009575073,0.00039435382],"domain_scores_gemma":[0.9984133,0.000076440694,0.0004089368,0.00059306587,0.00022874249,0.00027948924],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018708775,0.00032301262,0.00032182457,0.00012336245,0.0002680991,0.0001220867,0.0013128867,0.00023019558,0.00006908818],"category_scores_gemma":[0.000112643495,0.0003756445,0.00023019245,0.00042367313,0.00010083541,0.00038613012,0.00094038656,0.00035156726,0.000023701006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006427499,0.00015937914,0.007916257,0.0010719417,0.00022321238,0.00016530453,0.0010110283,0.028415905,0.00009647966,0.9532805,0.0014803266,0.0061153886],"study_design_scores_gemma":[0.00032177128,0.00009098387,0.0004031225,0.000056638084,0.000055212917,0.000005990658,0.000032303167,0.7398819,0.000116215895,0.258192,0.0004469696,0.00039690384],"about_ca_topic_score_codex":0.000029319412,"about_ca_topic_score_gemma":0.000034636283,"teacher_disagreement_score":0.93748564,"about_ca_system_score_codex":0.0001789956,"about_ca_system_score_gemma":0.00035668668,"threshold_uncertainty_score":0.9998695},"labels":[],"label_agreement":null},{"id":"W3110258702","doi":"10.48550/arxiv.2011.11682","title":"Efficient Construction of Nonlinear Models over Normalized Data","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Joins; Computer science; Redundancy (engineering); Computation; Artificial neural network; Factoring; Binary number; Nonlinear system; Data mining; Mixture model; Relational database; Machine learning; Artificial intelligence; Theoretical computer science; Algorithm; Mathematics","score_opus":0.11205083216256821,"score_gpt":0.20771335792670448,"score_spread":0.09566252576413627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110258702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06001487,0.00003570085,0.9371566,0.00012411558,0.00037139156,0.00018564833,0.00015063075,0.00013663343,0.0018243785],"genre_scores_gemma":[0.94857305,0.00009374344,0.05111261,0.000055231,0.00004878457,2.5972514e-7,0.00005386342,0.000010943897,0.000051491654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804926,0.00006499976,0.00028340117,0.0012094195,0.00014488565,0.00024806074],"domain_scores_gemma":[0.9971957,0.000043676,0.00041653754,0.0020138714,0.00017462047,0.00015560028],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001438319,0.00026008545,0.00038596493,0.00015608003,0.00007675711,0.00009069666,0.0035495611,0.00020478883,0.000030919553],"category_scores_gemma":[0.000026457194,0.0002843302,0.00011594218,0.00062817987,0.00015831683,0.0003611607,0.0053993687,0.0004156338,0.000022233951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043476277,0.000103686216,0.00046054553,0.00030558536,0.000085436004,0.00010821811,0.00018674988,0.40155962,0.000059138474,0.59614724,0.00012895431,0.00081134087],"study_design_scores_gemma":[0.00037756254,0.000027967715,0.00014171371,0.00009211681,0.000052486983,0.0000053527065,0.000024573272,0.945008,0.00014711036,0.053757463,0.000094837706,0.00027082174],"about_ca_topic_score_codex":0.00012850977,"about_ca_topic_score_gemma":0.0000071741906,"teacher_disagreement_score":0.8885582,"about_ca_system_score_codex":0.00005439478,"about_ca_system_score_gemma":0.00042811778,"threshold_uncertainty_score":0.9999609},"labels":[],"label_agreement":null},{"id":"W3116747493","doi":"","title":"All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference.","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Exponential family; Inference; Estimator; Computer science; Grid; Applied mathematics; Exponential function; Hyperparameter optimization; Upper and lower bounds; Mathematical optimization; Mathematics; Artificial intelligence; Statistics; Mathematical analysis","score_opus":0.06415591807818004,"score_gpt":0.3211540639702533,"score_spread":0.25699814589207326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116747493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27192613,0.000056014436,0.5707404,0.10245012,0.0005671904,0.00043789926,0.00002205481,0.00022689415,0.053573295],"genre_scores_gemma":[0.9943931,0.00003263059,0.0013948717,0.0039627072,0.000094494884,0.00002979019,0.00004389091,0.000008423764,0.000040058763],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781734,0.00032405512,0.00040756035,0.0005148248,0.0006579698,0.0002782809],"domain_scores_gemma":[0.99923193,0.00020900284,0.00017465852,0.0002169293,0.000098420605,0.0000690697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005175888,0.00020703855,0.00018781592,0.00017284587,0.00007479164,0.00042905528,0.0018669354,0.00007556414,0.0002454061],"category_scores_gemma":[0.0002607211,0.00016262432,0.000058304384,0.00039675692,0.000042607582,0.00049531803,0.0002483976,0.00086512585,0.000097580996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045561093,0.00012994569,0.02455979,0.000012564918,0.000018256307,0.00006643475,0.004650504,0.002805186,0.0009035618,0.9574481,0.000024889374,0.009335207],"study_design_scores_gemma":[0.0005724846,0.000101518126,0.16557278,0.000048414433,0.0000020545083,0.0000061439982,0.0001740331,0.80355406,0.0000143079515,0.029080568,0.0006680709,0.00020559655],"about_ca_topic_score_codex":0.00042699877,"about_ca_topic_score_gemma":0.00015530181,"teacher_disagreement_score":0.92836756,"about_ca_system_score_codex":0.000062804895,"about_ca_system_score_gemma":0.00015520422,"threshold_uncertainty_score":0.6631627},"labels":[],"label_agreement":null},{"id":"W3124376910","doi":"","title":"Kernel-Based Copula Processes","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Copula (linguistics); Computer science; Heteroscedasticity; Interdependence; Gaussian; Gaussian process; Kernel (algebra); Series (stratigraphy); Algorithm; Econometrics; Mathematics; Machine learning; Discrete mathematics; Physics","score_opus":0.006303209047814256,"score_gpt":0.23073505264124228,"score_spread":0.22443184359342802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124376910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054438,0.0023486603,0.98385906,0.005825215,0.00012393965,0.000072195624,3.868663e-7,0.00013733302,0.0021894255],"genre_scores_gemma":[0.9929379,0.00060546317,0.0048998003,0.0009223251,0.00014974269,0.0000023907533,8.140055e-7,0.000007908791,0.0004736445],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99708194,0.000035989047,0.0002591912,0.00029204416,0.00033882022,0.001992022],"domain_scores_gemma":[0.9991863,0.000029159628,0.00016922138,0.00028894973,0.0001973655,0.00012902074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061038794,0.00017756435,0.00016673778,0.00012439236,0.00025540494,0.00035741448,0.0012607316,0.00006458175,0.000012928596],"category_scores_gemma":[0.00008271261,0.00014414347,0.0000720648,0.00063166826,0.000027217373,0.0006327158,0.00003027138,0.0010376297,0.000054793214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017235878,0.00015093418,0.000614343,0.000021469777,0.000021930762,0.000022960421,0.000106448875,0.00008499178,0.00016802408,0.83771056,0.00026187068,0.16081923],"study_design_scores_gemma":[0.0005852848,0.00079376856,0.0010344333,0.00005651774,0.000010680063,0.00069725164,0.00006429127,0.0025224313,0.0010704164,0.9907187,0.0021290875,0.000317104],"about_ca_topic_score_codex":0.0000048265965,"about_ca_topic_score_gemma":0.000044848977,"teacher_disagreement_score":0.9874941,"about_ca_system_score_codex":0.00027370805,"about_ca_system_score_gemma":0.0057778265,"threshold_uncertainty_score":0.9998585},"labels":[],"label_agreement":null},{"id":"W3126264610","doi":"10.1109/cavs51000.2020.9334636","title":"A Probabilistic Model for Visual Driver Gaze Approximation from Head Pose Estimation","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Gaze; Computer science; Probabilistic logic; Computer vision; Artificial intelligence; Advanced driver assistance systems; Process (computing); Gaussian process; Head (geology); Kriging; Situation awareness; Interval (graph theory); Eye tracking; Visual search; Visual angle; Human–computer interaction; Gaussian; Machine learning; Engineering; Mathematics","score_opus":0.03553313601755526,"score_gpt":0.28033562955458013,"score_spread":0.24480249353702488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126264610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006258688,0.000010765063,0.9888593,0.0037968082,0.000050297214,0.00038480104,0.0000084868825,0.00022618636,0.0004046882],"genre_scores_gemma":[0.5789809,6.544541e-7,0.42026317,0.00062243687,0.00003365076,0.000038985065,0.000015538337,0.0000047104204,0.00003992571],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904954,0.000012438477,0.00020606749,0.00038889016,0.00017080101,0.00017224427],"domain_scores_gemma":[0.9994897,0.00005561372,0.00007474356,0.00017058493,0.00009980759,0.000109538785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056678375,0.00011765862,0.0001277223,0.00002656337,0.0000779972,0.00021390247,0.0003737645,0.000050800147,0.000018672054],"category_scores_gemma":[0.000103069244,0.00010057305,0.000043426815,0.00019139185,0.000019421146,0.0007109187,0.000091231326,0.000056281024,0.000048918726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006356188,0.00024364617,0.00010699846,0.00033999974,0.000026891592,0.0000028960737,0.0060331253,0.06699368,0.0023850284,0.7679142,0.0025786485,0.15331134],"study_design_scores_gemma":[0.00026704438,0.00008465898,0.00014307295,0.00001441977,0.0000061459505,5.970703e-7,0.000007899289,0.8611578,0.00051047315,0.13766095,0.000024352428,0.00012257787],"about_ca_topic_score_codex":0.000012355411,"about_ca_topic_score_gemma":0.0000062699637,"teacher_disagreement_score":0.7941641,"about_ca_system_score_codex":0.000023067874,"about_ca_system_score_gemma":0.00010616943,"threshold_uncertainty_score":0.41012502},"labels":[],"label_agreement":null},{"id":"W3128955336","doi":"10.48550/arxiv.2102.05208","title":"Attentive Gaussian processes for probabilistic time-series generation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Kernel (algebra); Sequence (biology); Probabilistic logic; Computation; Range (aeronautics); Scalability; Artificial neural network; Artificial intelligence; Gaussian process; Machine learning; Block (permutation group theory); Representation (politics); Process (computing); Algorithm; Gaussian; Mathematics","score_opus":0.06194783974044825,"score_gpt":0.18968476813308566,"score_spread":0.1277369283926374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128955336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022297885,0.00014581419,0.974724,0.0005033978,0.00042208366,0.0006338783,0.000041044208,0.00022805633,0.0010038583],"genre_scores_gemma":[0.97555345,0.00013793282,0.020429209,0.000084358675,0.00017582596,0.000017036782,0.00013722785,0.000023631395,0.0034413042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777764,0.00007168002,0.00024570912,0.0014221373,0.00009118388,0.00039162836],"domain_scores_gemma":[0.99781996,0.00007636443,0.00030780028,0.0008865962,0.00076466834,0.00014462498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014474695,0.00036901256,0.00038512365,0.00014996578,0.00027909662,0.0006321957,0.0013765717,0.00026693678,0.000037649846],"category_scores_gemma":[0.00016795447,0.00039746225,0.00016763978,0.0006956051,0.00011946549,0.0009772772,0.0010124547,0.00027517386,0.000031790027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013436117,0.0008533599,0.0022693705,0.006963278,0.0005682565,0.00057928276,0.0029365565,0.14231591,0.0011234229,0.834267,0.003149852,0.004839319],"study_design_scores_gemma":[0.0008407679,0.00033087883,0.0010663115,0.00067484664,0.0002653197,0.00003387894,0.00021046457,0.8151833,0.0028179786,0.17566203,0.00116537,0.0017488898],"about_ca_topic_score_codex":0.000018805893,"about_ca_topic_score_gemma":0.00009138912,"teacher_disagreement_score":0.9542948,"about_ca_system_score_codex":0.00014175041,"about_ca_system_score_gemma":0.0011078366,"threshold_uncertainty_score":0.9998477},"labels":[],"label_agreement":null},{"id":"W3129829742","doi":"10.1109/icdmw51313.2020.00082","title":"SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; sync; Copula (linguistics); Data mining; Scalability; Synthetic data; Machine learning; Artificial intelligence; Database","score_opus":0.07637689567952773,"score_gpt":0.2882800888457438,"score_spread":0.2119031931662161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129829742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033789717,0.00020603988,0.98885566,0.006705514,0.00013304055,0.0001827712,0.000058537793,0.00031146026,0.00016803115],"genre_scores_gemma":[0.45932326,0.0000024203339,0.53717625,0.0033114234,0.00011478188,0.000011728264,0.000037060472,0.00000897607,0.000014110346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998433,0.000036433903,0.00025405278,0.0007911633,0.00020432586,0.00028103654],"domain_scores_gemma":[0.9982324,0.0003874415,0.0001236715,0.0010103752,0.00006944473,0.00017665653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011947759,0.00016664757,0.00020613045,0.000026553607,0.00016913988,0.00060575956,0.0023626615,0.00008794067,0.00019607026],"category_scores_gemma":[0.00061158044,0.00013643573,0.000037094538,0.000334553,0.00003256144,0.00045550292,0.00048024143,0.00013411688,0.00006685307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021379869,0.0006634041,0.008893566,0.001201057,0.00044557024,0.00021191152,0.005656293,0.0042280103,0.022809325,0.5861873,0.06660952,0.30288023],"study_design_scores_gemma":[0.00018419093,0.00007246054,0.000047480524,0.00009351102,0.000012677969,0.0000011438143,0.000033194163,0.98324925,0.00449813,0.010115205,0.0014786235,0.00021412116],"about_ca_topic_score_codex":0.00005011272,"about_ca_topic_score_gemma":0.0000090582935,"teacher_disagreement_score":0.97902125,"about_ca_system_score_codex":0.0000080637055,"about_ca_system_score_gemma":0.00013115593,"threshold_uncertainty_score":0.58413535},"labels":[],"label_agreement":null},{"id":"W3132413728","doi":"10.48550/arxiv.2102.06559","title":"Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Stochastic differential equation; Estimator; Inference; Computer science; Bayesian probability; Ode; Bayesian inference; Stochastic neural network; Applied mathematics; Posterior probability; Algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Recurrent neural network; Statistics","score_opus":0.0396526418469261,"score_gpt":0.1778123489010236,"score_spread":0.1381597070540975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132413728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02651799,0.000083161714,0.9717757,0.00010842992,0.0005439853,0.00024811228,0.0000058584596,0.00026006668,0.00045669504],"genre_scores_gemma":[0.9959665,0.000024104473,0.003460585,0.00010510216,0.00012425063,0.000003251357,0.000049703784,0.000027066975,0.0002394562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975384,0.00012770567,0.0002622377,0.0013463541,0.00016373575,0.00056160206],"domain_scores_gemma":[0.99762666,0.00017007433,0.00032020226,0.0013048698,0.00028441142,0.00029376702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007087211,0.000471691,0.00043813128,0.00024998383,0.00029177248,0.0006974878,0.001958168,0.00031551303,0.00010870968],"category_scores_gemma":[0.00003513859,0.00047541127,0.00018917577,0.0010074582,0.00015148368,0.0005877989,0.0017797266,0.0009316956,0.000013146052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021285097,0.000094665746,0.0003939617,0.000055083903,0.000090592664,0.0003899851,0.00022927586,0.91767085,0.000002026862,0.07945061,0.000009923803,0.001591743],"study_design_scores_gemma":[0.00040061047,0.00008617396,0.0010810873,0.00013381512,0.000108321714,0.000017296901,0.00007559486,0.9909402,0.0000045153242,0.0065737343,0.0000037816746,0.00057483267],"about_ca_topic_score_codex":0.00008182544,"about_ca_topic_score_gemma":0.00025675347,"teacher_disagreement_score":0.9694485,"about_ca_system_score_codex":0.00010862473,"about_ca_system_score_gemma":0.00034537617,"threshold_uncertainty_score":0.99976975},"labels":[],"label_agreement":null},{"id":"W3132892856","doi":"","title":"Comparing Inverse Optimization and Machine Learning Methods for Imputing a Convex Objective Function","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Bayesian optimization; Correctness; Optimization problem; Gaussian process; Set (abstract data type); Support vector machine; Mathematical optimization; Algorithm; Gaussian; Mathematics","score_opus":0.08459372874696396,"score_gpt":0.22873783162252231,"score_spread":0.14414410287555834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132892856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020992171,0.00015943481,0.9775964,0.000036612273,0.0003571719,0.00026461395,0.0000017401716,0.0001812242,0.00041063258],"genre_scores_gemma":[0.775554,0.000113783055,0.22406818,0.000047377984,0.0000321886,0.0000017853882,0.000025158712,0.000012692663,0.00014486174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998306,0.0001800473,0.00018895291,0.0010079605,0.000047417845,0.00026964897],"domain_scores_gemma":[0.99870914,0.00020438443,0.00032649943,0.00036619307,0.00027828515,0.000115484894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043718787,0.00025684183,0.00036803063,0.0001955355,0.0003264954,0.00035350156,0.0004716158,0.00020612095,0.000010066432],"category_scores_gemma":[0.00012495699,0.00030427874,0.000113129776,0.00046534586,0.00006140791,0.0005147818,0.0013937922,0.00051986286,0.000001299271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033491997,0.000031935935,0.006089017,0.0002802758,0.000110931345,0.000020888545,0.00076207705,0.9534618,0.000081102764,0.03394005,0.0000058249625,0.0051825885],"study_design_scores_gemma":[0.00046789096,0.00006866816,0.0005117447,0.00012426576,0.00008669435,0.0000069372813,0.00027983563,0.9884743,0.00020805694,0.009411807,0.000043692715,0.00031607924],"about_ca_topic_score_codex":0.00011966338,"about_ca_topic_score_gemma":0.00003302048,"teacher_disagreement_score":0.7545618,"about_ca_system_score_codex":0.00011935387,"about_ca_system_score_gemma":0.00018825129,"threshold_uncertainty_score":0.99994093},"labels":[],"label_agreement":null},{"id":"W3139791528","doi":"10.1109/iccspa49915.2021.9385767","title":"Spatiotemporal Prediction Using Hierarchical Bayesian Modeling","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Gaussian process; Covariance; Bayesian probability; Kriging; Computer science; Gaussian; Artificial intelligence; Covariance function; Pattern recognition (psychology); Data mining; Exponential function; Bayesian inference; Algorithm; Machine learning; Mathematics; Statistics","score_opus":0.030548386031057567,"score_gpt":0.2570726136288241,"score_spread":0.22652422759776655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139791528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010443653,0.000047026344,0.98265123,0.0013728959,0.00021320372,0.00003830514,0.000001446297,0.00017720483,0.005055002],"genre_scores_gemma":[0.69379413,0.0000055777036,0.30580583,0.00019719789,0.00006590806,0.0000014591471,0.0000028076493,0.000004499984,0.00012261656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989198,0.000036688605,0.0002149126,0.00037190304,0.00023003262,0.00022666367],"domain_scores_gemma":[0.9993753,0.00001380294,0.000033057222,0.00035212297,0.000117188785,0.00010857393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010043105,0.00009924781,0.00010785787,0.000057066358,0.00014149369,0.0002873434,0.00030692972,0.00006395919,0.00007170545],"category_scores_gemma":[0.000025735611,0.00009103285,0.00004742461,0.00039589402,0.000016684236,0.00068766164,0.00018106822,0.00015830125,0.000012401835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008456291,0.0002315493,0.0061947866,0.00010456559,0.00004130905,0.00023915332,0.0008151332,0.028918324,0.005308401,0.8763875,0.00036837606,0.081382476],"study_design_scores_gemma":[0.00010388726,0.000015858992,0.000180371,0.00002184417,0.0000029626633,0.00007526827,0.000015952277,0.96268064,0.0017017737,0.03499646,0.00010071309,0.000104248305],"about_ca_topic_score_codex":0.0000398256,"about_ca_topic_score_gemma":0.000012765913,"teacher_disagreement_score":0.9337623,"about_ca_system_score_codex":0.000029870116,"about_ca_system_score_gemma":0.00031804864,"threshold_uncertainty_score":0.37122118},"labels":[],"label_agreement":null},{"id":"W3143384983","doi":"10.1073/pnas.2020397118","title":"Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes.","year":2021,"lang":"en","type":"article","venue":"PubMed","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; National Science Foundation","keywords":"Inference; Ordinary differential equation; Gaussian process; Computer science; Dynamical systems theory; Manifold (fluid mechanics); Gaussian; Statistical inference; Variety (cybernetics); Numerical integration; Differential equation; Process (computing); Mathematical optimization; Machine learning; Algorithm; Applied mathematics; Artificial intelligence; Mathematics","score_opus":0.02955525513005491,"score_gpt":0.23399751019907403,"score_spread":0.2044422550690191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143384983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0323586,0.0042072022,0.95632017,0.0014852814,0.0006551924,0.00071485125,0.00031545412,0.00021318762,0.003730087],"genre_scores_gemma":[0.9897123,0.00020169458,0.009603076,0.000077533296,0.000041470303,0.00013428257,0.00006986048,0.000011939489,0.0001478385],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978745,0.000058684007,0.00044066054,0.00085640047,0.00033128573,0.000438471],"domain_scores_gemma":[0.997737,0.0001608566,0.00024906272,0.0014158332,0.0002205837,0.00021668228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002560851,0.00021664811,0.00035769655,0.00008366346,0.00007754835,0.00039229958,0.001565318,0.00009975602,0.0000131189345],"category_scores_gemma":[0.00028971938,0.00019777229,0.000024241855,0.0006587899,0.00009820565,0.000988157,0.0009525388,0.0001506058,0.000008007583],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031155792,0.00051074737,0.016939519,0.0026345768,0.0002907663,0.0005728885,0.0010971563,0.000027312504,0.0011264507,0.07131224,0.00035232797,0.9051049],"study_design_scores_gemma":[0.002625726,0.00010197264,0.7254554,0.0007317478,0.00020216902,0.0005865924,0.000513972,0.22096664,0.0044380217,0.03941763,0.002953614,0.002006528],"about_ca_topic_score_codex":0.0001916387,"about_ca_topic_score_gemma":0.00021077823,"teacher_disagreement_score":0.9573537,"about_ca_system_score_codex":0.000024548188,"about_ca_system_score_gemma":0.00036920118,"threshold_uncertainty_score":0.806492},"labels":[],"label_agreement":null},{"id":"W3156514433","doi":"10.14293/s2199-1006.1.sor-.pps25dj.v1","title":"The Shallow Gibbs Network, Double Backpropagation and Differential Machine learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gibbs sampling; Backpropagation; Gibbs free energy; Computer science; Artificial intelligence; Boltzmann machine; Artificial neural network; Machine learning; Mathematics; Algorithm; Bayesian probability; Physics","score_opus":0.010188804587415105,"score_gpt":0.21188630138381664,"score_spread":0.20169749679640153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156514433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012141965,0.0008037026,0.9695344,0.004052862,0.00029384415,0.00007013706,2.227276e-7,0.000120209304,0.012982637],"genre_scores_gemma":[0.98404557,0.0002501356,0.009774557,0.00013829287,0.00009996992,0.0000062464637,0.0000036932315,0.0000052097366,0.0056763324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991782,0.000041656873,0.0001328365,0.00026402902,0.00015174119,0.00023155738],"domain_scores_gemma":[0.9995327,0.00006411621,0.000048816728,0.00022205437,0.0000721425,0.000060175244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012506057,0.00009047989,0.000084253144,0.000010949306,0.00042054415,0.00070935563,0.00029297586,0.00003391731,0.000080865066],"category_scores_gemma":[0.000015091053,0.000056812827,0.000026066911,0.00021745634,0.000032430296,0.0002513048,0.00030891516,0.00015378799,0.000020703928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015441821,0.000030605053,0.012734825,0.000028715638,0.000025264711,0.0000229831,0.00021236378,0.00018294321,0.00068574713,0.8250372,0.0005070648,0.16051686],"study_design_scores_gemma":[0.0014352966,0.00014841778,0.04323559,0.000060426322,0.000019099247,0.00014913928,0.000071212606,0.8603886,0.0038073168,0.053714287,0.036443986,0.00052665133],"about_ca_topic_score_codex":0.0000180355,"about_ca_topic_score_gemma":0.00019096816,"teacher_disagreement_score":0.9719036,"about_ca_system_score_codex":0.000007387889,"about_ca_system_score_gemma":0.000057247373,"threshold_uncertainty_score":0.6840333},"labels":[],"label_agreement":null},{"id":"W3157026294","doi":"10.59275/j.melba.2021-a6fd","title":"Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging","year":2021,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"European Regional Development Fund; European Commission","keywords":"Uncertainty quantification; Computer science; Regression; Inference; Calibration; Bayesian probability; Dropout (neural networks); Machine learning; Sensitivity analysis; Predictive inference; Artificial intelligence; Monte Carlo method; Bayesian inference; Econometrics; Statistics; Uncertainty analysis; Mathematics; Frequentist inference","score_opus":0.009173376499591528,"score_gpt":0.2787949627953283,"score_spread":0.26962158629573674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157026294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034927223,0.010278289,0.9320402,0.021869835,0.0006696995,0.00014033473,0.0000025408415,0.000022401997,0.00004947114],"genre_scores_gemma":[0.9851345,0.0012841539,0.013131462,0.0002147055,0.00018932567,0.0000030722422,0.000013715112,0.000016205995,0.000012891185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713284,0.000484787,0.0010062491,0.00027734233,0.0008239878,0.00027477168],"domain_scores_gemma":[0.9973793,0.00074685045,0.0011651383,0.00026568078,0.00025079542,0.00019225065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003646031,0.00021869491,0.0005375674,0.0003314682,0.000115023766,0.00015911878,0.0011857728,0.00012864333,0.000018501327],"category_scores_gemma":[0.0015440944,0.00013926263,0.00013335444,0.000383417,0.0002587557,0.00031101616,0.0010666215,0.0016842444,1.7024291e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001586232,0.00022964364,0.02715387,0.001755206,0.00009248446,0.00034133805,0.0039453385,0.0022238765,0.0026960804,0.0015554334,0.00046053817,0.95938754],"study_design_scores_gemma":[0.0008618381,0.000067340174,0.0018674572,0.0040017483,0.000044790955,0.0009885037,0.00022321944,0.9846851,0.00023111225,0.0060552955,0.0007793911,0.00019417673],"about_ca_topic_score_codex":0.00012529585,"about_ca_topic_score_gemma":0.0000055536884,"teacher_disagreement_score":0.9824613,"about_ca_system_score_codex":0.00006683463,"about_ca_system_score_gemma":0.00061058253,"threshold_uncertainty_score":0.73172975},"labels":[],"label_agreement":null},{"id":"W3163785802","doi":"","title":"Probing the Effect of Selection Bias on NN Generalization with a Thought Experiment.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Generalization; Set (abstract data type); Artificial intelligence; Computer science; Domain (mathematical analysis); Selection (genetic algorithm); Machine learning; Population; Training set; Mathematics","score_opus":0.04229101240425252,"score_gpt":0.19217142158434822,"score_spread":0.1498804091800957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163785802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4296783,0.000030556337,0.5691655,0.000048049646,0.00011580488,0.00023385156,9.669418e-7,0.000060478527,0.00066645775],"genre_scores_gemma":[0.99816656,0.00004287953,0.0013454409,0.000033978813,0.000039345607,0.000003843512,0.0000070950277,0.000011564422,0.0003492677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986319,0.00024245451,0.0001363794,0.00068089174,0.000118280186,0.00019009195],"domain_scores_gemma":[0.99889374,0.00007939092,0.00021484343,0.0006265026,0.00013710662,0.000048437265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019067702,0.00023426754,0.0002421727,0.00012549749,0.00015544883,0.00017561587,0.0008195921,0.00012763104,0.0000150836095],"category_scores_gemma":[0.000017973445,0.00016420652,0.00009579135,0.0008737487,0.00005967427,0.00025471757,0.00046085622,0.00028624848,0.000003917453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022251671,0.00019036002,0.01656877,0.00073962426,0.000303045,0.00017192493,0.0020782994,0.7028364,0.0022877282,0.27213988,0.00020953327,0.0022519433],"study_design_scores_gemma":[0.001121811,0.001257873,0.0018798703,0.0010910635,0.00017419194,0.000028161277,0.00015953508,0.76814836,0.22290581,0.0022821084,0.00017236723,0.00077885465],"about_ca_topic_score_codex":0.00007488497,"about_ca_topic_score_gemma":0.00002946228,"teacher_disagreement_score":0.5684883,"about_ca_system_score_codex":0.00009670592,"about_ca_system_score_gemma":0.00017689397,"threshold_uncertainty_score":0.6696148},"labels":[],"label_agreement":null},{"id":"W3164043830","doi":"10.2514/1.i010921","title":"Human-Aware Reinforcement Learning for Fault Recovery Using Contextual Gaussian Processes","year":2021,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Defense Advanced Research Projects Agency; National Aeronautics and Space Administration","keywords":"Forgetting; Reinforcement learning; Computer science; Artificial intelligence; Task (project management); Robot; Process (computing); Set (abstract data type); Machine learning; Gaussian process; Human–robot interaction; Baseline (sea); Gaussian; Engineering","score_opus":0.02755522201738358,"score_gpt":0.2802436116144039,"score_spread":0.25268838959702034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164043830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0086705545,0.0002729516,0.9887397,0.0003743081,0.0007659805,0.00021919762,0.000002677056,0.00003839804,0.0009161813],"genre_scores_gemma":[0.99189925,0.000045070876,0.0072433106,0.00016365081,0.00019163285,0.000010357506,0.000007729279,0.000007022786,0.00043199962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978274,0.000051086212,0.0011427951,0.00012219089,0.000569803,0.00028671583],"domain_scores_gemma":[0.9953324,0.00008581618,0.0018208224,0.0002119819,0.0024122712,0.00013666916],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00061279396,0.00017554183,0.0003664598,0.0001928995,0.00033508943,0.0012350361,0.0004653761,0.00010429567,0.000008022269],"category_scores_gemma":[0.0003455748,0.00014884277,0.00012387875,0.0004980183,0.000022533555,0.0056374106,0.00009030044,0.00024083255,0.00000995879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012650371,0.00013382835,0.001413555,0.0065811807,0.00038606217,0.00007122875,0.015082137,0.9007033,0.002931324,0.04787631,0.009438244,0.015256306],"study_design_scores_gemma":[0.008272891,0.0035173872,0.00061199645,0.00790748,0.00018166886,0.005261134,0.036789834,0.71432513,0.01931042,0.0011870967,0.20049523,0.0021397243],"about_ca_topic_score_codex":0.000013625898,"about_ca_topic_score_gemma":0.00000402219,"teacher_disagreement_score":0.9832287,"about_ca_system_score_codex":0.0001551839,"about_ca_system_score_gemma":0.000901025,"threshold_uncertainty_score":0.99980175},"labels":[],"label_agreement":null},{"id":"W3165295415","doi":"10.48550/arxiv.2105.11205","title":"Deconvolution density estimation with penalised MLE","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Smoothness; Sample size determination; Transformation (genetics); Statistics; Sample (material); Mathematics; Fourier transform; Blind deconvolution; Noise (video); Maximum likelihood; SIGNAL (programming language); Computer science; Applied mathematics; Algorithm; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.04043738399059722,"score_gpt":0.17030504153245626,"score_spread":0.12986765754185903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165295415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25001416,0.000027898122,0.74828374,0.00010118007,0.00016272886,0.0001284812,0.0000023433656,0.00015639015,0.001123093],"genre_scores_gemma":[0.9686114,0.000042063723,0.030758448,0.00007630743,0.00002570595,9.3088426e-7,0.000028956758,0.000010140512,0.0004460134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983798,0.00008110084,0.00015044224,0.0009987045,0.000111457186,0.00027844647],"domain_scores_gemma":[0.9982883,0.000038531012,0.00024900178,0.0009651367,0.00031497653,0.00014408177],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012762217,0.00025920125,0.0002677171,0.00014246369,0.00017970435,0.00034735017,0.0010409465,0.00020558224,0.000058553927],"category_scores_gemma":[0.000024264584,0.0002692308,0.000099922545,0.00058315374,0.00008025791,0.000753459,0.00094699813,0.000389116,0.000041454186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001382146,0.00038741357,0.014595636,0.0007640594,0.00028391642,0.0023994846,0.0011135492,0.41499943,0.00012506991,0.5551805,0.00032489432,0.009687878],"study_design_scores_gemma":[0.0004313475,0.00005413951,0.008844219,0.0002105212,0.000065266046,0.000030952408,0.000060992992,0.957107,0.0003936266,0.032317836,0.00003144913,0.0004526329],"about_ca_topic_score_codex":0.00016791109,"about_ca_topic_score_gemma":0.00015940698,"teacher_disagreement_score":0.7185973,"about_ca_system_score_codex":0.00017578932,"about_ca_system_score_gemma":0.00054601516,"threshold_uncertainty_score":0.999976},"labels":[],"label_agreement":null},{"id":"W3170281420","doi":"","title":"Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Kernel (algebra); Orthogonality; Scalability; Gaussian process; Computer science; Algorithm; Applied mathematics; Gaussian; Mathematical optimization; Range (aeronautics); Fourier transform; Mathematics; Mathematical analysis; Physics; Discrete mathematics; Quantum mechanics","score_opus":0.020270192004813586,"score_gpt":0.2882641235050731,"score_spread":0.2679939315002595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170281420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026739321,0.00012627547,0.93639827,0.012194415,0.00052096,0.000081192964,0.000011581489,0.00024427223,0.047749102],"genre_scores_gemma":[0.967517,0.00010883265,0.028925752,0.0007328503,0.00014710738,0.000025302981,0.000140919,0.000015815076,0.0023864121],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794525,0.00011046813,0.00034092748,0.000647175,0.00064624054,0.00030993126],"domain_scores_gemma":[0.9984702,0.0001186548,0.00022084311,0.00027249363,0.0007902625,0.00012754838],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020253015,0.00023310941,0.00019040852,0.00016835016,0.0002819326,0.0008100521,0.000936173,0.00008672933,0.0014023402],"category_scores_gemma":[0.00025184138,0.00022753264,0.000069282796,0.00047586585,0.00003715732,0.0008324065,0.00026305512,0.0005052792,0.00036513325],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004322979,0.0003521555,0.008170087,0.000086295804,0.00010870543,0.00017004725,0.00045671334,0.0024712577,0.007215088,0.93731725,0.00019860629,0.043410555],"study_design_scores_gemma":[0.00080549537,0.00018156187,0.011090548,0.00026927947,0.000013668476,0.00027110844,0.000043929627,0.88721657,0.008579869,0.08512517,0.0058365962,0.0005662002],"about_ca_topic_score_codex":0.00004641179,"about_ca_topic_score_gemma":0.000035120516,"teacher_disagreement_score":0.9648431,"about_ca_system_score_codex":0.00009721761,"about_ca_system_score_gemma":0.0005443533,"threshold_uncertainty_score":0.9995105},"labels":[],"label_agreement":null},{"id":"W3170350958","doi":"","title":"Out-of-Distribution Generalization via Risk Extrapolation (REx)","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Robustness (evolution); Extrapolation; Covariate; Computer science; Generalization; Minification; Variance (accounting); Econometrics; Machine learning; Artificial intelligence; Mathematical optimization; Mathematics; Statistics","score_opus":0.012415436549312273,"score_gpt":0.23880323248119587,"score_spread":0.2263877959318836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170350958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005009949,0.00009679719,0.99249876,0.00024284418,0.0003476984,0.000037936552,0.000006505043,0.00007298452,0.0016865509],"genre_scores_gemma":[0.937234,0.00006664486,0.062325727,0.00004718879,0.000046448746,0.000002546762,0.0000473689,0.0000028671955,0.00022721723],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926174,0.00004497323,0.0001982585,0.00021769645,0.00016012334,0.0001172107],"domain_scores_gemma":[0.99931264,0.000018306233,0.00011958924,0.000278979,0.00023049576,0.000040019906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010317137,0.000068835005,0.00008316766,0.000024846988,0.00006970489,0.00006866757,0.0001962025,0.000048374197,0.00008305093],"category_scores_gemma":[0.000042665073,0.0000630996,0.00003789872,0.00037706943,0.000014672625,0.00040274608,0.00006564622,0.000048501086,0.000025529407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033940464,0.000112685695,0.01758719,0.00005902443,0.000021658245,0.000008325934,0.00036841305,0.00044823665,0.010990788,0.8172979,0.0011498146,0.15195261],"study_design_scores_gemma":[0.0004330822,0.00008627486,0.08615613,0.000043128373,0.000022509024,0.000018874904,0.00002441539,0.6266223,0.1616004,0.120579645,0.004044303,0.0003689417],"about_ca_topic_score_codex":0.000026306137,"about_ca_topic_score_gemma":0.00002512866,"teacher_disagreement_score":0.93222404,"about_ca_system_score_codex":0.000017910887,"about_ca_system_score_gemma":0.00008084829,"threshold_uncertainty_score":0.2573127},"labels":[],"label_agreement":null},{"id":"W3176268138","doi":"10.71781/10674","title":"On improving variational inference with low-variance multi-sample estimators","year":2020,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Montréal","keywords":"Estimator; Inference; Variance (accounting); Sample (material); Statistics; Mathematics; Computer science; Econometrics; Artificial intelligence; Applied mathematics; Algorithm; Economics; Physics","score_opus":0.02337361973942188,"score_gpt":0.2991732527399016,"score_spread":0.2757996330004797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176268138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023861274,0.000020468879,0.9884803,0.00025609645,0.00039193177,0.0005590109,0.00005667202,0.000016414991,0.00783294],"genre_scores_gemma":[0.25605035,0.000004023194,0.7416308,0.00015966449,0.000056196317,0.00008120138,0.00040408023,0.000032211785,0.0015814507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976738,0.000038896243,0.00037239643,0.00109655,0.00047759007,0.00034076566],"domain_scores_gemma":[0.9982407,0.0002740436,0.00048976875,0.0005968928,0.00021157958,0.00018700305],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00014486756,0.0004002651,0.00038968268,0.00010333549,0.00023813758,0.0016311903,0.002628965,0.00019982227,0.00049870915],"category_scores_gemma":[0.00039014162,0.00034084718,0.000049657225,0.000526291,0.00002646898,0.00094044267,0.00025758208,0.00045384583,0.00046221656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004180111,0.0007141102,0.00078412896,0.00063498935,0.00020804237,0.00025994345,0.008522394,0.0015332641,0.00063933746,0.1771843,0.00028700376,0.80881447],"study_design_scores_gemma":[0.0041276757,0.0017349954,0.024085196,0.0037197045,0.00017192302,0.000033238353,0.000419159,0.9243644,0.011320658,0.019828986,0.0059724823,0.004221622],"about_ca_topic_score_codex":0.00013945941,"about_ca_topic_score_gemma":0.0002487,"teacher_disagreement_score":0.9228311,"about_ca_system_score_codex":0.000060466737,"about_ca_system_score_gemma":0.0015592546,"threshold_uncertainty_score":0.99990433},"labels":[],"label_agreement":null},{"id":"W3184650294","doi":"10.1098/rsos.210171","title":"Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations","year":2021,"lang":"en","type":"article","venue":"Royal Society Open Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto; Fields Institute for Research in Mathematical Sciences","funders":"","keywords":"Partial differential equation; Inference; Ordinary differential equation; Nonlinear system; Dynamical systems theory; Applied mathematics; Statistical inference; Computer science; Observable; Bayesian inference; Stochastic partial differential equation; Gaussian process; Algorithm; Mathematical optimization; Mathematics; Sampling (signal processing); Gaussian; Differential equation; Bayesian probability; Artificial intelligence; Statistics; Mathematical analysis; Physics","score_opus":0.05274328933390326,"score_gpt":0.3383527260546331,"score_spread":0.28560943672072986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184650294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008179979,0.00003706976,0.9905368,0.00042123412,0.0001696488,0.00039560712,0.000033813885,0.000022295833,0.00020353938],"genre_scores_gemma":[0.5002895,0.0000024247984,0.49944454,0.00005227607,0.000018473444,0.000055714794,0.000005316375,0.000004252551,0.00012749825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978473,0.00006929217,0.00046320914,0.0006384951,0.0006143448,0.00036731907],"domain_scores_gemma":[0.99708664,0.00068887195,0.0003329403,0.00068784825,0.0010642787,0.00013940722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064918015,0.00015466001,0.00038182366,0.000032185722,0.00036031182,0.00049125863,0.002590771,0.00006408831,0.000020993339],"category_scores_gemma":[0.00047859253,0.000121798505,0.00012769294,0.0015620365,0.00036168547,0.00086115807,0.00086210034,0.000118996024,0.0000015839216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009241287,0.001960414,0.019120244,0.00092922885,0.00025460645,0.000009452219,0.0056652315,0.046748865,0.027718188,0.87374353,0.0010522539,0.02270555],"study_design_scores_gemma":[0.00040640292,0.00024522436,0.004302075,0.000095732335,0.0000242049,0.0000043776713,0.0001682605,0.98135686,0.011898674,0.001072633,0.00023545147,0.00019010987],"about_ca_topic_score_codex":0.00027776803,"about_ca_topic_score_gemma":0.000007772987,"teacher_disagreement_score":0.934608,"about_ca_system_score_codex":0.00003880597,"about_ca_system_score_gemma":0.0016648313,"threshold_uncertainty_score":0.4966799},"labels":[],"label_agreement":null},{"id":"W3187054880","doi":"","title":"Plinko: A Theory-Free Behavioral Measure of Priors for Statistical Learning and Mental Model Updating.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Prior probability; Bayesian probability; Artificial intelligence; Posterior probability; Bayesian inference; Computer science; Measure (data warehouse); Task (project management); Probability distribution; Machine learning; Psychology; Cognitive psychology; Mathematics; Statistics; Data mining","score_opus":0.05422884318763529,"score_gpt":0.22092716201379192,"score_spread":0.16669831882615663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187054880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14378189,0.00006340744,0.8553748,0.00004409825,0.00008983428,0.00022851783,0.00006661775,0.00005973555,0.00029111982],"genre_scores_gemma":[0.93328804,0.000055359866,0.06635313,0.000014488709,0.000012654701,0.0000018707253,0.00003154842,0.000013984161,0.0002289439],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984536,0.00009455977,0.00021795381,0.00084472995,0.00011625229,0.0002729138],"domain_scores_gemma":[0.9987187,0.000119930715,0.0002546587,0.0005527927,0.00021905747,0.00013485507],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002921459,0.0002425013,0.00034873735,0.00010792456,0.00015697179,0.00014921869,0.0010302868,0.00020593384,0.000016443695],"category_scores_gemma":[0.000095452284,0.0002683422,0.0001097277,0.00019471522,0.00015273147,0.00025979924,0.0019986108,0.00044910374,8.571035e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008833272,0.00020093734,0.0037811901,0.00048220356,0.00008981617,0.00008024605,0.0024692088,0.014189293,0.00019288194,0.97356886,0.00007222083,0.0047847917],"study_design_scores_gemma":[0.00063119986,0.00012966755,0.0002704234,0.00016673954,0.00009391932,0.000006091655,0.0005764076,0.8610578,0.00021864519,0.13649192,0.000014576447,0.0003426065],"about_ca_topic_score_codex":0.00002563018,"about_ca_topic_score_gemma":0.000022663953,"teacher_disagreement_score":0.8468685,"about_ca_system_score_codex":0.00006075367,"about_ca_system_score_gemma":0.00036175404,"threshold_uncertainty_score":0.9999769},"labels":[],"label_agreement":null},{"id":"W3197943580","doi":"10.48550/arxiv.2002.11259","title":"Dimensional Analysis in Statistical Modelling","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Logarithm; Statistical model; Computer science; Scale (ratio); Statistical theory; Frequentist probability; Natural (archaeology); Statistical physics; Bayesian probability; Mathematics; Artificial intelligence; Statistics; Physics","score_opus":0.08345103208852576,"score_gpt":0.19503770638009585,"score_spread":0.11158667429157008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197943580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02576284,0.000026682019,0.9728239,0.00025559304,0.00010648888,0.000093600844,0.000025977453,0.00009859981,0.0008062909],"genre_scores_gemma":[0.96692073,0.00003534595,0.032772157,0.000113105765,0.000022474294,4.774474e-7,0.000027578799,0.0000075592934,0.00010055206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980792,0.00008258289,0.00022691747,0.0011896535,0.00011830878,0.00030337082],"domain_scores_gemma":[0.998834,0.00010543632,0.00014041076,0.0006367541,0.000083031846,0.00020037386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012321975,0.00024145197,0.00041606333,0.00040065745,0.00006516901,0.00013301006,0.0013341458,0.0001820369,0.00005522766],"category_scores_gemma":[0.000018306138,0.000271524,0.00016398456,0.0017270353,0.000062754014,0.00023453694,0.0015206411,0.0005955947,0.00006383085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008161225,0.000032481366,0.001953193,0.000030170217,0.00008335593,0.00043430727,0.0000659721,0.6449995,0.0000012125589,0.35225418,0.00002948772,0.000107968895],"study_design_scores_gemma":[0.000121090634,0.000015188248,0.0015176684,0.000025091029,0.00010409066,8.049992e-7,0.0000074217783,0.78818923,0.000005331499,0.2097584,0.000020125559,0.00023555224],"about_ca_topic_score_codex":0.00021690414,"about_ca_topic_score_gemma":0.00005946608,"teacher_disagreement_score":0.94115794,"about_ca_system_score_codex":0.000106105435,"about_ca_system_score_gemma":0.0002672121,"threshold_uncertainty_score":0.9999737},"labels":[],"label_agreement":null},{"id":"W3199348969","doi":"10.1609/aaai.v36i6.20567","title":"Deconvolutional Density Network: Modeling Free-Form Conditional Distributions","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; University of Jinan; National Natural Science Foundation of China","keywords":"Deconvolution; Computer science; Curse of dimensionality; Discretization; Conditional probability distribution; A priori and a posteriori; Algorithm; Artificial neural network; Density estimation; Artificial intelligence; Multivariate statistics; Machine learning; Data mining; Mathematics; Statistics","score_opus":0.05418182185245969,"score_gpt":0.26589894993699853,"score_spread":0.21171712808453885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199348969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048543938,0.000036587444,0.934604,0.007984645,0.0007513259,0.0003781585,0.00011928764,0.00014834123,0.0074337265],"genre_scores_gemma":[0.99349654,0.000010582787,0.0058484618,0.00032101013,0.00010511914,0.00007949445,0.0000088829565,0.000009115231,0.00012082341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975144,0.000023719635,0.0005857175,0.00055105967,0.0008236022,0.00050147256],"domain_scores_gemma":[0.9983667,0.00007550808,0.0003623616,0.00039472713,0.00068249716,0.000118201955],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005809473,0.00022807988,0.00024221289,0.00009599188,0.0013315919,0.00023938753,0.0032118235,0.000061225735,0.0003429269],"category_scores_gemma":[0.00021529467,0.00019633253,0.00016401053,0.00093879865,0.00023374861,0.00052684004,0.0016635592,0.0005378674,0.000037765363],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026599533,0.00012906844,0.00022690278,0.000014046167,0.000014331326,7.0283517e-7,0.00025287343,0.0043839216,0.0007120504,0.9901875,0.0007371996,0.0033147868],"study_design_scores_gemma":[0.00002315105,0.000077193494,0.00018367023,0.000027353466,0.0000073956344,0.000019217596,0.00012142771,0.39550716,0.0055784388,0.59817314,0.00012390355,0.00015795254],"about_ca_topic_score_codex":0.000045826873,"about_ca_topic_score_gemma":0.000016496462,"teacher_disagreement_score":0.94495255,"about_ca_system_score_codex":0.00014857626,"about_ca_system_score_gemma":0.00034354604,"threshold_uncertainty_score":0.9999685},"labels":[],"label_agreement":null},{"id":"W3201304010","doi":"10.1109/mercon52712.2021.9525652","title":"Quadcopter Disturbance Estimation using Different Learning Methods","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadcopter; Computer science; Artificial neural network; Aerodynamics; Process (computing); Artificial intelligence; Control theory (sociology); Control engineering; System dynamics; Vehicle dynamics; Gaussian process; Machine learning; Engineering; Gaussian; Control (management); Automotive engineering","score_opus":0.027308238816398076,"score_gpt":0.33408068023189164,"score_spread":0.30677244141549354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201304010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011483032,0.00016636972,0.98423207,0.0004972813,0.00019669753,0.00003190038,1.2908882e-7,0.00013422457,0.0032582753],"genre_scores_gemma":[0.4652502,0.0000054652965,0.5340166,0.000108487024,0.000015211265,0.0000021076607,9.824225e-7,0.0000033841866,0.00059760193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990225,0.00011570938,0.00017342911,0.00033661816,0.00015032913,0.00020143575],"domain_scores_gemma":[0.9994072,0.00007836012,0.000068289344,0.00029618008,0.00008548093,0.000064520624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013229268,0.00011320344,0.00013876964,0.000033978416,0.00014344727,0.00032470294,0.00029338954,0.00003926607,0.00009979963],"category_scores_gemma":[0.00008797813,0.000089900816,0.000046849334,0.00028212424,0.000018000379,0.00043810267,0.00020803676,0.00013479397,0.000020329142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024171259,0.000107951135,0.004221824,0.000104153034,0.00002543431,0.000043143547,0.0007982102,0.0050663184,0.013032856,0.28763387,0.000096443975,0.6888674],"study_design_scores_gemma":[0.00009220627,0.00001794019,0.0041639567,0.000035816505,0.000005319796,0.00003561147,0.000026547701,0.9453132,0.035776086,0.013781754,0.00058294385,0.00016861438],"about_ca_topic_score_codex":0.0000092529535,"about_ca_topic_score_gemma":0.0000021589572,"teacher_disagreement_score":0.9402469,"about_ca_system_score_codex":0.000032619864,"about_ca_system_score_gemma":0.00006319282,"threshold_uncertainty_score":0.3666049},"labels":[],"label_agreement":null},{"id":"W3211283390","doi":"10.5281/zenodo.3998873","title":"Source data for PointNovo","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Data source; Computer science; Data science; Information retrieval","score_opus":0.06351317257249739,"score_gpt":0.2695143967456252,"score_spread":0.20600122417312783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211283390","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.9592823e-7,0.000047336867,0.33096054,0.0017690464,0.00014381562,0.00037829613,0.664729,0.0005377073,0.0014339879],"genre_scores_gemma":[0.00006155247,0.00013870781,0.0047106203,0.0006937995,0.00037482425,7.776547e-8,0.9930209,0.0007304957,0.00026900257],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972131,0.0001764188,0.00037288954,0.0012242603,0.0004989175,0.00051438966],"domain_scores_gemma":[0.9962739,0.000050806364,0.00026457076,0.0026262435,0.0004892834,0.00029524378],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.00060156226,0.00028604016,0.00029765556,0.00021579521,0.0017767127,0.0030458511,0.012056769,0.00015961177,0.0022348592],"category_scores_gemma":[0.0012512594,0.00029287426,0.000068725094,0.0007213523,0.00011793325,0.00066872087,0.010483537,0.00050041795,0.014149743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015237917,0.000060137158,1.5983568e-8,0.00029526092,0.00003459052,0.000009875087,0.000073733994,0.000003136841,0.000019389929,0.0017581335,0.9497227,0.048007794],"study_design_scores_gemma":[0.00031040524,0.00024602853,0.0000027205267,0.00006051753,0.000024482999,0.00009680992,0.000021427524,0.0022971116,0.000016027274,0.00052803685,0.9960613,0.0003350979],"about_ca_topic_score_codex":0.000014913786,"about_ca_topic_score_gemma":4.6965238e-7,"teacher_disagreement_score":0.32829195,"about_ca_system_score_codex":0.00007483911,"about_ca_system_score_gemma":0.000023947698,"threshold_uncertainty_score":0.9999523},"labels":[],"label_agreement":null},{"id":"W3215903943","doi":"10.3847/2515-5172/ac3dfe","title":"Updates to LUCI: A New Fitting Paradigm Using Mixture Density Networks","year":2021,"lang":"en","type":"article","venue":"Research Notes of the AAS","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"","keywords":"Computer science; Algorithm; Inference; Point (geometry); Computation; Convolutional neural network; Code (set theory); Bayesian inference; Pipeline (software); Simple (philosophy); Gaussian; Bayesian probability; Machine learning; Artificial intelligence; Mathematics","score_opus":0.10076408515865795,"score_gpt":0.36841624519527433,"score_spread":0.2676521600366164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215903943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13895182,0.00094860455,0.8376501,0.021710055,0.00021169033,0.00020533046,0.0000017935855,0.000041192274,0.00027941875],"genre_scores_gemma":[0.9357208,0.00002812461,0.06357442,0.00032122474,0.000153232,0.0000018283125,5.746456e-7,0.0000082575725,0.00019157087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980604,0.00022218384,0.00020047059,0.0003870518,0.0005911559,0.00053870893],"domain_scores_gemma":[0.99795556,0.00049352617,0.00006804639,0.0009697436,0.00031311528,0.00019998006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075121823,0.00010783729,0.00017884353,0.00007541787,0.00032244448,0.0003204618,0.001624689,0.00007388241,0.000020856203],"category_scores_gemma":[0.0011839751,0.00007808847,0.000086306776,0.0015769824,0.000075252516,0.0002330635,0.0017840307,0.00041363994,0.0000166937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014980162,0.0006349533,0.08417121,0.00078658044,0.0003628789,0.0005432601,0.0076173437,0.05234952,0.106534846,0.37718078,0.03409611,0.33557272],"study_design_scores_gemma":[0.0005916752,0.00019661908,0.037661366,0.0014093067,0.000025213654,0.00020946859,0.00014186272,0.41418812,0.32545224,0.21511884,0.00429391,0.0007113539],"about_ca_topic_score_codex":0.00037260648,"about_ca_topic_score_gemma":0.00009754915,"teacher_disagreement_score":0.79676896,"about_ca_system_score_codex":0.000046412053,"about_ca_system_score_gemma":0.0005931309,"threshold_uncertainty_score":0.31843555},"labels":[],"label_agreement":null},{"id":"W4213276373","doi":"10.1016/j.jqsrt.2022.108134","title":"Rate coefficient function estimation using Gaussian process regression","year":2022,"lang":"en","type":"article","venue":"Journal of Quantitative Spectroscopy and Radiative Transfer","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Air Force Research Laboratory; National Academies of Sciences, Engineering, and Medicine","keywords":"Mathematics; Regression function; Statistics; Estimation; Kriging; Regression; Gaussian process; Regression analysis; Function (biology); Applied mathematics; Gaussian; Engineering; Biology; Physics","score_opus":0.024515562311492264,"score_gpt":0.313810083601017,"score_spread":0.2892945212895247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213276373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16693723,0.0006930424,0.83099765,0.00073494884,0.0003316835,0.00012813817,0.000006942067,0.0000155879,0.0001547674],"genre_scores_gemma":[0.95885694,0.00007928977,0.04079758,0.00019154622,0.000043584496,0.000005597061,0.0000012030819,0.000010187648,0.000014084498],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983573,0.00029909075,0.0004356612,0.0002569334,0.000418845,0.00023219075],"domain_scores_gemma":[0.9992103,0.00011710547,0.0002560898,0.00010646327,0.00019711496,0.00011292468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007144104,0.00018035874,0.00029724484,0.00030175576,0.00057316816,0.00012685647,0.00031049267,0.000032947097,0.000052011972],"category_scores_gemma":[0.000029558749,0.00013558049,0.00008499921,0.00067947805,0.000096616895,0.0009552213,0.000034338645,0.00040608124,0.0000013214072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020914134,0.00082780817,0.0018571573,0.00034448513,0.0003487669,0.00020596314,0.034529287,0.11734033,0.11306515,0.7203932,0.0004549169,0.008541512],"study_design_scores_gemma":[0.0031512058,0.008356873,0.01699741,0.0003344377,0.00017859932,0.00040102736,0.003952587,0.846005,0.04738952,0.07189627,0.0006076601,0.00072936993],"about_ca_topic_score_codex":0.0000040547425,"about_ca_topic_score_gemma":0.0000013370149,"teacher_disagreement_score":0.7919197,"about_ca_system_score_codex":0.000110940986,"about_ca_system_score_gemma":0.00026477585,"threshold_uncertainty_score":0.5528812},"labels":[],"label_agreement":null},{"id":"W4230318596","doi":"10.1007/978-1-4939-7131-2_100315","title":"Eigenvalue with the Largest Magnitude – Dominant Eigenvalue","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Eigenvalues and eigenvectors; Magnitude (astronomy); Mathematics; Physics; Quantum mechanics; Astrophysics","score_opus":0.012114916251125311,"score_gpt":0.2091950761416582,"score_spread":0.19708015989053287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230318596","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011690286,0.00028709584,0.34649777,0.002131573,0.0002555984,0.00035452054,0.000010745211,0.00014828487,0.6503027],"genre_scores_gemma":[0.0074562267,0.00024968595,0.032496847,0.001967788,0.00057006965,0.00003906778,0.000010017918,0.000088516026,0.9571218],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973276,0.000026344942,0.0003605865,0.0009823103,0.0007315247,0.00057160173],"domain_scores_gemma":[0.9972787,0.00009232024,0.00032545882,0.001783612,0.00033593093,0.00018401149],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00035841606,0.00062245206,0.00045824988,0.00012763918,0.00039635127,0.00056405296,0.0029470923,0.00030414277,0.0020532415],"category_scores_gemma":[0.000010661173,0.00032350724,0.00015636184,0.00012071312,0.0003874292,0.00032867846,0.00067542994,0.0005057451,0.002493834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001061644,0.000020442898,0.000010560481,0.00005069596,0.00007262221,0.00009749141,0.00024227788,0.0000011840023,0.000010070937,0.9715441,0.020957103,0.0069828588],"study_design_scores_gemma":[0.0005611308,0.000811882,0.00023987795,0.00042652577,0.00013937501,0.00040089074,0.0000320334,0.0011231423,0.0007421252,0.14534104,0.84860295,0.0015790373],"about_ca_topic_score_codex":0.000030728515,"about_ca_topic_score_gemma":0.00019685188,"teacher_disagreement_score":0.82764584,"about_ca_system_score_codex":0.000054500953,"about_ca_system_score_gemma":0.00046828465,"threshold_uncertainty_score":0.9999217},"labels":[],"label_agreement":null},{"id":"W4230558268","doi":"10.1007/978-1-4899-7502-7_109-1","title":"Gaussian Process Reinforcement Learning","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Process (computing); Gaussian process; Computer science; Artificial intelligence; Gaussian; Machine learning; Physics","score_opus":0.016659008463119216,"score_gpt":0.2669173883033986,"score_spread":0.2502583798402794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230558268","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054918823,0.0018712115,0.24354346,0.00033169228,0.00022665258,0.00017356574,0.000016193895,0.00023725559,0.75354505],"genre_scores_gemma":[0.15700664,0.0101217525,0.054915532,0.0002441974,0.00078873127,0.000019787898,0.0014820123,0.00020971292,0.77521163],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996934,0.000065451786,0.0007581635,0.001201351,0.0005958533,0.000445185],"domain_scores_gemma":[0.9973563,0.0002639772,0.00097211124,0.0010791827,0.000112668014,0.00021573671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00084434944,0.00055112795,0.000724925,0.00029268433,0.0003419565,0.0002101272,0.0019910722,0.00039139428,0.0001818282],"category_scores_gemma":[0.0004262616,0.00050583744,0.000070075184,0.00010818409,0.00013056252,0.000522275,0.0017818946,0.0016678643,0.0000387459],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038390037,0.000027310747,0.0047224993,0.0017999349,0.00019895553,0.000083647785,0.0032930144,0.0012835782,0.0000062900053,0.13663566,0.0017852782,0.85012543],"study_design_scores_gemma":[0.000526758,0.00053732976,0.00010017509,0.0013298518,0.000104825536,0.00007659037,0.00007679483,0.15427518,0.0000066887706,0.0026782374,0.83926827,0.0010193237],"about_ca_topic_score_codex":0.000032450418,"about_ca_topic_score_gemma":0.00001101533,"teacher_disagreement_score":0.84910613,"about_ca_system_score_codex":0.000018249508,"about_ca_system_score_gemma":0.00020215189,"threshold_uncertainty_score":0.99973935},"labels":[],"label_agreement":null},{"id":"W4237425891","doi":"10.32920/ryerson.14655975","title":"Financial time series volatility analysis using Gaussian process state-space models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Particle filter; Stochastic volatility; State-space representation; State space; Gaussian process; Markov chain Monte Carlo; Computer science; Likelihood function; Bayesian inference; Inference; Hidden Markov model; Monte Carlo method; Volatility (finance); Finance; Econometrics; Algorithm; Bayesian probability; Gaussian; Mathematics; Kalman filter; Estimation theory; Artificial intelligence; Statistics","score_opus":0.019993591647033036,"score_gpt":0.2618178534428355,"score_spread":0.24182426179580244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237425891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083691604,0.00019701471,0.91168064,0.00053797604,0.00022160282,0.00026043697,0.000037993963,0.0003262237,0.003046499],"genre_scores_gemma":[0.79748875,0.000032242446,0.20075214,0.00012266955,0.00006397469,0.000028792883,0.000039720133,0.000022179029,0.0014495479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599826,0.00013222265,0.0007081141,0.0017592538,0.00072268205,0.00067948847],"domain_scores_gemma":[0.9968436,0.000035640165,0.00045466283,0.0017748761,0.0006748709,0.00021638273],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003702764,0.0006205798,0.0010532395,0.00041528678,0.00027352647,0.001819267,0.0021287838,0.00039699863,0.00026090484],"category_scores_gemma":[0.00006990242,0.000570566,0.00043864967,0.0023500163,0.00011447772,0.0019643223,0.0022726052,0.0007385627,0.000015927808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121105855,0.0008588159,0.011587053,0.004408949,0.0028253042,0.00065796054,0.02350104,0.8572821,0.00043341485,0.08258813,0.0006584271,0.01507769],"study_design_scores_gemma":[0.00008222271,0.000019301346,0.0014630797,0.00011481959,0.00020159602,0.000009802168,0.00005258692,0.89335656,0.0008042302,0.103216276,0.000010850585,0.00066866184],"about_ca_topic_score_codex":0.0003530645,"about_ca_topic_score_gemma":0.00033851457,"teacher_disagreement_score":0.71379715,"about_ca_system_score_codex":0.00013726488,"about_ca_system_score_gemma":0.0023504826,"threshold_uncertainty_score":0.99967456},"labels":[],"label_agreement":null},{"id":"W4248998625","doi":"10.32920/ryerson.14655975.v1","title":"Financial time series volatility analysis using Gaussian process state-space models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stochastic volatility; Particle filter; State-space representation; State space; Markov chain Monte Carlo; Computer science; Gaussian process; Likelihood function; Bayesian inference; Inference; Hidden Markov model; Monte Carlo method; Volatility (finance); Finance; Econometrics; Bayesian probability; Algorithm; Gaussian; Mathematics; Kalman filter; Estimation theory; Artificial intelligence; Statistics; Economics","score_opus":0.019993591647033036,"score_gpt":0.2618178534428355,"score_spread":0.24182426179580244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248998625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083691604,0.00019701471,0.91168064,0.00053797604,0.00022160282,0.00026043697,0.000037993963,0.0003262237,0.003046499],"genre_scores_gemma":[0.79748875,0.000032242446,0.20075214,0.00012266955,0.00006397469,0.000028792883,0.000039720133,0.000022179029,0.0014495479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599826,0.00013222265,0.0007081141,0.0017592538,0.00072268205,0.00067948847],"domain_scores_gemma":[0.9968436,0.000035640165,0.00045466283,0.0017748761,0.0006748709,0.00021638273],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003702764,0.0006205798,0.0010532395,0.00041528678,0.00027352647,0.001819267,0.0021287838,0.00039699863,0.00026090484],"category_scores_gemma":[0.00006990242,0.000570566,0.00043864967,0.0023500163,0.00011447772,0.0019643223,0.0022726052,0.0007385627,0.000015927808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121105855,0.0008588159,0.011587053,0.004408949,0.0028253042,0.00065796054,0.02350104,0.8572821,0.00043341485,0.08258813,0.0006584271,0.01507769],"study_design_scores_gemma":[0.00008222271,0.000019301346,0.0014630797,0.00011481959,0.00020159602,0.000009802168,0.00005258692,0.89335656,0.0008042302,0.103216276,0.000010850585,0.00066866184],"about_ca_topic_score_codex":0.0003530645,"about_ca_topic_score_gemma":0.00033851457,"teacher_disagreement_score":0.71379715,"about_ca_system_score_codex":0.00013726488,"about_ca_system_score_gemma":0.0023504826,"threshold_uncertainty_score":0.99967456},"labels":[],"label_agreement":null},{"id":"W4253389928","doi":"10.2307/2669463","title":"Bayesian Regression Modeling with Interactions and Smooth Effects","year":2000,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bayesian linear regression; Interpretation (philosophy); Bayesian probability; Computation; Machine learning; Regression; Artificial intelligence; Bivariate analysis; Model selection; Regression analysis; Gaussian process; Bayesian inference; Algorithm; Gaussian; Mathematics; Statistics","score_opus":0.004437693998908057,"score_gpt":0.24336192438036883,"score_spread":0.23892423038146077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253389928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08657524,0.000018294611,0.91019195,0.002769935,0.000071729744,0.000038856226,0.0000019979504,0.000009853411,0.00032214884],"genre_scores_gemma":[0.93125504,0.00003441272,0.06826221,0.0002901222,0.000048356353,7.245624e-7,1.7463469e-7,0.0000042898337,0.00010466925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909365,0.000120281875,0.00021223439,0.00010451453,0.00033149988,0.0001377914],"domain_scores_gemma":[0.9989003,0.00032158053,0.000473025,0.00011273637,0.00011530768,0.00007703203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019001648,0.00007834307,0.0001801167,0.000039205086,0.00012711954,0.00013406183,0.00024425553,0.000014342278,0.000012491291],"category_scores_gemma":[0.00017594559,0.000042368203,0.000030711726,0.00026746248,0.0000391609,0.00032534776,0.00003148191,0.00022019554,0.0000026922266],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001577664,0.00017691513,0.015285047,0.000043323394,0.00012826188,0.000044875436,0.0010123651,0.0020070854,0.00035788387,0.019740103,0.0019353904,0.959111],"study_design_scores_gemma":[0.0008918386,0.0010342567,0.14319444,0.00051484513,0.0001327865,0.0002445631,0.00012864752,0.8030062,0.00021051863,0.04941647,0.00089564495,0.00032983458],"about_ca_topic_score_codex":0.000027072701,"about_ca_topic_score_gemma":0.000007786934,"teacher_disagreement_score":0.9587811,"about_ca_system_score_codex":0.00010922708,"about_ca_system_score_gemma":0.00006583137,"threshold_uncertainty_score":0.17277253},"labels":[],"label_agreement":null},{"id":"W4254472742","doi":"10.1007/978-1-4614-6170-8_100178","title":"Eigenvalue with the Largest Magnitude – Dominant Eigenvalue","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Magnitude (astronomy); Eigenvalues and eigenvectors; Mathematics; Physics; Quantum mechanics; Astrophysics","score_opus":0.009564182088872938,"score_gpt":0.19742777135107512,"score_spread":0.18786358926220217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254472742","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003117954,0.0002101148,0.49214643,0.0023514263,0.00016433712,0.00025900174,0.000005200964,0.0001156839,0.50474465],"genre_scores_gemma":[0.020260535,0.00023724746,0.02636877,0.0026249317,0.00040453346,0.00004360849,0.0000107008045,0.00009239666,0.94995725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997366,0.000034036533,0.0003711073,0.0009455068,0.00072831765,0.0005550301],"domain_scores_gemma":[0.99731743,0.00013363197,0.00034171873,0.0017805273,0.0002374162,0.0001892591],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041508852,0.0006293988,0.00051700784,0.00012565531,0.00035359,0.000533197,0.002860294,0.00029911986,0.00057952903],"category_scores_gemma":[0.000012105647,0.000328065,0.00016862394,0.0001003199,0.0002378776,0.00020137227,0.00057270564,0.0005950299,0.0013472338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006413937,0.000011316535,0.000010802892,0.000055499244,0.00004948696,0.000056330256,0.00008332574,0.0000053517792,0.00000740121,0.9759378,0.0072802054,0.016496109],"study_design_scores_gemma":[0.0005491658,0.0005185486,0.00025474708,0.0003636508,0.00012410634,0.00031487088,0.000012156312,0.0021414787,0.0003739561,0.08659439,0.9073139,0.0014390714],"about_ca_topic_score_codex":0.00003586602,"about_ca_topic_score_gemma":0.00015509874,"teacher_disagreement_score":0.90003365,"about_ca_system_score_codex":0.000047614627,"about_ca_system_score_gemma":0.00035410366,"threshold_uncertainty_score":0.99991715},"labels":[],"label_agreement":null},{"id":"W4254524777","doi":"10.14293/s2199-1006.1.sor-.pps25dj.v2","title":"The Shallow Gibbs Network, Double Backpropagation and Differential Machine learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gibbs sampling; Backpropagation; Gibbs free energy; Computer science; Artificial intelligence; Boltzmann machine; Artificial neural network; Mathematics; Machine learning; Algorithm; Bayesian probability; Physics","score_opus":0.014326761428649215,"score_gpt":0.22630557254650202,"score_spread":0.2119788111178528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254524777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011390278,0.0018891108,0.9765447,0.002474967,0.0010025072,0.00023780513,8.0537023e-7,0.00020230358,0.0062575536],"genre_scores_gemma":[0.9761303,0.0011569457,0.019085923,0.000101793994,0.00027194264,0.000038987753,0.000033223438,0.00001607226,0.0031648043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982123,0.00009453568,0.00030525724,0.00070210284,0.00030641392,0.00037942606],"domain_scores_gemma":[0.99886554,0.00009162096,0.0002037068,0.0006059904,0.00013135414,0.00010177203],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002740209,0.000271111,0.00025575008,0.0000322132,0.0005041489,0.002752491,0.0010162441,0.00017992039,0.00008225356],"category_scores_gemma":[0.000020210084,0.00017748958,0.0000849636,0.0001782141,0.000063749474,0.0002394474,0.0029247778,0.0008399659,0.000010504463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007934732,0.00014511227,0.020521367,0.0007990181,0.00032251826,0.00008590457,0.0017525045,0.007128713,0.0002872853,0.541425,0.0011642694,0.42628896],"study_design_scores_gemma":[0.0006338378,0.00007979728,0.013702298,0.00026929192,0.000036863363,0.000043632055,0.000050848117,0.951854,0.0003020886,0.028200934,0.004126593,0.00069980224],"about_ca_topic_score_codex":0.00013043136,"about_ca_topic_score_gemma":0.00042569128,"teacher_disagreement_score":0.96474004,"about_ca_system_score_codex":0.000024392604,"about_ca_system_score_gemma":0.00018302775,"threshold_uncertainty_score":0.99828273},"labels":[],"label_agreement":null},{"id":"W4280576960","doi":"10.1007/s10994-022-06172-1","title":"MAGMA: inference and prediction using multi-task Gaussian processes with common mean","year":2022,"lang":"en","type":"article","venue":"Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Army Research Laboratory; Army Research Office; Engineering and Physical Sciences Research Council; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Gaussian process; Computer science; Task (project management); Inference; Computation; Process (computing); Gaussian; Machine learning; Data mining; Artificial intelligence; Algorithm","score_opus":0.016205121222673265,"score_gpt":0.24954763735420707,"score_spread":0.2333425161315338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280576960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18572912,0.00059575855,0.8118972,0.00047821578,0.00009480599,0.00020320644,0.000011779809,0.00037801237,0.00061189407],"genre_scores_gemma":[0.9710103,0.000025790825,0.028595356,0.00012291415,0.000026123063,0.000027161783,0.0000124779735,0.000018843206,0.00016100905],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998488,0.00014013369,0.00021179407,0.0004966969,0.00034709636,0.0003162892],"domain_scores_gemma":[0.9993068,0.00008415734,0.00018553948,0.00025470334,0.000063330604,0.0001054593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025802432,0.00020097136,0.000197286,0.000148594,0.0010160764,0.00029979562,0.00049593486,0.000031779913,0.00003796536],"category_scores_gemma":[0.000066447916,0.00017184492,0.000018816218,0.0007323174,0.00006105073,0.00058395235,0.0005710917,0.0005250393,0.0000023659386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071162445,0.00028017908,0.88922274,0.00063644594,0.000059718306,0.00015605526,0.010286181,0.040616654,0.0016001863,0.0070961122,0.000020346442,0.04995423],"study_design_scores_gemma":[0.0008841723,0.00065888476,0.02761789,0.00011332813,0.000026128726,0.00040093795,0.00038669314,0.96607155,0.00022148904,0.0007527741,0.0024298031,0.00043635102],"about_ca_topic_score_codex":0.00028449149,"about_ca_topic_score_gemma":0.00023625341,"teacher_disagreement_score":0.9254549,"about_ca_system_score_codex":0.00005335706,"about_ca_system_score_gemma":0.00018636571,"threshold_uncertainty_score":0.78149414},"labels":[],"label_agreement":null},{"id":"W4281552525","doi":"10.1038/s43586-022-00121-x","title":"Nested sampling for physical scientists","year":2022,"lang":"en","type":"article","venue":"Nature Reviews Methods Primers","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"Air Force Research Laboratory; Office of Naval Research; Science and Technology Facilities Council; Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Sampling (signal processing); Environmental science; Statistics; Computer science; Mathematics; Telecommunications","score_opus":0.06239660459391613,"score_gpt":0.45686582370151063,"score_spread":0.39446921910759447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281552525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007891036,0.01903556,0.9773579,0.00079997873,0.0011781038,0.00072122255,0.000005453349,0.00010185972,0.0007210112],"genre_scores_gemma":[0.002958275,0.0002183325,0.99508965,0.0010976605,0.000118708,0.0002769609,0.0000064207275,0.000014417775,0.0002195871],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99774164,0.0005760616,0.00031373484,0.0006675416,0.00031705698,0.00038395397],"domain_scores_gemma":[0.99846923,0.000407526,0.00025989782,0.0006588158,0.00008859078,0.00011595956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030112835,0.0001973974,0.00044794142,0.00012703509,0.0005214081,0.00014727596,0.0015520727,0.00007375069,0.000023370592],"category_scores_gemma":[0.0005660282,0.00016334791,0.00026828118,0.0013497304,0.000042953412,0.0002804814,0.0005185334,0.0006897364,0.000008798476],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005000505,0.000053312197,0.000008035245,0.00014023697,0.000009843706,0.0000012730021,0.00021557197,0.000011253619,0.0027333521,0.10394159,0.0013994926,0.89148104],"study_design_scores_gemma":[0.00019436934,0.00012239885,0.000110013796,0.00003988306,0.000026146901,0.00003087228,0.000012889055,0.0072257575,0.002035402,0.024636287,0.9652868,0.00027919622],"about_ca_topic_score_codex":0.0000012566593,"about_ca_topic_score_gemma":2.3846974e-7,"teacher_disagreement_score":0.9638873,"about_ca_system_score_codex":0.000083054205,"about_ca_system_score_gemma":0.00017569287,"threshold_uncertainty_score":0.66611344},"labels":[],"label_agreement":null},{"id":"W4281747453","doi":"","title":"Which Gaussian Process for Bayesian Optimization ?","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bayesian optimization; Gaussian process; Computer science; Process (computing); Bayesian probability; Artificial intelligence; Gaussian; Mathematical optimization; Machine learning; Mathematics","score_opus":0.014972084625462196,"score_gpt":0.25019404305298465,"score_spread":0.23522195842752244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281747453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030050357,0.0002943491,0.9319806,0.021017462,0.0003860426,0.0008600967,0.00007309734,0.00048561033,0.044602256],"genre_scores_gemma":[0.47149256,0.00019504572,0.5230925,0.00019562893,0.00004070597,0.0007093345,0.00058674987,0.00006784377,0.003619653],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99467355,0.0019168609,0.0006858473,0.0014907226,0.00065304385,0.0005799694],"domain_scores_gemma":[0.9924649,0.00072303944,0.00079314417,0.0028455923,0.002912303,0.00026103968],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003772657,0.00046958317,0.0004681608,0.0003112314,0.0008409686,0.0012348904,0.0043700496,0.00030535666,0.00031703233],"category_scores_gemma":[0.0012007238,0.00051249407,0.00021868925,0.0011089009,0.00010045277,0.00049023936,0.0024132498,0.0007719072,0.000009800625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025518666,0.0011345508,0.001081406,0.0016539315,0.00014761742,0.000008083856,0.017107815,0.03609434,0.000121883146,0.88894767,0.0017333203,0.051943887],"study_design_scores_gemma":[0.0005877576,0.0000016575634,0.00040411908,0.00094232045,0.000042728734,0.000016396341,0.00013537768,0.93704224,0.0044479347,0.050161544,0.0053504985,0.0008674026],"about_ca_topic_score_codex":0.00016323951,"about_ca_topic_score_gemma":0.000403147,"teacher_disagreement_score":0.9009479,"about_ca_system_score_codex":0.00016121438,"about_ca_system_score_gemma":0.0011267139,"threshold_uncertainty_score":0.99980193},"labels":[],"label_agreement":null},{"id":"W4288026183","doi":"10.48550/arxiv.1911.08333","title":"Exactly Sparse Gaussian Variational Inference with Application to\\n Derivative-Free Batch Nonlinear State Estimation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Aalto-Yliopisto","keywords":"Covariance; Maximum a posteriori estimation; Mathematics; Gaussian; Mathematical optimization; Covariance matrix; Nonlinear system; Estimation of covariance matrices; Algorithm; Inference; Applied mathematics; Computer science; Artificial intelligence; Statistics","score_opus":0.03158930537731935,"score_gpt":0.19821378548112947,"score_spread":0.16662448010381012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288026183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02324975,0.0000045250445,0.97164136,0.0028087206,0.00013410722,0.00070508855,0.000052216295,0.00023920505,0.0011650092],"genre_scores_gemma":[0.8727467,0.000014413118,0.12584263,0.00085799344,0.0000458277,0.000008930505,0.00007231807,0.000022503209,0.0003886789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744606,0.00009276038,0.00029991535,0.0014598152,0.0002616266,0.00043982125],"domain_scores_gemma":[0.99674004,0.00015645578,0.00048462182,0.0019507799,0.00044876462,0.00021935767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002041269,0.00043587474,0.00036845662,0.00032324228,0.00017139575,0.00034595522,0.0027257488,0.0002476397,0.000027390677],"category_scores_gemma":[0.00007876259,0.0004416801,0.00007758146,0.0010946237,0.00008018508,0.00091164827,0.0016255945,0.0006436446,0.0002840134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005702822,0.00011588576,0.00477801,0.00017348838,0.00007165109,0.0000703914,0.00051501236,0.8082402,0.00003261318,0.18294324,0.0002573723,0.002745107],"study_design_scores_gemma":[0.00044456884,0.00013076565,0.012425538,0.00014292821,0.000030967014,0.000005237495,0.000016498556,0.884678,0.00014956128,0.10108582,0.00032231494,0.00056776236],"about_ca_topic_score_codex":0.00019458511,"about_ca_topic_score_gemma":0.000075624295,"teacher_disagreement_score":0.84949696,"about_ca_system_score_codex":0.00023336607,"about_ca_system_score_gemma":0.0008563334,"threshold_uncertainty_score":0.9998035},"labels":[],"label_agreement":null},{"id":"W4293275986","doi":"10.48550/arxiv.1602.04450","title":"Bayesian Optimization with Safety Constraints: Safe and Automatic\\n Parameter Tuning in Robotics","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Bayesian optimization; Overshoot (microwave communication); Computer science; Robotics; Process (computing); Context (archaeology); Gaussian process; Robot; Bayesian probability; Set (abstract data type); Artificial intelligence; Probabilistic logic; Optimization problem; Life-critical system; Mathematical optimization; Machine learning; Algorithm; Gaussian; Software; Mathematics","score_opus":0.028972554208270735,"score_gpt":0.1760027893119889,"score_spread":0.14703023510371815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293275986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031159613,0.000021926426,0.9925023,0.00038420135,0.00010381014,0.0002548054,0.000009635803,0.00013408305,0.0034732763],"genre_scores_gemma":[0.8598081,0.00011778314,0.13983536,0.00007048774,0.000014931158,8.4182176e-7,0.0000052617725,0.000013823645,0.00013343556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982424,0.000106844716,0.00026526157,0.00092788297,0.00009362105,0.00036398286],"domain_scores_gemma":[0.99858636,0.00016723796,0.00027675796,0.00069321954,0.00011231343,0.00016409508],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019243557,0.0003208369,0.00036498898,0.00028298027,0.00011172293,0.0002074921,0.00084794965,0.00024415174,0.000045765413],"category_scores_gemma":[0.00004263804,0.00028000132,0.000049924867,0.00045982073,0.00029046345,0.0005801049,0.000803107,0.00037344347,0.000007461896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029383216,0.0000620188,0.009831858,0.0002111247,0.000060363713,0.0003941304,0.0003187551,0.72815055,0.000002905122,0.2516691,0.000012927382,0.009256878],"study_design_scores_gemma":[0.0006239418,0.000057151276,0.0014515659,0.0006627903,0.000028543152,0.000022785252,0.0000469667,0.9627672,0.000008010647,0.033901125,0.000010823005,0.0004191295],"about_ca_topic_score_codex":0.000019971058,"about_ca_topic_score_gemma":0.000028631968,"teacher_disagreement_score":0.85669214,"about_ca_system_score_codex":0.00014581514,"about_ca_system_score_gemma":0.00030195678,"threshold_uncertainty_score":0.9999652},"labels":[],"label_agreement":null},{"id":"W4295763119","doi":"10.1002/cjs.11727","title":"Classified generalized linear mixed model prediction incorporating pseudo‐prior information","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation of Sri Lanka; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Class (philosophy); Computer science; Generalized linear mixed model; Matching (statistics); Data mining; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.021463678460413246,"score_gpt":0.21396711617655967,"score_spread":0.19250343771614642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295763119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006507356,0.000026405807,0.9914696,0.00055365235,0.0006728656,0.00006588071,0.0003978231,0.000013228012,0.00029316585],"genre_scores_gemma":[0.5897082,0.0000048935826,0.40979004,0.00036436957,0.000054838125,0.0000037811933,0.000023387598,0.0000053923445,0.000045075034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873537,0.000056954897,0.00055558456,0.000092613474,0.00033626333,0.00022323473],"domain_scores_gemma":[0.9985009,0.00003398844,0.00055341504,0.00016750998,0.00037375515,0.00037041874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003733029,0.00010030772,0.00015311294,0.00030533428,0.00048214188,0.00021217988,0.0005868225,0.00003491892,0.00004232445],"category_scores_gemma":[0.00013583037,0.00010222651,0.0000351655,0.00034692197,0.000038250724,0.00088169245,0.000054195156,0.00032658168,0.0000051571833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028880511,0.00003117719,0.0016027691,0.00008534347,0.000055610526,0.00024423184,0.004180293,0.20262331,0.00017328604,0.6541832,0.048124548,0.08866734],"study_design_scores_gemma":[0.00041528727,0.00015860643,0.00075331173,0.000013440895,0.0000123272175,0.00018644703,0.00015314865,0.9585185,0.000038024158,0.03707261,0.00255961,0.000118657794],"about_ca_topic_score_codex":0.00036176937,"about_ca_topic_score_gemma":0.0005872837,"teacher_disagreement_score":0.7558952,"about_ca_system_score_codex":0.0002609389,"about_ca_system_score_gemma":0.0034731703,"threshold_uncertainty_score":0.6161255},"labels":[],"label_agreement":null},{"id":"W4307288126","doi":"10.1017/s0263574722001497","title":"Variational inference as iterative projection in a Bayesian Hilbert space with application to robotic state estimation","year":2022,"lang":"en","type":"article","venue":"Robotica","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian inference; Inference; Mathematics; Kullback–Leibler divergence; Hilbert space; Artificial intelligence; Gaussian; Bayesian probability; Divergence (linguistics); Subspace topology; Mathematical optimization; Computer science; Algorithm","score_opus":0.006714854513496193,"score_gpt":0.24774157713549078,"score_spread":0.2410267226219946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307288126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009354075,0.0000061032893,0.9906411,0.00651797,0.0000729693,0.0006176877,0.0000022753795,0.00009863149,0.0011078264],"genre_scores_gemma":[0.79828125,9.3942896e-7,0.20074353,0.00033672663,0.000011012423,0.00039233317,0.000013193379,0.000009334968,0.00021165481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833745,0.000112906426,0.00025333968,0.00054554705,0.0004665918,0.0002841672],"domain_scores_gemma":[0.9991969,0.000104043,0.00013208366,0.00034838592,0.00011696788,0.000101642596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024378019,0.00016410826,0.00016394704,0.0002747642,0.00026123936,0.00021926172,0.0004712052,0.00002892535,0.00004312574],"category_scores_gemma":[0.00007406357,0.00015588087,0.00001979391,0.0015899984,0.00002112682,0.0006119304,0.00021670494,0.00024268817,0.000054278684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003156741,0.00012460655,0.0011926255,0.000019001407,0.000007367013,0.00000911031,0.0023842256,0.76553255,0.0001240852,0.22276038,0.000034473072,0.0077800155],"study_design_scores_gemma":[0.00026055297,0.00041166006,0.011993503,0.00003196311,0.0000050308895,0.00004175064,0.00004810735,0.95508695,0.00012341884,0.031660035,0.00010265366,0.00023439975],"about_ca_topic_score_codex":0.00020538186,"about_ca_topic_score_gemma":0.00013067375,"teacher_disagreement_score":0.7973459,"about_ca_system_score_codex":0.00024980408,"about_ca_system_score_gemma":0.00042311897,"threshold_uncertainty_score":0.63566375},"labels":[],"label_agreement":null},{"id":"W4307782464","doi":"10.1016/j.ijforecast.2022.11.001","title":"Distributional regression and its evaluation with the CRPS : bounds and convergence of the minimax risk","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"Agence Nationale de la Recherche","keywords":"Minimax; Convergence (economics); Regression; Econometrics; Mathematics; Regression analysis; Applied mathematics; Mathematical optimization; Statistics; Economics","score_opus":0.04567453432888425,"score_gpt":0.19207663489156773,"score_spread":0.1464021005626835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307782464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90678155,0.0005365421,0.09100196,0.0006792691,0.0001828459,0.0003331492,0.000074909265,0.000021631042,0.0003881587],"genre_scores_gemma":[0.9992992,0.00032187405,0.000117386146,0.000024937824,0.0000116043975,0.000002576064,0.0000068803656,0.0000037545037,0.00021173076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877596,0.00023172985,0.00010804472,0.0005368376,0.0002121677,0.00013527418],"domain_scores_gemma":[0.9987445,0.000102691294,0.00037109334,0.0005268877,0.00020376204,0.000051084222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004309359,0.00015383396,0.00013974182,0.00004302562,0.00047056616,0.00007090174,0.0009618927,0.00007135765,0.000054038373],"category_scores_gemma":[0.000047619207,0.00009275284,0.000044375163,0.00040450372,0.0002227631,0.00019693491,0.0018605558,0.0003753201,8.024218e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001939178,0.00017766398,0.18285786,0.0004316374,0.00023598055,0.00004671561,0.0023142877,0.022419358,0.00012899016,0.78457445,0.00086014887,0.005758976],"study_design_scores_gemma":[0.0005912401,0.0001230634,0.21550708,0.00018545473,0.00021352553,0.000018302957,0.00030236423,0.7266763,0.00028117956,0.055209797,0.0005560225,0.00033569962],"about_ca_topic_score_codex":0.000044510864,"about_ca_topic_score_gemma":0.000023069115,"teacher_disagreement_score":0.7293647,"about_ca_system_score_codex":0.000065425585,"about_ca_system_score_gemma":0.0003607877,"threshold_uncertainty_score":0.37823507},"labels":[],"label_agreement":null},{"id":"W4309044296","doi":"10.48550/arxiv.2211.05836","title":"Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Leverage (statistics); Inference; Weak gravitational lensing; Dark energy; Parameter space; Photometric redshift; Bayesian inference; Computer science; Galaxy; Bayesian probability; Lens (geology); Approximate Bayesian computation; COSMIC cancer database; Physics; Algorithm; Astrophysics; Machine learning; Artificial intelligence; Cosmology; Statistics; Redshift; Optics; Mathematics","score_opus":0.17137045332883735,"score_gpt":0.26759362444889045,"score_spread":0.0962231711200531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309044296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09101381,0.000016902995,0.9076632,0.00011975884,0.0004115592,0.00027698852,0.00005270621,0.00025575314,0.00018930538],"genre_scores_gemma":[0.94588274,0.0000019174206,0.05355218,0.00027366306,0.00003625705,8.9753735e-7,0.0002031321,0.000026171012,0.00002306407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99695396,0.00022282182,0.00032979125,0.0017672678,0.000272488,0.00045368104],"domain_scores_gemma":[0.99544394,0.00095095776,0.00053738826,0.0027475648,0.00018003194,0.00014011284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036937994,0.0004326829,0.0003487161,0.0004827631,0.0005179663,0.0006426806,0.0030749775,0.00013968519,0.00007827328],"category_scores_gemma":[0.00025911248,0.00052306225,0.00012236854,0.0008113521,0.00011266383,0.0012014849,0.0023175282,0.0007900341,0.000016976199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022093891,0.00007688129,0.00451445,0.00009976214,0.00001847976,0.000086811684,0.000051375977,0.97851026,0.000006608485,0.013669156,0.000005597876,0.0029385004],"study_design_scores_gemma":[0.0003933819,0.00003835002,0.0010646373,0.0002541481,0.000065930355,8.308293e-7,0.000031591208,0.983974,0.000036861915,0.01355784,0.00003590162,0.00054652325],"about_ca_topic_score_codex":0.00033003694,"about_ca_topic_score_gemma":0.000016142418,"teacher_disagreement_score":0.8548689,"about_ca_system_score_codex":0.00048138853,"about_ca_system_score_gemma":0.0010073776,"threshold_uncertainty_score":0.9997221},"labels":[],"label_agreement":null},{"id":"W4309299652","doi":"10.48550/arxiv.2211.08262","title":"A mixed-categorical correlation kernel for Gaussian process","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office National d'études et de Recherches Aérospatiales; University of California, San Diego; European Commission; Glenn Research Center; Centre National de la Recherche Scientifique; Polytechnique Montréal; National Aeronautics and Space Administration","keywords":"Categorical variable; Kernel (algebra); Mathematics; Gaussian process; Applied mathematics; Gaussian; Computer science; Algorithm; Residual; Mathematical optimization; Artificial intelligence; Statistics; Discrete mathematics","score_opus":0.07121991222082329,"score_gpt":0.2092945306211843,"score_spread":0.138074618400361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309299652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0128373,0.000059970556,0.9808869,0.00036174152,0.0011173354,0.00061181904,0.000032101154,0.0003371211,0.0037557306],"genre_scores_gemma":[0.9933617,0.000046793783,0.003950738,0.00010547604,0.00009972601,0.000021692478,0.00006871881,0.000028167578,0.002316993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743515,0.000091394555,0.00029383218,0.0015122328,0.00017056864,0.00049684837],"domain_scores_gemma":[0.9979701,0.00012734256,0.00042880888,0.0010402363,0.0002179117,0.00021561408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002594078,0.00038577296,0.0003848811,0.00028149795,0.0004135715,0.0002501151,0.0026640054,0.00030193018,0.00011256866],"category_scores_gemma":[0.000059787202,0.00043558417,0.00025879324,0.0008902482,0.000078443714,0.0005646821,0.0016237925,0.0007584654,0.000046043435],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051666433,0.00017542543,0.001994068,0.00035399702,0.000060096565,0.0001441223,0.0003862656,0.10595571,0.0000027650958,0.8882222,0.00076623197,0.0018874379],"study_design_scores_gemma":[0.00041111084,0.00010225548,0.0011593617,0.000034101555,0.00005580195,0.000010600367,0.00013249814,0.63797516,0.000022496944,0.35832056,0.0012836904,0.0004923864],"about_ca_topic_score_codex":0.00004323192,"about_ca_topic_score_gemma":0.00001582015,"teacher_disagreement_score":0.9805244,"about_ca_system_score_codex":0.00028034963,"about_ca_system_score_gemma":0.00063794217,"threshold_uncertainty_score":0.9998096},"labels":[],"label_agreement":null},{"id":"W4312651588","doi":"10.1109/iros47612.2022.9981548","title":"Mapping of Spatiotemporal Scalar Fields by Mobile Robots using Gaussian Process Regression","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Kriging; Gaussian process; Mobile robot; Computer science; Scalar (mathematics); Sensor fusion; Ground-penetrating radar; Gaussian; Artificial intelligence; Robot; Data mining; Machine learning; Mathematics; Radar","score_opus":0.04725050552240219,"score_gpt":0.30557618322149294,"score_spread":0.25832567769909076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312651588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20826015,0.0007987048,0.7814607,0.0009589141,0.0035648237,0.00084227126,0.00012329311,0.00011862458,0.0038724928],"genre_scores_gemma":[0.9972139,0.00017435198,0.00088595296,0.0001214921,0.00010679595,0.00016756535,0.00003883287,0.000021243815,0.0012698474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663377,0.00017946011,0.000854266,0.0007909418,0.001164141,0.00037742054],"domain_scores_gemma":[0.99832046,0.0000627331,0.0006673465,0.0004683392,0.00030444836,0.00017668342],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051998155,0.00034636917,0.00045967946,0.00036817175,0.00042094468,0.0003838119,0.0014214168,0.00012183172,0.0003991869],"category_scores_gemma":[0.000027025915,0.0003048781,0.00011130149,0.0004665363,0.00009683776,0.00046139007,0.00041496364,0.00048327728,0.000009232561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000480213,0.002594118,0.027594898,0.0024303964,0.00082077534,0.00040510442,0.020256853,0.31536692,0.058478773,0.44603366,0.014178417,0.11135986],"study_design_scores_gemma":[0.00053459243,0.0006676851,0.0003334234,0.00090530317,0.000015834483,0.00017737037,0.0034064653,0.9788216,0.009472036,0.003324085,0.0015813296,0.0007602887],"about_ca_topic_score_codex":0.00031253984,"about_ca_topic_score_gemma":0.000009146616,"teacher_disagreement_score":0.7889538,"about_ca_system_score_codex":0.00016134097,"about_ca_system_score_gemma":0.00023259425,"threshold_uncertainty_score":0.99994034},"labels":[],"label_agreement":null},{"id":"W4312888181","doi":"10.2139/ssrn.4263158","title":"Fast Inference for Quantile Regression with Tens of Millions of Observations","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Quantile regression; Inference; Econometrics; Statistics; Regression; Quantile; Regression analysis; Computer science; Mathematics; Artificial intelligence","score_opus":0.018488760593552516,"score_gpt":0.2599417369887415,"score_spread":0.241452976395189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312888181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058877077,0.00042871566,0.93937916,0.0009879841,0.000074957934,0.00012894071,0.00001485769,0.000016111295,0.00009222236],"genre_scores_gemma":[0.98338443,0.0002270627,0.016055575,0.000032158754,0.000018973731,0.000023086068,0.0000032544378,0.0000076140955,0.00024785518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99841005,0.000043614942,0.00030547281,0.00017490236,0.00031853208,0.0007474099],"domain_scores_gemma":[0.9989407,0.000092397575,0.00041247028,0.000257554,0.00025169458,0.000045206827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005480228,0.00010054504,0.00018424528,0.00014039467,0.00034956654,0.000027495304,0.00083607814,0.000022698056,0.000012057371],"category_scores_gemma":[0.00004973574,0.0000742353,0.00007475528,0.00060363446,0.000047123296,0.00028254575,0.00013480775,0.00057595497,2.6331358e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004224143,0.00014681272,0.0043773516,0.000028791166,0.00004963373,0.0000011409306,0.00045135355,0.0009999151,0.0023454668,0.98150694,0.00008505251,0.009965288],"study_design_scores_gemma":[0.0020593866,0.0062256237,0.008249395,0.00023571546,0.00007602426,0.00069986755,0.0040276293,0.050699394,0.004248,0.9213687,0.0015787125,0.00053152855],"about_ca_topic_score_codex":0.000024873714,"about_ca_topic_score_gemma":0.000106063206,"teacher_disagreement_score":0.9245073,"about_ca_system_score_codex":0.000121071105,"about_ca_system_score_gemma":0.0026742758,"threshold_uncertainty_score":0.47440502},"labels":[],"label_agreement":null},{"id":"W4313016443","doi":"10.1016/j.ifacol.2022.09.470","title":"Automatic State Matching Gaussian Process Ensemble for Wood Planer Control","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weighting; Matching (statistics); Computer science; Gaussian; Process (computing); Gaussian process; State (computer science); Scheme (mathematics); Algorithm; Ensemble learning; Artificial intelligence; Control engineering; Engineering; Mathematics; Statistics","score_opus":0.009670622241952894,"score_gpt":0.24807729290186015,"score_spread":0.23840667065990725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313016443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10105972,0.00027223755,0.88546073,0.00837041,0.00066701527,0.0011152225,0.00032209812,0.0007332495,0.0019993093],"genre_scores_gemma":[0.7646683,0.0000042043625,0.23267438,0.0015747481,0.00009144566,0.00028965267,0.000034786244,0.000029874609,0.0006326568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977006,0.00007167634,0.00041567808,0.0006369338,0.00051168434,0.0006634833],"domain_scores_gemma":[0.9988432,0.00016436273,0.00024519907,0.00048683086,0.00008220639,0.00017823331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037729883,0.00029146645,0.00037015753,0.0001391433,0.0006467844,0.0002644561,0.0012732844,0.000047812217,0.00016700265],"category_scores_gemma":[0.000037704598,0.0002594295,0.00011443023,0.00044801962,0.00003568972,0.00045023876,0.00019267178,0.00030960972,0.00003139648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061113376,0.0030777203,0.0019020154,0.00399078,0.00085617,0.00086165516,0.052064043,0.020051068,0.021770433,0.17473608,0.00061586563,0.71946305],"study_design_scores_gemma":[0.005064022,0.0011494815,0.00082932296,0.00013827486,0.00006853338,0.00039007716,0.0018256846,0.90891045,0.0014229108,0.07610387,0.0026731605,0.0014242119],"about_ca_topic_score_codex":0.000024545125,"about_ca_topic_score_gemma":0.000027642658,"teacher_disagreement_score":0.8888594,"about_ca_system_score_codex":0.00007950527,"about_ca_system_score_gemma":0.00029664004,"threshold_uncertainty_score":0.9999858},"labels":[],"label_agreement":null},{"id":"W4322720982","doi":"10.2172/1958791","title":"Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inference; Computer science; Bayesian inference; Bayesian probability; Artificial neural network; Estimation theory; Posterior probability; Galaxy; Artificial intelligence; Approximate Bayesian computation; Algorithm; Pattern recognition (psychology); Physics; Astrophysics","score_opus":0.10824151819588256,"score_gpt":0.3570253962239304,"score_spread":0.24878387802804786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322720982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026929429,0.0000055590904,0.9711251,0.00083211326,0.00018971828,0.00014427476,0.000007391576,0.0005405258,0.00022585133],"genre_scores_gemma":[0.7793803,2.1147815e-7,0.21995835,0.00054892007,0.000026080123,0.0000025199975,0.000058746336,0.000012251488,0.0000125887445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980867,0.000061270264,0.00031605116,0.00069853803,0.0004333083,0.0004041132],"domain_scores_gemma":[0.9971594,0.0012097668,0.00014881758,0.0012728542,0.00012041568,0.00008869529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036222884,0.00020892693,0.00016688544,0.00030011736,0.0002851046,0.00081538863,0.0010758907,0.000047841844,0.000027232225],"category_scores_gemma":[0.00047600621,0.0001906174,0.00003773886,0.0009987421,0.000051783943,0.0015482935,0.00026679353,0.00015008848,0.000105465384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000470095,0.00002758678,0.002422461,0.000034870824,0.0000039960155,0.000009366944,0.000049670965,0.9359478,0.00011030204,0.005747104,0.000031931882,0.055610217],"study_design_scores_gemma":[0.00025663062,0.000026591115,0.0031577046,0.00013222774,0.000007941884,7.527662e-7,0.000015501404,0.9921105,0.00052543206,0.0034885409,0.000031473017,0.00024671337],"about_ca_topic_score_codex":0.00011616937,"about_ca_topic_score_gemma":0.000010126244,"teacher_disagreement_score":0.7524509,"about_ca_system_score_codex":0.00006836525,"about_ca_system_score_gemma":0.00031943913,"threshold_uncertainty_score":0.78628117},"labels":[],"label_agreement":null},{"id":"W4361250802","doi":"10.1109/itnec56291.2023.10082021","title":"Deep Learning-driven Fast Planning of Informative Sensing for Environmental Field Reconstruction","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Gaussian process; Computer science; Covariance function; Artificial intelligence; Field (mathematics); Covariance; Machine learning; Sampling (signal processing); Deep learning; Process (computing); Function (biology); Artificial neural network; Kriging; Mutual information; Gaussian; Data mining; Algorithm; Covariance matrix; Mathematics; Statistics; Computer vision","score_opus":0.011610573846550476,"score_gpt":0.23406875295073482,"score_spread":0.22245817910418433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361250802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027811967,0.000005290817,0.9672311,0.00015062357,0.000111415524,0.000078891055,8.145028e-7,0.0001015396,0.0045083486],"genre_scores_gemma":[0.92559165,0.000007630106,0.07402424,0.000052713596,0.000018369074,0.0000025404463,0.00000414196,0.0000031325183,0.00029560202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994689,0.0000098136115,0.0001634096,0.00012346874,0.00008899333,0.00014538337],"domain_scores_gemma":[0.9996507,0.00010948845,0.00010052498,0.00009486028,0.000015044497,0.000029354182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007210522,0.000064583546,0.000089482855,0.00008747452,0.00010094302,0.000043868255,0.00015323046,0.000038860697,0.00001575063],"category_scores_gemma":[0.000027443839,0.00005795821,0.0000351219,0.00016437538,0.000023501614,0.00041897735,0.00009180319,0.00007376402,0.0000213636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009849503,0.000007215471,0.005044833,0.000061832434,0.000018828403,0.0000027738888,0.0046492363,0.004476947,0.0013290424,0.005366333,0.00017316619,0.97885996],"study_design_scores_gemma":[0.0001866632,0.00017775515,0.003138758,0.000047323494,0.000002931819,0.000027316493,0.0017464954,0.9812735,0.011198439,0.0017371031,0.00034804156,0.00011570218],"about_ca_topic_score_codex":0.000002643954,"about_ca_topic_score_gemma":0.0000010832232,"teacher_disagreement_score":0.97874427,"about_ca_system_score_codex":0.000011679635,"about_ca_system_score_gemma":0.000013746754,"threshold_uncertainty_score":0.23634672},"labels":[],"label_agreement":null},{"id":"W4366122358","doi":"10.1007/s10915-023-02190-w","title":"A Non-intrusive Solution to the Ill-Conditioning Problem of the Gradient-Enhanced Gaussian Covariance Matrix for Gaussian Processes","year":2023,"lang":"en","type":"article","venue":"Journal of Scientific Computing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Covariance matrix; Covariance; Mathematics; Condition number; Gaussian; Mathematical optimization; Covariance function; CMA-ES; Matrix (chemical analysis); Applied mathematics; Quadratic equation; Eigenvalues and eigenvectors; Algorithm; Statistics; Geometry","score_opus":0.01528928988428733,"score_gpt":0.27249394461731746,"score_spread":0.2572046547330301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366122358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07180349,0.000067488596,0.9162117,0.008993914,0.0020573246,0.00061622367,0.000010671174,0.000046279245,0.00019288706],"genre_scores_gemma":[0.94490916,0.0000040177606,0.05436658,0.00012676677,0.00023920201,0.000012550025,0.0000017592384,0.000011945069,0.00032802648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737835,0.00006007832,0.0008509985,0.0004308602,0.000732169,0.0005475638],"domain_scores_gemma":[0.9964348,0.0002467936,0.0012803691,0.00048696774,0.0014172653,0.00013381106],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021354607,0.00018844867,0.0003067443,0.00035097596,0.0013705977,0.000818979,0.002219311,0.0000535458,0.0000047174253],"category_scores_gemma":[0.00041841937,0.00011251204,0.00018885637,0.00418713,0.00019334597,0.00061395246,0.00047774301,0.0002580882,0.00001531154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002929117,0.00082761183,0.0029071565,0.0053784954,0.00057031744,0.000076438395,0.11230809,0.1840794,0.16619901,0.15133637,0.07862837,0.29739583],"study_design_scores_gemma":[0.0035934122,0.0013540204,0.032314193,0.009226482,0.00022150965,0.0007205522,0.004878504,0.70280313,0.14084762,0.086103134,0.016358454,0.0015789845],"about_ca_topic_score_codex":0.0000067780848,"about_ca_topic_score_gemma":0.00002306533,"teacher_disagreement_score":0.87310565,"about_ca_system_score_codex":0.00008090158,"about_ca_system_score_gemma":0.0007411897,"threshold_uncertainty_score":0.9999295},"labels":[],"label_agreement":null},{"id":"W4366327855","doi":"10.48550/arxiv.2304.08309","title":"Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Government of Canada; Canadian Institute for Advanced Research","keywords":"Laplace's method; Laplace transform; Bayesian probability; Gaussian process; Bayesian optimization; Mathematical optimization; Maximum a posteriori estimation; Mathematics; Artificial neural network; Kernel (algebra); Applied mathematics; Covariance; Gaussian; Computer science; Artificial intelligence; Statistics; Physics; Maximum likelihood; Mathematical analysis","score_opus":0.053379815749307856,"score_gpt":0.18145974651174426,"score_spread":0.1280799307624364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366327855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03999823,0.000029885701,0.95853436,0.00036159853,0.00020991857,0.00031862772,0.0000075759854,0.00008684809,0.0004529421],"genre_scores_gemma":[0.99150556,0.00020944659,0.007386779,0.000028870703,0.000014006057,0.0000011577283,0.000002564121,0.00001053155,0.0008410554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988318,0.000096209034,0.00019473842,0.00061204535,0.0000762438,0.00018895265],"domain_scores_gemma":[0.9988553,0.00007908677,0.00024273907,0.0006758469,0.000088438646,0.000058611513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018555873,0.00017443072,0.00024293085,0.00017383294,0.000066748944,0.00008827738,0.0012324885,0.0001772036,0.000007020125],"category_scores_gemma":[0.00006629403,0.0001530941,0.00007056099,0.0008092392,0.00010486853,0.00022602097,0.0016907209,0.00031428962,0.0000035792852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025919333,0.00007157235,0.01738757,0.00033179365,0.000029949528,0.000051731833,0.0004665614,0.907479,0.00003240632,0.073798396,0.00005960267,0.00026548424],"study_design_scores_gemma":[0.00039330186,0.000021040358,0.006491241,0.00025195812,0.000017576413,0.0000016961299,0.000033447104,0.96699417,0.00016023204,0.02543015,0.00001322621,0.00019196892],"about_ca_topic_score_codex":0.00015414823,"about_ca_topic_score_gemma":0.00009541069,"teacher_disagreement_score":0.9515074,"about_ca_system_score_codex":0.000046469824,"about_ca_system_score_gemma":0.00024238725,"threshold_uncertainty_score":0.6242996},"labels":[],"label_agreement":null},{"id":"W4379534469","doi":"10.3847/2041-8213/acd645","title":"Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise","year":2023,"lang":"en","type":"article","venue":"The Astrophysical Journal Letters","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"Simons Foundation","keywords":"Noise (video); Gaussian noise; Cosmic microwave background; Physics; Gaussian; Probability density function; Statistical physics; Algorithm; Computer science; Statistics; Mathematics; Artificial intelligence; Optics; Anisotropy","score_opus":0.00787970556860507,"score_gpt":0.22060411145230804,"score_spread":0.21272440588370298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379534469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22638157,0.000007319587,0.74175215,0.0308963,0.00012505476,0.00018924555,0.0000059579206,0.0001253538,0.00051706185],"genre_scores_gemma":[0.8844939,0.000007569732,0.10984129,0.0048896596,0.000565347,0.000041855663,0.00000759669,0.000033955046,0.00011885177],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9965846,0.00017796428,0.0004583012,0.0006904597,0.0010130543,0.0010756003],"domain_scores_gemma":[0.9978596,0.00006744553,0.0002743792,0.0010385986,0.000108302855,0.0006516815],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003892412,0.00042774348,0.00058390165,0.0005366126,0.0006336329,0.001136509,0.0025892197,0.000058143938,0.000013009818],"category_scores_gemma":[0.000014624291,0.00024812383,0.00039516727,0.0052906396,0.00015004181,0.0005987856,0.00041100773,0.0007274959,0.00028827],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079301867,0.0015882975,0.0037897974,0.00016608837,0.006203939,0.0019580345,0.015364717,0.14513035,0.6068773,0.08411103,0.050056472,0.083960935],"study_design_scores_gemma":[0.0057955175,0.0021686158,0.5800001,0.0003011719,0.0024694977,0.0010090201,0.0007633689,0.3872385,0.0048758048,0.0093026925,0.0023056979,0.0037699915],"about_ca_topic_score_codex":0.00003360844,"about_ca_topic_score_gemma":0.0000036240097,"teacher_disagreement_score":0.6581123,"about_ca_system_score_codex":0.00008004619,"about_ca_system_score_gemma":0.00013790195,"threshold_uncertainty_score":0.9999971},"labels":[],"label_agreement":null},{"id":"W4380738484","doi":"10.1016/j.neucom.2023.126472","title":"A mixed-categorical correlation kernel for Gaussian process","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Office National d'études et de Recherches Aérospatiales; University of California, San Diego; European Commission; Glenn Research Center; Centre National de la Recherche Scientifique; Polytechnique Montréal; National Aeronautics and Space Administration","keywords":"Categorical variable; Kernel (algebra); Mathematics; Gaussian process; Applied mathematics; Computer science; Gaussian; Residual; Algorithm; Mathematical optimization; Variable kernel density estimation; Artificial intelligence; Kernel method; Statistics; Discrete mathematics","score_opus":0.03386895225232832,"score_gpt":0.2801943517271174,"score_spread":0.24632539947478904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380738484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025343735,0.00001231639,0.96986526,0.001605032,0.00069320766,0.00031296388,9.81003e-7,0.00090694387,0.0012595294],"genre_scores_gemma":[0.9869567,0.00000248713,0.01229751,0.0002782009,0.00021188226,0.000047240297,0.00000730349,0.000020587084,0.00017804568],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983207,0.000031633826,0.00030244942,0.00058392104,0.00026049887,0.00050077774],"domain_scores_gemma":[0.99908817,0.00023171357,0.00015059389,0.00029956375,0.000113853246,0.00011609628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002491916,0.00017152102,0.00017196806,0.0001643182,0.0003267059,0.00028156926,0.00078646885,0.00007511756,0.0000026791836],"category_scores_gemma":[0.00011330021,0.00016018243,0.000077256926,0.0012410643,0.00002265548,0.00039436272,0.00020527185,0.00017604647,0.0001487992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023754825,0.00015423249,0.0058049173,0.00057077396,0.000024545394,0.00011256031,0.002332262,0.014100286,0.00067682675,0.5009006,0.007031006,0.4682682],"study_design_scores_gemma":[0.00030130346,0.00008869319,0.012214923,0.000028036884,0.0000047802696,0.000039858296,0.000036996385,0.94564897,0.0003891822,0.039566316,0.0014632427,0.00021771478],"about_ca_topic_score_codex":0.0000028327836,"about_ca_topic_score_gemma":0.0000010265467,"teacher_disagreement_score":0.961613,"about_ca_system_score_codex":0.000019248915,"about_ca_system_score_gemma":0.00009854967,"threshold_uncertainty_score":0.653205},"labels":[],"label_agreement":null},{"id":"W4381512122","doi":"10.3390/a16070310","title":"Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching","year":2023,"lang":"en","type":"article","venue":"Algorithms","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of North Carolina at Charlotte","keywords":"Kernel density estimation; Probability density function; Density estimation; Algorithm; Nonparametric statistics; Cumulative distribution function; Mathematics; Ranging; Block (permutation group theory); Probability distribution; Parametric statistics; Variable kernel density estimation; Computer science; Multivariate kernel density estimation; Principle of maximum entropy; Statistics; Kernel method; Artificial intelligence; Estimator","score_opus":0.04335569242947228,"score_gpt":0.27074102419238905,"score_spread":0.22738533176291675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381512122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06373832,0.000043912063,0.93463933,0.0005392419,0.00013840689,0.00013373424,0.0000017165537,0.00030765898,0.00045768943],"genre_scores_gemma":[0.6602482,0.000016123575,0.33960882,0.000059883478,0.00002260749,0.00001466448,0.0000024759954,0.0000035449318,0.000023655255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989834,0.000037199938,0.0001607168,0.00037958112,0.00020114516,0.00023797595],"domain_scores_gemma":[0.9994086,0.00013402857,0.00007066363,0.00024864945,0.00007865725,0.000059390368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028447792,0.00010464476,0.00012713796,0.00008060178,0.00025113925,0.00022191815,0.00022935116,0.00004685693,0.0000038483217],"category_scores_gemma":[0.00014301334,0.000096047785,0.000024182273,0.001187289,0.00005303667,0.0009474461,0.00020508262,0.00011903218,0.000078450474],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006247176,0.0000794147,0.0058204904,0.00008744441,0.000031053125,0.00007412174,0.0034927803,0.0026486479,0.000057638514,0.23887487,0.00035869822,0.7484686],"study_design_scores_gemma":[0.00010882338,0.00005600363,0.03429268,0.000028729988,0.000004674427,0.00002154478,0.00003185464,0.7256878,0.00037511624,0.2392214,0.000040664723,0.0001307312],"about_ca_topic_score_codex":0.00010033597,"about_ca_topic_score_gemma":0.000008957818,"teacher_disagreement_score":0.74833786,"about_ca_system_score_codex":0.000029761832,"about_ca_system_score_gemma":0.000052216834,"threshold_uncertainty_score":0.3916715},"labels":[],"label_agreement":null},{"id":"W4385559495","doi":"10.1017/eds.2023.22","title":"Environmental sensor placement with convolutional Gaussian neural processes","year":2023,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"Natural Environment Research Council; Engineering and Physical Sciences Research Council; Sight Research UK; UK Research and Innovation; Alan Turing Institute; National Science Foundation","keywords":"Computer science; Gaussian process; Convolutional neural network; Scalability; Gaussian; Flexibility (engineering); Kriging; Artificial intelligence; Machine learning; Probabilistic logic; Anomaly detection; Sampling (signal processing); Data mining; Process (computing); Computer vision; Statistics; Mathematics","score_opus":0.02121041352193427,"score_gpt":0.2344499535427245,"score_spread":0.2132395400207902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385559495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8058684,0.00046498716,0.17923261,0.005012352,0.000908946,0.0013699281,0.0025896013,0.0011267238,0.0034264761],"genre_scores_gemma":[0.98741376,0.00012665683,0.011479964,0.0002753368,0.000052024512,0.00002595598,0.0002753904,0.0000146057455,0.00033629278],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996179,0.000028376437,0.00026051456,0.0013955572,0.0013581098,0.0007784358],"domain_scores_gemma":[0.99797976,0.00005787578,0.00013068231,0.0015249894,0.0000061544165,0.00030055578],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043739876,0.00027609072,0.00016392315,0.00018868833,0.0007075232,0.00039302578,0.003970802,0.000039467115,0.00018462249],"category_scores_gemma":[0.000030191346,0.00022016637,0.000019805699,0.0011429576,0.001292085,0.0040687947,0.0026233098,0.00017026476,0.00093216717],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045307496,0.005090686,0.3522173,0.00086070783,0.00032984657,0.0040033935,0.009746279,0.011412048,0.34945044,0.046016667,0.025717812,0.19470175],"study_design_scores_gemma":[0.0020513043,0.0009708026,0.660558,0.00012310836,0.00004154575,0.00083775533,0.0021426762,0.29467937,0.01321736,0.0008719897,0.022346523,0.0021595624],"about_ca_topic_score_codex":0.0000074552427,"about_ca_topic_score_gemma":0.000004422596,"teacher_disagreement_score":0.33623308,"about_ca_system_score_codex":0.00015708232,"about_ca_system_score_gemma":0.00024545522,"threshold_uncertainty_score":0.99984574},"labels":[],"label_agreement":null},{"id":"W4385857783","doi":"10.1146/annurev-statistics-031017-100108","title":"Inverse Problems for Physics-Based Process Models","year":2023,"lang":"en","type":"article","venue":"Annual Review of Statistics and Its Application","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inverse problem; Context (archaeology); Bayesian probability; Inference; Inverse; Bayesian inference; Process (computing); Computer science; Calibration; Data science; Statistics; Mathematics; Artificial intelligence; Geography","score_opus":0.025461736151769273,"score_gpt":0.3046267260139326,"score_spread":0.2791649898621633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385857783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004273249,0.0016422803,0.99652976,0.0006310078,0.000013561741,0.0006804512,0.00029681416,0.000040325525,0.00012304276],"genre_scores_gemma":[0.627514,0.08409556,0.2807065,0.0035935193,0.00013943895,0.0028402417,0.00081747066,0.000057247235,0.00023601505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992701,0.000008146844,0.00022935269,0.00022619055,0.00014382003,0.00012239759],"domain_scores_gemma":[0.9991046,0.00006142505,0.00017851841,0.00016690533,0.00044240642,0.000046154008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019916928,0.000083209714,0.0001616582,0.000026569049,0.000056059907,0.000020956692,0.00023517375,0.000021711361,0.0000010374457],"category_scores_gemma":[0.00004075523,0.00007078745,0.000019479035,0.00039018775,0.000020587124,0.00023459965,0.000034690802,0.000031988715,0.000008373596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013978046,0.000029540659,0.000004199738,0.016167732,0.000004406903,1.487685e-7,0.00018328184,0.00022579292,0.0000694281,0.78996515,0.002149195,0.1911997],"study_design_scores_gemma":[0.00012097357,0.00007543664,0.000045177243,0.0008772377,0.000013245619,3.8159084e-7,0.0000104869005,0.7492581,0.00027855387,0.24556044,0.003655693,0.00010426844],"about_ca_topic_score_codex":0.0000026915927,"about_ca_topic_score_gemma":8.3658e-7,"teacher_disagreement_score":0.7490323,"about_ca_system_score_codex":0.000004779772,"about_ca_system_score_gemma":0.00008010425,"threshold_uncertainty_score":0.28866282},"labels":[],"label_agreement":null},{"id":"W4385966270","doi":"10.5281/zenodo.8256422","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 4","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Mutation; Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Computational biology; Machine learning; Genetics; Biology; Gene","score_opus":0.04788279144490647,"score_gpt":0.24404469082500574,"score_spread":0.19616189938009926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385966270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012947397,0.00004357928,0.992885,0.00057717226,0.000110074325,0.0005652568,0.00004208626,0.00041408918,0.004068029],"genre_scores_gemma":[0.96118355,0.0000038282337,0.037838265,0.00022349584,0.0000879582,0.0000014460447,0.00028582744,0.0003453941,0.000030221647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982697,0.0002721545,0.00026944565,0.0004904959,0.00028739485,0.0004108494],"domain_scores_gemma":[0.9990026,0.00015747502,0.00013089566,0.00036768106,0.00023862849,0.00010276033],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006108938,0.00012003299,0.00012376386,0.0001773803,0.0018924259,0.00075687456,0.0014504979,0.00003190602,0.00039737436],"category_scores_gemma":[0.00083725184,0.00013327578,0.00003495185,0.0010670074,0.000060931055,0.0004324542,0.0014504876,0.00037818582,0.00005281556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047933587,0.00016656479,0.000005922948,0.00008547768,0.00001062516,0.000031457454,0.002678198,0.35502353,0.000030372337,0.39059278,0.002762762,0.24856438],"study_design_scores_gemma":[0.00022373258,0.00028141838,0.00010190848,0.000019601986,0.0000032601936,0.000086877684,0.00012501181,0.85806566,0.0000033081485,0.12887685,0.012073149,0.0001392],"about_ca_topic_score_codex":0.000008939082,"about_ca_topic_score_gemma":6.099022e-7,"teacher_disagreement_score":0.9598888,"about_ca_system_score_codex":0.00012366526,"about_ca_system_score_gemma":0.000007921675,"threshold_uncertainty_score":0.999407},"labels":[],"label_agreement":null},{"id":"W4386050073","doi":"10.11159/icsta23.001","title":"Deep Kernel Learning based Gaussian Processes for Bayesian Image Regression Analysis","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer science; Kernel (algebra); Gaussian process; Pattern recognition (psychology); Machine learning; Bayesian probability; Regression; Kernel regression; Regression analysis; Kriging; Gaussian; Statistics; Mathematics","score_opus":0.01924558642818343,"score_gpt":0.2919499462524115,"score_spread":0.2727043598242281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386050073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066446216,0.000017521137,0.99262655,0.002494342,0.0000586449,0.00038479842,0.0001564056,0.00012056883,0.0034766837],"genre_scores_gemma":[0.9447423,0.00009828413,0.053316213,0.00016391385,0.000058362948,0.00035388555,0.00005895101,0.000016932378,0.0011911548],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985119,0.000020500076,0.000365339,0.0005147193,0.0003499997,0.00023751885],"domain_scores_gemma":[0.9975829,0.00053553464,0.000457459,0.00021498809,0.0011204825,0.000088595865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006560412,0.0002025158,0.00023666256,0.00033730836,0.00041929208,0.00042221637,0.0013899205,0.000067395726,0.000040736162],"category_scores_gemma":[0.00066499395,0.00015071942,0.0000820879,0.0011891733,0.00022137721,0.0003119985,0.00024627306,0.00018501915,0.000009572837],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049300026,0.00005491747,0.0010450897,0.00018901017,0.00008487003,2.3845553e-7,0.0002295575,0.00009996128,0.0012147931,0.98848987,0.00035287338,0.008189516],"study_design_scores_gemma":[0.00030778456,0.00008107565,0.0022789682,0.00013825447,0.00010996373,0.000002227741,0.0005357944,0.28805804,0.0038364425,0.7031835,0.0012136005,0.00025439673],"about_ca_topic_score_codex":0.0000041332173,"about_ca_topic_score_gemma":0.000004678875,"teacher_disagreement_score":0.94407785,"about_ca_system_score_codex":0.000029931763,"about_ca_system_score_gemma":0.00013639519,"threshold_uncertainty_score":0.614616},"labels":[],"label_agreement":null},{"id":"W4386373863","doi":"10.7717/peerj-cs.1516","title":"PyMC: a modern, and comprehensive probabilistic programming framework in Python","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":792,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"","keywords":"Computer science; Python (programming language); Probabilistic logic; Programming language; Syntax; Theoretical computer science; Statistical model; Variety (cybernetics); Artificial intelligence","score_opus":0.02204423488414791,"score_gpt":0.27558336004011846,"score_spread":0.25353912515597055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386373863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10049506,0.0001048004,0.89618224,0.0018301985,0.00045172824,0.0003252849,9.095494e-7,0.0005106573,0.00009912422],"genre_scores_gemma":[0.70184994,0.000015832966,0.2977507,0.0002678005,0.000053233984,0.00003466573,6.4539165e-7,0.000008296925,0.000018873578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706745,0.000043543867,0.00031338484,0.0011078942,0.0006667744,0.0008009801],"domain_scores_gemma":[0.9985405,0.0002189619,0.00009527702,0.0006918828,0.00022296367,0.00023040154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006312973,0.00022708983,0.00026356947,0.0005055296,0.0003081853,0.0010262955,0.0017794906,0.00007111495,0.0000017927762],"category_scores_gemma":[0.00012080376,0.00020414683,0.00003598937,0.0042575486,0.00050305785,0.0011197022,0.0014483144,0.00028346656,0.00004774491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005171526,0.00009265203,0.004258906,0.00018285474,0.000004940455,0.00015393636,0.0060386746,0.0029533384,0.00041729896,0.17413403,0.00013758741,0.8116206],"study_design_scores_gemma":[0.00016503093,0.00010498957,0.04534252,0.00015705885,0.0000016644682,0.000051936335,0.000022882039,0.8381396,0.000086740576,0.11521409,0.00043386512,0.00027963723],"about_ca_topic_score_codex":0.000024774834,"about_ca_topic_score_gemma":0.000008696206,"teacher_disagreement_score":0.83518624,"about_ca_system_score_codex":0.000061252445,"about_ca_system_score_gemma":0.00026103779,"threshold_uncertainty_score":0.9896591},"labels":[],"label_agreement":null},{"id":"W4387048691","doi":"10.21203/rs.3.rs-3359335/v1","title":"Efficient Modeling of Quasi-Periodic Data with Seasonal Gaussian Process","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"","keywords":"Gaussian process; Gaussian; Inference; Basis function; Applied mathematics; Spline (mechanical); Population; Prior probability; Representation (politics); Computer science; Basis (linear algebra); Amplitude; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence; Mathematical analysis; Bayesian probability","score_opus":0.12891369872685146,"score_gpt":0.3989576030171133,"score_spread":0.2700439042902618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387048691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025949351,0.0007176471,0.9675089,0.002571994,0.00021443696,0.001106272,0.00037143743,0.00036611085,0.0011938508],"genre_scores_gemma":[0.98620605,0.000094115254,0.013026444,0.000012426643,0.00013854002,0.00015405797,0.00017330986,0.00005651781,0.00013855864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928136,0.00028737352,0.0005662933,0.0019956534,0.0032304067,0.001106706],"domain_scores_gemma":[0.9937736,0.00021272185,0.00023239109,0.004034923,0.0013754481,0.00037091802],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002498466,0.0004081711,0.00059340923,0.0006650145,0.00036135357,0.00078567915,0.007938279,0.00029545987,0.000029478482],"category_scores_gemma":[0.00036601798,0.0003182776,0.00009040266,0.0019527508,0.0003086634,0.00025559065,0.008256177,0.0018446833,0.0000861388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032105442,0.0021014458,0.0067972788,0.034793384,0.00041967392,0.00085310347,0.010550466,0.8566672,0.000060880204,0.05963041,0.0015407472,0.026264345],"study_design_scores_gemma":[0.0002427382,0.00025833052,0.0006163444,0.0031501208,0.000010691945,0.000014311005,0.0004882087,0.9869379,0.000041320436,0.007824777,0.000039422317,0.00037586185],"about_ca_topic_score_codex":0.00030168082,"about_ca_topic_score_gemma":0.0000950853,"teacher_disagreement_score":0.9602567,"about_ca_system_score_codex":0.00011943509,"about_ca_system_score_gemma":0.0040060556,"threshold_uncertainty_score":0.9999269},"labels":[],"label_agreement":null},{"id":"W4387540966","doi":"10.1038/s41586-023-06574-8","title":"State estimation of a physical system with unknown governing equations","year":2023,"lang":"en","type":"article","venue":"Nature","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; State (computer science); Estimation; Physical system; Focus (optics); Bayesian probability; Bayesian inference; Mathematical optimization; Applied mathematics; Mathematics; Artificial intelligence; Algorithm","score_opus":0.00723078163461809,"score_gpt":0.2482160131018002,"score_spread":0.2409852314671821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387540966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02578197,0.000027848391,0.9719395,0.00045196596,0.00006267334,0.000073854324,0.000007925952,0.00022413912,0.0014300895],"genre_scores_gemma":[0.9912819,7.9697406e-7,0.008517176,0.000024341707,0.000015454962,0.000006762556,0.00000556313,0.0000041455714,0.00014381814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937415,0.0000136389335,0.00008022479,0.00015414464,0.0002624172,0.00011540966],"domain_scores_gemma":[0.99954253,0.00007487434,0.000082966915,0.0001892106,0.00008137094,0.00002902404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007293565,0.000062918894,0.000089901485,0.00005534988,0.000055869743,0.00005463798,0.00027054298,0.00009128584,7.507565e-7],"category_scores_gemma":[0.00003732023,0.00004524424,0.000020364476,0.0008248677,0.000015687661,0.0002586958,0.00005608691,0.00034290415,0.000042557647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006151616,0.00003083146,0.00012598073,0.00032322897,0.00001872012,0.000018615994,0.0017650723,0.030387532,0.00041481983,0.9334802,0.00042927393,0.032999568],"study_design_scores_gemma":[0.000113020214,0.00006203731,0.0019643193,0.0001451609,0.0000040781806,0.000004039182,0.00003288766,0.9945364,0.00088304724,0.0020302215,0.00015307864,0.00007172486],"about_ca_topic_score_codex":0.0000053595472,"about_ca_topic_score_gemma":0.0000041760686,"teacher_disagreement_score":0.9655,"about_ca_system_score_codex":0.000021082185,"about_ca_system_score_gemma":0.000078001205,"threshold_uncertainty_score":0.18450066},"labels":[],"label_agreement":null},{"id":"W4389262603","doi":"10.1016/j.biortech.2023.130147","title":"Bayesian uncertainty quantification for anaerobic digestion models","year":2023,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Python (programming language); Bayesian probability; Computer science; Uncertainty quantification; Anaerobic digestion; Bootstrapping (finance); Flexibility (engineering); Benchmark (surveying); Machine learning; Data mining; Artificial intelligence; Econometrics; Mathematics; Statistics","score_opus":0.02781760362219225,"score_gpt":0.2630317088514593,"score_spread":0.23521410522926708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389262603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01645049,0.0000717124,0.9709852,0.00972264,0.00012948588,0.00031058805,0.000007888191,0.0021204343,0.00020160046],"genre_scores_gemma":[0.96800226,0.000029152681,0.031425554,0.00008670352,0.000032006854,0.00016708353,0.000024173722,0.000014610509,0.00021843213],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864817,0.000017127526,0.000237419,0.0005386699,0.00013559048,0.00042301172],"domain_scores_gemma":[0.9989433,0.00005813101,0.00011790474,0.0007113428,0.000111151196,0.000058133457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022075455,0.0001504934,0.0001644124,0.00059616804,0.00021486009,0.00009644633,0.0010669474,0.0002442765,0.0000029956668],"category_scores_gemma":[0.00008654389,0.00013959828,0.00005612891,0.0019910524,0.00011351021,0.00021925285,0.00018528146,0.00011920869,0.00007864532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070084902,0.000030774332,0.00026105074,0.000034102373,0.000009291642,0.0000042903616,0.00014788588,0.002462875,0.00404304,0.89399,0.0016199443,0.09738977],"study_design_scores_gemma":[0.00023901217,0.00013354598,0.00064851274,0.000026833208,0.0000061200303,0.000011172954,0.000089193636,0.67400485,0.0035713757,0.31214336,0.008895589,0.00023042363],"about_ca_topic_score_codex":0.000014347638,"about_ca_topic_score_gemma":0.000011739221,"teacher_disagreement_score":0.9515518,"about_ca_system_score_codex":0.000040856,"about_ca_system_score_gemma":0.000061490195,"threshold_uncertainty_score":0.5692653},"labels":[],"label_agreement":null},{"id":"W4389763759","doi":"10.1109/mcse.2023.3342149","title":"An Intuitive Tutorial to Gaussian Process Regression","year":2023,"lang":"en","type":"article","venue":"Computing in Science & Engineering","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":281,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaussian process; Regression; Computer science; Process (computing); Kriging; Econometrics; Gaussian; Machine learning; Mathematics; Statistics; Programming language; Chemistry; Computational chemistry","score_opus":0.011715792230822395,"score_gpt":0.30168908710326325,"score_spread":0.28997329487244083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389763759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48674744,0.0000072686184,0.5106686,0.00022221658,0.0012951838,0.00014041257,4.7011915e-7,0.0006294261,0.0002889833],"genre_scores_gemma":[0.97149825,0.0000013664016,0.028171888,0.00006085736,0.00023532653,0.000010480311,7.7985e-7,0.0000125338465,0.000008507869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974427,0.000017854865,0.00029775602,0.0008182898,0.00060713204,0.0008162172],"domain_scores_gemma":[0.99887997,0.000070103815,0.000063628824,0.0005368357,0.00013608129,0.0003133987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011767125,0.00020129111,0.00019659412,0.00087528117,0.0002513118,0.0005767868,0.0023457664,0.000053942724,0.0000021066235],"category_scores_gemma":[0.00036416948,0.00018244513,0.00002612266,0.0069197305,0.00007230706,0.0012926679,0.0005099174,0.00023474493,0.000058506917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011195458,0.00011204233,0.00714648,0.000118696844,0.0000043604327,0.00020779854,0.025568629,0.61992425,0.064769015,0.19560973,0.00013177076,0.08639605],"study_design_scores_gemma":[0.00015114908,0.000101048994,0.03561406,0.00033944022,7.5282946e-7,0.000013186601,0.00014323657,0.952339,0.009701161,0.0010902002,0.00016926427,0.00033752358],"about_ca_topic_score_codex":0.000010442042,"about_ca_topic_score_gemma":0.0000021139153,"teacher_disagreement_score":0.4847508,"about_ca_system_score_codex":0.000121090314,"about_ca_system_score_gemma":0.00027473335,"threshold_uncertainty_score":0.74398965},"labels":[],"label_agreement":null},{"id":"W4390042939","doi":"10.1088/2632-2153/ad17d3","title":"Neural network field theories: non-Gaussianity, actions, and locality","year":2023,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"National Science Foundation","keywords":"Path integral formulation; Field (mathematics); Field theory (psychology); Auxiliary field; Feynman diagram; Artificial neural network; Locality; Limit (mathematics); Action (physics); Mathematics; Statistical physics; Measure (data warehouse); Physics; Computer science; Mathematical analysis; Pure mathematics; Quantum mechanics; Mathematical physics; Artificial intelligence","score_opus":0.008662183483395319,"score_gpt":0.25987356349536744,"score_spread":0.25121138001197213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390042939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5602395,0.00070579903,0.3376865,0.090386935,0.00078284985,0.00034849835,0.0000020214218,0.0028851298,0.0069627897],"genre_scores_gemma":[0.9968424,0.00012553734,0.0024331412,0.0003749834,0.00003118004,0.000010177098,5.7991133e-7,0.000004528412,0.00017748863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985989,0.000026447275,0.00014427603,0.00052559335,0.00022807594,0.00047673556],"domain_scores_gemma":[0.9993145,0.00011045109,0.00007070708,0.00032399636,0.00009153925,0.00008883019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008272967,0.00012741216,0.00015348011,0.000351843,0.0010178733,0.00031602927,0.00076883106,0.00010199988,0.000007538117],"category_scores_gemma":[0.00045597204,0.000103674975,0.000012785821,0.003787925,0.00069132564,0.0004852451,0.00094942487,0.00048190833,0.000019675565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003926869,0.000013985362,0.1097644,0.000033065553,0.0000057923744,0.00003206123,0.00028303752,0.00015867074,0.0006372661,0.52350885,0.0003181207,0.3652408],"study_design_scores_gemma":[0.00031273093,0.0005784323,0.041037258,0.00005028868,0.000008326589,0.00023711124,0.00030746928,0.6610682,0.0011330262,0.28517517,0.00965976,0.00043223606],"about_ca_topic_score_codex":0.000065174936,"about_ca_topic_score_gemma":0.000022757966,"teacher_disagreement_score":0.66090953,"about_ca_system_score_codex":0.000013160159,"about_ca_system_score_gemma":0.00009596706,"threshold_uncertainty_score":0.78287613},"labels":[],"label_agreement":null},{"id":"W4390414985","doi":"10.1016/j.neucom.2023.127183","title":"Analytically tractable heteroscedastic uncertainty quantification in Bayesian neural networks for regression tasks","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Homoscedasticity; Heteroscedasticity; Inference; Computer science; Covariate; Bayesian probability; Artificial neural network; Gaussian; Bayesian inference; Gaussian process; Artificial intelligence; Predictive inference; Regression; Mathematics; Uncertainty quantification; Machine learning; Statistics; Frequentist inference","score_opus":0.03047839580974849,"score_gpt":0.29478439677707613,"score_spread":0.26430600096732765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390414985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04835664,0.000018985793,0.94906276,0.0012600748,0.00044952502,0.00034287636,0.0000013648497,0.00037369537,0.0001340568],"genre_scores_gemma":[0.991918,0.0000056015288,0.007560627,0.00028892653,0.000121397636,0.000031215226,0.0000142075005,0.000019927558,0.00004008287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978237,0.00008172398,0.00048106664,0.0007227658,0.0002267336,0.00066397834],"domain_scores_gemma":[0.99869835,0.00047513357,0.00018536682,0.0004376557,0.0000847588,0.00011872462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004564515,0.00021085731,0.0002539568,0.00025341308,0.00024557693,0.00035156292,0.000795605,0.00009672243,0.0000020249604],"category_scores_gemma":[0.00016331516,0.00018858373,0.00008838215,0.0015040116,0.000030945852,0.0003730446,0.00020627055,0.00027882098,0.00001028532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036558307,0.00010749689,0.005378203,0.00017325525,0.000007865253,0.000066815,0.00034608121,0.67480326,0.0017061926,0.015080944,0.00057366316,0.30171967],"study_design_scores_gemma":[0.00031362512,0.000085048814,0.015147453,0.00009079255,0.0000038653875,0.000011388856,0.000014718717,0.9814946,0.000114808994,0.0024150675,0.0001152841,0.00019331927],"about_ca_topic_score_codex":0.000016569451,"about_ca_topic_score_gemma":0.000015336349,"teacher_disagreement_score":0.9435614,"about_ca_system_score_codex":0.000035513258,"about_ca_system_score_gemma":0.00005084367,"threshold_uncertainty_score":0.76902217},"labels":[],"label_agreement":null},{"id":"W4390618520","doi":"10.1080/10618600.2024.2302528","title":"A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Discovery Eye Foundation","keywords":"Ode; Ordinary differential equation; Collocation (remote sensing); Collocation method; Applied mathematics; Nonlinear system; Bayesian probability; Computer science; Orthogonal collocation; Mathematics; Mathematical optimization; Bayes estimator; Estimation theory; Gaussian; Basis (linear algebra); Numerical integration; Algorithm; Differential equation; Mathematical analysis; Artificial intelligence; Machine learning","score_opus":0.01458855396392059,"score_gpt":0.3136028009829331,"score_spread":0.29901424701901247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390618520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065327605,0.00015067476,0.9972751,0.0015298272,0.00022753875,0.00010645725,0.000037578997,0.000013617052,0.00000588891],"genre_scores_gemma":[0.4730597,0.000008009029,0.5268382,0.0000478603,0.000021925865,0.0000051763022,0.000009608152,0.0000030102576,0.0000065020877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894667,0.0000605654,0.00046666682,0.00015598835,0.00024991325,0.00012021304],"domain_scores_gemma":[0.99773836,0.0017603968,0.0001310531,0.00004802287,0.00024136294,0.00008079515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028853034,0.000098711105,0.00017481355,0.0003141909,0.00006854751,0.00028567621,0.00015979564,0.000052349922,0.000008298211],"category_scores_gemma":[0.00020448337,0.0000777218,0.000056410587,0.00043249465,0.000043214168,0.0003560106,0.000022301878,0.00018143996,6.7206764e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002441425,0.00006467187,0.000052918098,0.000100280995,0.000029411636,0.000017851506,0.00021287076,0.0117896395,0.000010156262,0.7454953,0.00026059215,0.24194187],"study_design_scores_gemma":[0.00015899779,0.00014332405,0.002109404,0.000056141173,0.000012354593,0.000044008473,0.0000047236676,0.52617234,0.000003859226,0.47120002,0.000045773402,0.00004901199],"about_ca_topic_score_codex":0.000005617853,"about_ca_topic_score_gemma":0.0000057929674,"teacher_disagreement_score":0.5143827,"about_ca_system_score_codex":0.000029240315,"about_ca_system_score_gemma":0.00018208216,"threshold_uncertainty_score":0.3169403},"labels":[],"label_agreement":null},{"id":"W4390720380","doi":"10.1017/9781009299909.007","title":"Variational Inference","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Estimator; Bayesian inference; Divergence (linguistics); Computer science; Kullback–Leibler divergence; Nonlinear system; Applied mathematics; Gaussian; Bayesian probability; Artificial intelligence; Mathematics; Algorithm; Mathematical optimization; Machine learning; Statistics","score_opus":0.01861129605764767,"score_gpt":0.2043258690751493,"score_spread":0.18571457301750163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390720380","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.2248556e-7,0.00013567142,0.18317357,0.00009115013,0.00040515885,0.00013568935,0.00011663329,0.00030899612,0.81563234],"genre_scores_gemma":[0.0013093888,0.00006909713,0.0031072192,0.000095446405,0.0001306518,7.454712e-7,0.000022309761,0.000025143107,0.99524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983859,0.000012306027,0.00019391728,0.0007545111,0.000374864,0.0002785173],"domain_scores_gemma":[0.9986793,0.000080656435,0.0001601536,0.0007106881,0.00019536637,0.00017384689],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007419472,0.00036001156,0.000290777,0.00023067473,0.00015083562,0.0003183521,0.0016191469,0.00031427768,0.000010341101],"category_scores_gemma":[0.000008271809,0.00039459387,0.00016963611,0.000022918224,0.000121306686,0.00028026386,0.0010600063,0.0005452339,0.00019357908],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004487193,0.000004465688,4.7279525e-7,0.000107616004,0.00006591787,0.00031943308,0.00003360739,0.0000019325958,0.0000049541836,0.98376626,0.013626057,0.002064803],"study_design_scores_gemma":[0.00015057468,0.000040784198,0.000011187055,0.00026581265,0.00007309761,0.000030084937,0.0000025818415,0.0026336059,0.000046319077,0.0070305783,0.98920107,0.00051427673],"about_ca_topic_score_codex":0.000037458198,"about_ca_topic_score_gemma":0.000001114577,"teacher_disagreement_score":0.97673565,"about_ca_system_score_codex":0.00014291177,"about_ca_system_score_gemma":0.00039054334,"threshold_uncertainty_score":0.9998506},"labels":[],"label_agreement":null},{"id":"W4390720519","doi":"10.1017/9781009299909.004","title":"Linear-Gaussian Estimation","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kalman filter; Trajectory; Context (archaeology); Gaussian; Gaussian process; Computer science; Extended Kalman filter; Linear system; Nonlinear system; Maximum a posteriori estimation; Mathematical optimization; Mathematics; Artificial intelligence; Statistics","score_opus":0.01735878868919154,"score_gpt":0.2085548131773438,"score_spread":0.19119602448815226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390720519","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015881899,0.00015099197,0.2067795,0.00013058641,0.0003890968,0.0001830121,0.0000684665,0.00043121638,0.7918655],"genre_scores_gemma":[0.001299214,0.000057612535,0.0076019685,0.00007478557,0.0001232719,7.4497916e-7,0.000027287744,0.00003701189,0.9907781],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99835026,0.00001270672,0.00021679801,0.0007813518,0.00033457763,0.00030431192],"domain_scores_gemma":[0.9986244,0.00003547683,0.00017869298,0.00083859655,0.00012345963,0.00019941575],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007793698,0.00040046737,0.00032830582,0.00026126456,0.00017140295,0.00026363676,0.0014094622,0.0003551079,0.000004953831],"category_scores_gemma":[0.0000054607867,0.00042895795,0.00019578496,0.000025861922,0.00013096475,0.0003092864,0.0008162728,0.00056549505,0.00022794867],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005615428,0.000004366259,1.2273664e-7,0.00018809522,0.000053493488,0.0004664357,0.000040652107,0.00000628754,0.0000047919916,0.97758394,0.013571323,0.008074894],"study_design_scores_gemma":[0.0001845525,0.000061516614,0.0000038017931,0.00047711463,0.00011047409,0.000059800946,0.000004514913,0.021485733,0.0001765554,0.0027634632,0.97407037,0.00060207484],"about_ca_topic_score_codex":0.000038578775,"about_ca_topic_score_gemma":0.0000011711535,"teacher_disagreement_score":0.97482044,"about_ca_system_score_codex":0.00015135542,"about_ca_system_score_gemma":0.0002236542,"threshold_uncertainty_score":0.99981624},"labels":[],"label_agreement":null},{"id":"W4391007130","doi":"10.1016/j.engappai.2024.107843","title":"Wood planer control: Predictive and prescriptive approaches via Automatic State Matching Gaussian processes","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; Université Laval","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Leverage (statistics); Weighting; Gaussian process; Model predictive control; Dimensionality reduction; Machine learning; Artificial intelligence; Gaussian; Data mining; Control (management)","score_opus":0.017103154423299283,"score_gpt":0.2281480332083735,"score_spread":0.21104487878507422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391007130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016362957,0.0009777186,0.9959403,0.0002526136,0.000083258856,0.00041576102,0.000025039484,0.00041880365,0.00025015874],"genre_scores_gemma":[0.94666433,0.000038354763,0.052919574,0.000009554176,0.000045729877,0.00028188762,0.0000032222417,0.000016263299,0.000021099753],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987228,0.000013717358,0.0003884366,0.000442458,0.00019359548,0.00023899341],"domain_scores_gemma":[0.9992195,0.00021496895,0.000077916804,0.0003155593,0.000079635654,0.00009243742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018123559,0.00018679614,0.00019182534,0.00019798413,0.00008544163,0.00028114687,0.0005120968,0.00005344362,0.000004750963],"category_scores_gemma":[0.000036142956,0.00017039561,0.000031951367,0.00075268804,0.00008297491,0.00048119976,0.00009169249,0.000186806,0.000021102092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008625279,0.00012702314,0.000018033747,0.0018936538,0.00011956968,0.0000073014558,0.007655413,0.032629393,0.0024520233,0.5989741,0.000013384252,0.35610148],"study_design_scores_gemma":[0.000013281812,0.000060670074,0.00006852154,0.00019045071,0.000016672704,0.000018693683,0.00017017909,0.9042298,0.0135285035,0.08136516,0.00014358477,0.00019446928],"about_ca_topic_score_codex":0.000023847639,"about_ca_topic_score_gemma":0.0000063491325,"teacher_disagreement_score":0.945028,"about_ca_system_score_codex":0.000024155946,"about_ca_system_score_gemma":0.00010204373,"threshold_uncertainty_score":0.69485307},"labels":[],"label_agreement":null},{"id":"W4391230394","doi":"10.1007/s12065-023-00900-9","title":"Gaussian mixture models for training Bayesian convolutional neural networks","year":2024,"lang":"en","type":"article","venue":"Evolutionary Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Convolutional neural network; Machine learning; Artificial intelligence; Training (meteorology); Bayesian probability; Gaussian process; Gaussian; Mixture model; Training set; Pattern recognition (psychology)","score_opus":0.032539786954102354,"score_gpt":0.2700905110650882,"score_spread":0.23755072411098582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391230394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028001696,0.007382391,0.9828636,0.004703636,0.0018700984,0.0003303137,0.000034447767,0.0005623687,0.0022251164],"genre_scores_gemma":[0.90426713,0.00011866403,0.09375313,0.0005073074,0.0005350507,0.00013227419,0.000029542614,0.000025991187,0.00063090364],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974799,0.000050306247,0.00047330273,0.00089849,0.00038289154,0.0007151053],"domain_scores_gemma":[0.9987459,0.00031844387,0.00007573321,0.00045702767,0.000173436,0.00022942557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028282995,0.00032382007,0.00024216538,0.0002099299,0.00038707536,0.0004081344,0.0012414103,0.00025995393,0.000091352464],"category_scores_gemma":[0.00004443448,0.00029780527,0.00022072301,0.00085889146,0.0001713251,0.0016539475,0.00017831962,0.00054791605,0.00004108835],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010554609,0.000029698378,0.000015118283,0.00007227552,0.000029262492,0.000030847343,0.0007335199,0.08493823,0.000013951105,0.8319663,0.00381647,0.07834379],"study_design_scores_gemma":[0.00003477397,0.000081506434,0.00006704963,0.00011997,0.00000901602,0.00017052458,0.00006879783,0.7280142,0.000024827381,0.26744533,0.0036851477,0.0002788929],"about_ca_topic_score_codex":0.000013219402,"about_ca_topic_score_gemma":0.000007849867,"teacher_disagreement_score":0.9042391,"about_ca_system_score_codex":0.00013132361,"about_ca_system_score_gemma":0.00039328556,"threshold_uncertainty_score":0.9999474},"labels":[],"label_agreement":null},{"id":"W4391591814","doi":"10.1029/2024jh000155","title":"A Virtual Solar Wind Monitor at Mars With Uncertainty Quantification Using Gaussian Processes","year":2024,"lang":"en","type":"preprint","venue":"Journal of Geophysical Research Machine Learning and Computation","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"NASA Headquarters; Nuclear Safety and Security Commission; Irish Research Council; National Science Foundation; Science Foundation Ireland; National Aeronautics and Space Administration","keywords":"Mars Exploration Program; Solar wind; Astrobiology; Environmental science; Gaussian; Remote sensing; Aerospace engineering; Computer science; Meteorology; Physics; Geology; Engineering; Plasma","score_opus":0.04527142155836497,"score_gpt":0.36551120438022916,"score_spread":0.32023978282186416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391591814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6351646,0.0011371461,0.36149636,0.001631777,0.00021450609,0.00021454385,0.0000058589794,0.000057020334,0.00007815809],"genre_scores_gemma":[0.9883206,0.00021482565,0.0108229425,0.000012117106,0.00037308745,0.000004801637,0.000012525533,0.00003009053,0.00020904622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964227,0.00051898014,0.00055302354,0.0006258321,0.0014505888,0.0004288454],"domain_scores_gemma":[0.9970133,0.0005469518,0.0005834109,0.00020533937,0.0013917501,0.0002592911],"candidate_categories":["scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013134233,0.0003036113,0.0004726363,0.0005590549,0.0005024064,0.0012937373,0.00064186676,0.00017062364,0.0000035838968],"category_scores_gemma":[0.0003672452,0.00022056355,0.00010134956,0.0009352735,0.00020804511,0.0003514395,0.0012574625,0.0031130055,0.0000145794775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013722486,0.0007915649,0.007228484,0.007369352,0.0007847147,0.0008563053,0.00927061,0.46285796,0.0049261693,0.0124284215,0.00031953194,0.49179465],"study_design_scores_gemma":[0.00037961185,0.0014317045,0.0034926562,0.0019063598,0.00005866012,0.00021062419,0.00017342072,0.9558627,0.00024121765,0.035697233,0.00021775102,0.00032809106],"about_ca_topic_score_codex":0.00026375696,"about_ca_topic_score_gemma":0.000018489842,"teacher_disagreement_score":0.4930047,"about_ca_system_score_codex":0.00022359469,"about_ca_system_score_gemma":0.0011055273,"threshold_uncertainty_score":0.99974304},"labels":[],"label_agreement":null},{"id":"W4391729859","doi":"10.1007/978-3-031-17299-1_1828","title":"Mode Effects","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mode (computer interface); Materials science; Chemistry; Computer science; Human–computer interaction","score_opus":0.014712335608862561,"score_gpt":0.23346153077463974,"score_spread":0.21874919516577718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391729859","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.0888825e-7,0.000043728967,0.37351802,0.00031949356,0.00036265707,0.000081698534,0.0000017228646,0.00048797647,0.6251846],"genre_scores_gemma":[0.0006258794,0.00010266377,0.01398886,0.0004058058,0.00010669507,0.000008780563,0.0000041563935,0.000035210378,0.98472196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987938,0.0000040157583,0.0001754437,0.0005061968,0.00028441538,0.00023614877],"domain_scores_gemma":[0.9989834,0.00008873178,0.00008550637,0.00068536843,0.000054282194,0.0001027286],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000052043644,0.00025410275,0.00025012196,0.00013498034,0.000059028138,0.00018701811,0.0011177824,0.00021599542,0.000095873234],"category_scores_gemma":[0.000011132146,0.00020903497,0.00010480001,0.0000574039,0.000029190378,0.00017424753,0.0004191852,0.00023538503,0.0045279944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.0343094e-7,0.0000021842495,4.2263366e-7,0.000088978864,0.0000136827375,0.00006952694,0.00002034981,0.0000014648326,0.0000032989662,0.9595707,0.008629816,0.031599246],"study_design_scores_gemma":[0.00007771525,0.000055141976,0.000013842363,0.00020064446,0.000009259064,0.000012015447,2.7246068e-7,0.0040220567,0.00011126047,0.9422214,0.052896146,0.00038021515],"about_ca_topic_score_codex":0.000007753501,"about_ca_topic_score_gemma":0.0000105263525,"teacher_disagreement_score":0.35953736,"about_ca_system_score_codex":0.000021510683,"about_ca_system_score_gemma":0.00010853322,"threshold_uncertainty_score":0.9962471},"labels":[],"label_agreement":null},{"id":"W4391933919","doi":"10.1214/23-sts919","title":"Emerging Directions in Bayesian Computation","year":2024,"lang":"en","type":"article","venue":"Statistical Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Office of Naval Research; National Institutes of Health; European Commission","keywords":"Bayesian probability; Computer science; Computation; Approximate Bayesian computation; Artificial intelligence; Machine learning; Algorithm; Inference","score_opus":0.01065958484695598,"score_gpt":0.3056126727917594,"score_spread":0.2949530879448034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391933919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041715207,0.000110229965,0.9863355,0.0015175372,0.0005101766,0.00006366925,0.00000478432,0.00019329264,0.01084764],"genre_scores_gemma":[0.863127,0.000010705606,0.13671026,0.00006650494,0.000021981803,0.000008033231,8.8142815e-7,0.0000032218759,0.000051385843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851465,0.000023048005,0.00018925195,0.00053170574,0.00037936572,0.00036198224],"domain_scores_gemma":[0.9994719,0.00017744304,0.000016451888,0.00015031852,0.000048415444,0.00013549015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045371416,0.00008609422,0.00008387671,0.0002832553,0.00018607348,0.0007222198,0.0005352834,0.000020922407,0.000046141457],"category_scores_gemma":[0.00015829765,0.000073859184,0.000014701941,0.0027491094,0.00025805295,0.00094182114,0.00013662588,0.00014524093,0.00009567705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.2473991e-7,0.000012536224,0.00014204637,0.00001723453,6.0620243e-7,0.000041587133,0.00023790273,0.000055724544,0.0001818529,0.7305189,0.0001398812,0.2686514],"study_design_scores_gemma":[0.0000391218,0.00003036054,0.013181494,0.00005925193,0.0000013467284,0.000022065647,0.000021235044,0.7655576,0.000131458,0.2194714,0.001358356,0.00012634418],"about_ca_topic_score_codex":0.00006460132,"about_ca_topic_score_gemma":0.000035443532,"teacher_disagreement_score":0.8627099,"about_ca_system_score_codex":0.00008984296,"about_ca_system_score_gemma":0.0003241868,"threshold_uncertainty_score":0.69643825},"labels":[],"label_agreement":null},{"id":"W4392587473","doi":"10.5194/egusphere-egu24-2574","title":"Statistically impossible temperatures.","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.011083017187781386,"score_gpt":0.2660162426115242,"score_spread":0.2549332254237428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392587473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015836133,0.0010457622,0.90999323,0.004723111,0.0016884031,0.00018573055,0.000039314342,0.00079941715,0.08136667],"genre_scores_gemma":[0.5047013,0.000115176495,0.48397994,0.0009860967,0.00023174217,0.000061028964,0.000018069943,0.000029879617,0.009876789],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980493,0.000024187228,0.00031708405,0.00091841974,0.00034168904,0.00034928008],"domain_scores_gemma":[0.9986247,0.00004960543,0.00006399713,0.00097863,0.00011912028,0.00016396953],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00015056354,0.00030192971,0.0002700991,0.000145576,0.000063852276,0.0022855117,0.0018187901,0.00021196945,0.00019210773],"category_scores_gemma":[0.00003323678,0.00022588365,0.00010388643,0.000286885,0.000046845744,0.00010820429,0.0044441666,0.0010441422,0.00070881005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.791982e-7,0.000020172089,0.000011794012,0.00047018833,0.000026218391,0.00015979374,0.00011302028,0.000012019329,0.000057375157,0.95757294,0.031809196,0.009746415],"study_design_scores_gemma":[0.000049369253,0.000035765886,0.0001604489,0.00023808252,0.00001413953,0.00003690141,0.000004377548,0.031563524,0.00053512515,0.9635094,0.0034497194,0.00040316302],"about_ca_topic_score_codex":0.000042275027,"about_ca_topic_score_gemma":0.000011253544,"teacher_disagreement_score":0.50454295,"about_ca_system_score_codex":0.000044066917,"about_ca_system_score_gemma":0.00087051414,"threshold_uncertainty_score":0.9987502},"labels":[],"label_agreement":null},{"id":"W4392736312","doi":"10.1016/j.spl.2025.110555","title":"Optimal sub-Gaussian variance proxy for truncated Gaussian and exponential random variables","year":2025,"lang":"en","type":"preprint","venue":"Statistics & Probability Letters","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Institut Universitaire de France; Agence Nationale de la Recherche","keywords":"Gaussian; Proxy (statistics); Gaussian random field; Exponential function; Mathematics; Variance (accounting); Econometrics; Gaussian process; Exponential family; Statistics; Statistical physics; Applied mathematics; Mathematical analysis; Physics; Economics","score_opus":0.013235359263128958,"score_gpt":0.24846755058336306,"score_spread":0.2352321913202341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392736312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027523113,0.00015235986,0.9825613,0.0071106884,0.0013475814,0.003190581,0.002376218,0.000315931,0.00019303149],"genre_scores_gemma":[0.025496291,0.00006355201,0.9716667,0.0009542548,0.00020416865,0.0010296024,0.0004234331,0.00003817122,0.00012382814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9947318,0.0003000652,0.0011544907,0.0022735875,0.00054062414,0.0009994305],"domain_scores_gemma":[0.9964687,0.00062047807,0.0006187288,0.0016214838,0.00036809922,0.00030248566],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011010669,0.00084364874,0.0010705867,0.00024578307,0.00044469003,0.0012722006,0.001912804,0.00044194618,0.00002049492],"category_scores_gemma":[0.0005858222,0.0008190239,0.00018643055,0.00039577088,0.0004700243,0.00038517333,0.0015743293,0.00087680906,0.000005115997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005135408,0.00041450473,0.00081400253,0.007828871,0.00040041126,0.00005991436,0.0016051355,0.00086877996,0.0012240043,0.9600113,0.0078454465,0.018414106],"study_design_scores_gemma":[0.0031012613,0.000190341,0.0019755976,0.00072341494,0.00027178865,0.00002177728,0.0000058692704,0.12407633,0.0009752249,0.8651824,0.0019857085,0.0014903067],"about_ca_topic_score_codex":0.00015069828,"about_ca_topic_score_gemma":0.00003851722,"teacher_disagreement_score":0.123207554,"about_ca_system_score_codex":0.00023740993,"about_ca_system_score_gemma":0.0012359262,"threshold_uncertainty_score":0.99976456},"labels":[],"label_agreement":null},{"id":"W4393508323","doi":"10.5281/zenodo.6581961","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3","year":2022,"lang":"en","type":"dataset","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Artificial neural network; Computer science; Mutation; Deep neural networks; Artificial intelligence; Computational biology; Genetics; Biology; Gene","score_opus":0.020636044604407346,"score_gpt":0.24653558591531607,"score_spread":0.22589954131090872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393508323","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012616143,0.00063636,0.8063737,0.0006990493,0.0003328883,0.0016550806,0.18991825,0.00035574878,0.000016271088],"genre_scores_gemma":[0.003961834,0.00013476811,0.63463706,0.0025496401,0.00038636226,0.008925909,0.3492754,0.000106256266,0.00002274094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571395,0.00026057297,0.00095890084,0.0012797741,0.00053750083,0.0012492745],"domain_scores_gemma":[0.9958636,0.0013142306,0.0007425409,0.0016265217,0.00015874402,0.0002943634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008405967,0.000666939,0.00070956454,0.00069787283,0.00044489605,0.000711377,0.0027198165,0.0005423842,0.000095985444],"category_scores_gemma":[0.0012048979,0.00070941483,0.00020876988,0.0015802048,0.000093146846,0.0007109224,0.001025223,0.0017577258,0.0000021528056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007840756,0.00039849573,0.000086422784,0.000517301,0.00004628148,0.0003200547,0.00022788292,0.6340701,0.0000017275661,0.100415975,0.23207949,0.031757854],"study_design_scores_gemma":[0.00021370695,0.00025236007,0.00009695431,0.00015562426,0.00003514966,0.00011579763,0.000016303235,0.7609626,0.0000019808626,0.2291095,0.008473577,0.00056641694],"about_ca_topic_score_codex":0.0036930016,"about_ca_topic_score_gemma":0.003974083,"teacher_disagreement_score":0.22360592,"about_ca_system_score_codex":0.0004925008,"about_ca_system_score_gemma":0.00050566014,"threshold_uncertainty_score":0.9995357},"labels":[],"label_agreement":null},{"id":"W4393580462","doi":"10.5281/zenodo.6577004","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2","year":2022,"lang":"en","type":"dataset","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Mutation; Artificial neural network; Computer science; Artificial intelligence; Genetics; Biology; Gene","score_opus":0.020355228747401224,"score_gpt":0.24641367329208577,"score_spread":0.22605844454468454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393580462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012470081,0.00064286555,0.8070576,0.00069935946,0.00033205055,0.0016538654,0.1892289,0.00035636706,0.000016523634],"genre_scores_gemma":[0.0038615994,0.00013595427,0.6300025,0.0025476068,0.00038683988,0.008909303,0.35402787,0.00010596925,0.000022382957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957147,0.0002607183,0.0009589006,0.001279426,0.0005372855,0.0012489555],"domain_scores_gemma":[0.9958711,0.0013081254,0.0007433317,0.0016258964,0.00015719776,0.00029433996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000836894,0.00066683575,0.0007096531,0.0006976775,0.0004449587,0.000711438,0.002720003,0.00054242794,0.00009650388],"category_scores_gemma":[0.0011923809,0.0007093713,0.00020874618,0.0015796445,0.000093139824,0.0007108655,0.0010257544,0.0017576887,0.0000021120582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007693728,0.00039505734,0.00008316827,0.00050914683,0.000045910092,0.0003214405,0.00022604325,0.6302072,0.0000016564184,0.10153797,0.23476411,0.031831387],"study_design_scores_gemma":[0.0002125203,0.00025287413,0.00009615554,0.0001543224,0.00003534255,0.00011706988,0.000016235477,0.76008177,0.0000019340223,0.22985205,0.008614052,0.000565679],"about_ca_topic_score_codex":0.0036885943,"about_ca_topic_score_gemma":0.003970836,"teacher_disagreement_score":0.22615007,"about_ca_system_score_codex":0.0004908957,"about_ca_system_score_gemma":0.0005034317,"threshold_uncertainty_score":0.99953574},"labels":[],"label_agreement":null},{"id":"W4393617247","doi":"10.5281/zenodo.6581962","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Computer science; Mutation; Artificial neural network; Deep neural networks; Artificial intelligence; Machine learning; Computational biology; Genetics; Biology; Gene","score_opus":0.048757083136335734,"score_gpt":0.2552288401572365,"score_spread":0.2064717570209008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393617247","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006713183,0.000072792864,0.7681035,0.00024928089,0.0002238827,0.0009106892,0.22945304,0.00035321154,0.00062690716],"genre_scores_gemma":[0.002412739,0.00005218075,0.017841926,0.0002966262,0.00026403106,0.0000028228526,0.9783514,0.00075344555,0.000024803041],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99716043,0.00040260528,0.00047758126,0.0008969311,0.00045147885,0.0006109858],"domain_scores_gemma":[0.9980032,0.00029093793,0.0003285534,0.000833141,0.00038069548,0.00016347444],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074430805,0.00027639797,0.00028120686,0.0003614174,0.0017147228,0.0015947948,0.0030923788,0.0001391361,0.0038262208],"category_scores_gemma":[0.0020135865,0.0003024854,0.000067755536,0.0012662255,0.00010502413,0.000496756,0.0024876725,0.0009201281,0.00013256566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038416074,0.0001672816,1.8184599e-7,0.00036191716,0.000020887099,0.000061701154,0.0004383221,0.058641575,0.0000010955923,0.023960719,0.84954864,0.06675924],"study_design_scores_gemma":[0.0001928161,0.00027547436,0.0000074450777,0.00007471714,0.000011398649,0.00008471841,0.00003331008,0.45332822,3.1999477e-7,0.035389002,0.5103548,0.00024773757],"about_ca_topic_score_codex":0.000033569864,"about_ca_topic_score_gemma":0.000003261082,"teacher_disagreement_score":0.75026155,"about_ca_system_score_codex":0.00019736226,"about_ca_system_score_gemma":0.00001801262,"threshold_uncertainty_score":0.9999427},"labels":[],"label_agreement":null},{"id":"W4393689244","doi":"10.5281/zenodo.6577005","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Artificial neural network; Mutation; Computer science; Artificial intelligence; Deep neural networks; Machine learning; Genetics; Biology; Gene","score_opus":0.04808160967153502,"score_gpt":0.255099248751793,"score_spread":0.207017639080258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393689244","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006664992,0.00007378056,0.7679942,0.00025034303,0.00022420603,0.00091354764,0.22954352,0.00035515157,0.0006385618],"genre_scores_gemma":[0.0023426649,0.000052344676,0.017573992,0.00029487428,0.0002629776,0.0000028033462,0.9786984,0.0007476424,0.000024306893],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99716073,0.000402818,0.000477581,0.00089669967,0.00045130737,0.00061083765],"domain_scores_gemma":[0.99800795,0.00028965794,0.0003288878,0.00083284394,0.0003771841,0.00016346344],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074120023,0.0002763574,0.00028124012,0.0003613215,0.0017149515,0.0015949244,0.0030925798,0.00013914674,0.0038457303],"category_scores_gemma":[0.0019937726,0.0003024678,0.00006774824,0.0012657999,0.00010501663,0.0004967183,0.0024888923,0.0009201096,0.0001301868],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037683065,0.00016569374,1.751172e-7,0.00035603627,0.000020700778,0.000061872386,0.00043439196,0.058286145,0.00000105139,0.024198268,0.84964085,0.066797115],"study_design_scores_gemma":[0.0001915854,0.0002756955,0.0000073786155,0.00007404118,0.0000114450295,0.000085504,0.00003314153,0.4513614,3.1246705e-7,0.035508875,0.51220345,0.0002471532],"about_ca_topic_score_codex":0.000033531756,"about_ca_topic_score_gemma":0.0000032585467,"teacher_disagreement_score":0.7504202,"about_ca_system_score_codex":0.00019675244,"about_ca_system_score_gemma":0.000017937344,"threshold_uncertainty_score":0.9999427},"labels":[],"label_agreement":null},{"id":"W4393787782","doi":"10.5281/zenodo.6561382","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Computer science; Artificial neural network; Mutation; Deep neural networks; Artificial intelligence; Computational biology; Genetics; Biology; Gene","score_opus":0.04948459791673763,"score_gpt":0.2555494789308763,"score_spread":0.2060648810141387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393787782","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000066612415,0.00007236019,0.76614785,0.00024987137,0.00022174005,0.000909242,0.23141645,0.00035302428,0.0006228007],"genre_scores_gemma":[0.002377372,0.000051274023,0.017918382,0.00029517687,0.00026004933,0.0000028520644,0.9783213,0.0007493183,0.000024266139],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99715996,0.00040293174,0.00047740218,0.00089726003,0.0004514013,0.00061102514],"domain_scores_gemma":[0.99800277,0.00029312665,0.00032851318,0.00083420245,0.0003778955,0.00016348784],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074459193,0.00027643074,0.00028118267,0.000361519,0.0017148468,0.0015920456,0.0030940126,0.00013914605,0.0039070314],"category_scores_gemma":[0.002014041,0.0003025503,0.00006775918,0.0012666206,0.00010501784,0.0004964345,0.0024891838,0.0009202863,0.00013135732],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003773614,0.00016521777,1.7932518e-7,0.00035225498,0.000020635534,0.00006030361,0.0004327216,0.057878595,0.0000010793668,0.023778746,0.8521373,0.06513518],"study_design_scores_gemma":[0.00019123315,0.0002767216,0.000007508144,0.0000751012,0.000011524071,0.00008567563,0.000033555607,0.45453396,3.153115e-7,0.0354238,0.50911206,0.00024854313],"about_ca_topic_score_codex":0.00003325233,"about_ca_topic_score_gemma":0.000003242538,"teacher_disagreement_score":0.7482295,"about_ca_system_score_codex":0.00019670543,"about_ca_system_score_gemma":0.000018004239,"threshold_uncertainty_score":0.99994266},"labels":[],"label_agreement":null},{"id":"W4394055305","doi":"10.5281/zenodo.6561381","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1","year":2022,"lang":"en","type":"dataset","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Mutation; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Genetics; Biology","score_opus":0.02092767895809806,"score_gpt":0.2468258120546542,"score_spread":0.22589813309655615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394055305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012508274,0.0006321245,0.80473995,0.00070052093,0.00032939925,0.0016516597,0.19156225,0.00035541074,0.000016152277],"genre_scores_gemma":[0.00389067,0.00013196118,0.6333848,0.0025300288,0.00037924803,0.00899959,0.35055616,0.00010537296,0.000022165195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571365,0.00026079555,0.0009585203,0.0012802673,0.00053740264,0.0012493575],"domain_scores_gemma":[0.9958523,0.0013246472,0.0007424411,0.001628698,0.00015751121,0.00029438728],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008409349,0.0006670224,0.0007095002,0.0006980797,0.00044493002,0.00071008195,0.0027213334,0.0005424252,0.000098133925],"category_scores_gemma":[0.0012051851,0.0007095754,0.00020878173,0.0015807251,0.00009314096,0.0007104371,0.0010258813,0.0017580446,0.0000021321027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007739224,0.00039560112,0.00008565527,0.00050566846,0.000045959703,0.00031423068,0.0002261214,0.6289508,0.0000017104879,0.10017136,0.23811255,0.03111299],"study_design_scores_gemma":[0.0002112725,0.0002528672,0.00009755152,0.00015603728,0.000035459896,0.000116855714,0.00001638479,0.76135725,0.000001944912,0.22885065,0.008336908,0.0005667945],"about_ca_topic_score_codex":0.0036562863,"about_ca_topic_score_gemma":0.003950336,"teacher_disagreement_score":0.22977564,"about_ca_system_score_codex":0.00049077196,"about_ca_system_score_gemma":0.000505412,"threshold_uncertainty_score":0.99953556},"labels":[],"label_agreement":null},{"id":"W4394410253","doi":"10.6084/m9.figshare.20277778","title":"Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian; Statistical physics; Scalability; Mixture model; Computer science; Mathematics; Artificial intelligence; Physics","score_opus":0.03375250700435088,"score_gpt":0.2639648428540332,"score_spread":0.23021233584968231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394410253","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.7890376e-7,0.00009050134,0.0043107695,0.00081464497,0.00012626986,0.00060944556,0.99365306,0.00010837526,0.0002864795],"genre_scores_gemma":[0.00011436148,0.000005280655,0.0021659317,0.00032015634,0.00004167991,0.0016837223,0.9954751,0.00001607496,0.00017768146],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974965,0.00007125149,0.0004727226,0.0008673567,0.0004862929,0.00060588744],"domain_scores_gemma":[0.9980676,0.000053965778,0.00020503115,0.0012947555,0.00011334211,0.00026527056],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000768492,0.000370155,0.00037804677,0.0003807817,0.0002949736,0.0005849127,0.0026188297,0.00018777947,0.20309202],"category_scores_gemma":[0.00017787532,0.0003730566,0.00011249542,0.0013923023,0.000007221103,0.00066036277,0.0019861704,0.0005964892,0.0013984034],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002212346,0.00012024382,1.8930423e-7,0.00023269064,0.000007508046,0.000063056315,0.000049777824,0.0003917767,8.2903375e-7,0.0004092539,0.9973396,0.0013828742],"study_design_scores_gemma":[0.00018781469,0.00009718257,0.00017301548,0.0011610657,0.0000090000785,0.000050610302,0.000012466458,0.008580247,0.000012648049,0.0022332096,0.9868639,0.0006188533],"about_ca_topic_score_codex":0.00007555876,"about_ca_topic_score_gemma":0.00013053883,"teacher_disagreement_score":0.20169362,"about_ca_system_score_codex":0.0002465786,"about_ca_system_score_gemma":0.0004846213,"threshold_uncertainty_score":0.99987215},"labels":[],"label_agreement":null},{"id":"W4395097782","doi":"10.2139/ssrn.4772338","title":"Mechanism of Fracture Process Zones Statistically Defined Via Machine Learning Using the Bayesian Gaussian Mixture Model","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mechanism (biology); Gaussian process; Bayesian probability; Process (computing); Fracture (geology); Computer science; Artificial intelligence; Mixture model; Gaussian; Machine learning; Bayesian inference; Geology; Physics; Geotechnical engineering","score_opus":0.009687322358476826,"score_gpt":0.2584281830236715,"score_spread":0.24874086066519466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395097782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008734599,0.005479699,0.98846614,0.004126388,0.0003715664,0.00029591125,0.000020832093,0.00012562405,0.00024036884],"genre_scores_gemma":[0.9430724,0.0011663891,0.054954313,0.00019611433,0.00023460475,0.000014622446,0.000012178852,0.00007617625,0.0002732199],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948433,0.00021407811,0.00086588203,0.00085130456,0.0009361417,0.0022892677],"domain_scores_gemma":[0.9977358,0.00009543394,0.00091684266,0.00068704266,0.0003790885,0.00018576426],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0016438226,0.00066045416,0.00068331184,0.00031072745,0.0005083163,0.00070486945,0.0027905458,0.0004827891,0.00002211506],"category_scores_gemma":[0.00011094325,0.0004358242,0.00031005207,0.0005373825,0.00010741612,0.00026365343,0.0010841093,0.011664922,0.000006101171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031680105,0.00007235274,0.00002386013,0.00054327014,0.0003464106,0.00003875971,0.0014585342,0.049696367,0.00033137712,0.9369076,0.000016022414,0.010533753],"study_design_scores_gemma":[0.00010559228,0.000102604245,0.0000023872292,0.00024296365,0.000100823876,0.00053168903,0.00009656749,0.4712563,0.00016844134,0.527123,0.000009830483,0.0002598371],"about_ca_topic_score_codex":0.00010591875,"about_ca_topic_score_gemma":0.00024233552,"teacher_disagreement_score":0.94219893,"about_ca_system_score_codex":0.0004963539,"about_ca_system_score_gemma":0.0090040015,"threshold_uncertainty_score":0.9998093},"labels":[],"label_agreement":null},{"id":"W4396673092","doi":"10.1037/met0000645","title":"A deep learning method for comparing Bayesian hierarchical models.","year":2024,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"Deutsche Forschungsgemeinschaft; Google","keywords":"Computer science; Benchmark (surveying); Machine learning; Artificial intelligence; Inference; Bayesian inference; Model selection; Bayesian probability; Set (abstract data type); Hierarchical database model; Probabilistic logic; Code (set theory); Approximate Bayesian computation; Data mining","score_opus":0.08995888785594917,"score_gpt":0.442734058898519,"score_spread":0.35277517104256984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396673092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000049560796,0.0011455895,0.97935355,0.0020923982,0.0006835188,0.0002409468,6.587106e-7,0.00082413334,0.015609642],"genre_scores_gemma":[0.14214897,0.000038059305,0.8568806,0.00045330185,0.00014530428,0.00012498676,0.0000021266337,0.00001901711,0.000187648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694693,0.00072399963,0.00040898102,0.0011063906,0.00021485017,0.0005988649],"domain_scores_gemma":[0.99757653,0.0016065257,0.00006170766,0.00045318366,0.000063234205,0.00023879293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023771194,0.00026326158,0.0004170022,0.00017330643,0.00021976713,0.00062511524,0.0011743951,0.00020412874,0.00004895133],"category_scores_gemma":[0.0003233664,0.00019535558,0.00023484256,0.00077862706,0.000073627125,0.00044086494,0.0002685986,0.00073696725,0.000024662539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011023317,0.000040647115,0.00001281338,0.00005186434,0.00001559843,0.00001346707,0.00025083334,0.0007594669,0.00029219035,0.41903564,0.00008014984,0.5794363],"study_design_scores_gemma":[0.000114245704,0.00016400899,0.0001325649,0.000030994597,0.000008525096,0.000059445076,0.00000926978,0.58092827,0.00008881919,0.41285366,0.0054391944,0.00017101761],"about_ca_topic_score_codex":0.000003569832,"about_ca_topic_score_gemma":7.308252e-7,"teacher_disagreement_score":0.5801688,"about_ca_system_score_codex":0.000031826716,"about_ca_system_score_gemma":0.000022439659,"threshold_uncertainty_score":0.7966369},"labels":[],"label_agreement":null},{"id":"W4399589947","doi":"10.1098/rspa.2023.0608","title":"Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Air Force Office of Scientific Research","keywords":"Cluster (spacecraft); Bayesian probability; Computer science; Data mining; State (computer science); Estimation; Pattern recognition (psychology); Artificial intelligence; Algorithm; Engineering","score_opus":0.013134860391905603,"score_gpt":0.2402195325412119,"score_spread":0.22708467214930628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399589947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016978541,0.0000359372,0.9810981,0.0013812243,0.000024286575,0.0002921419,0.0000060905395,0.00008600976,0.00009765055],"genre_scores_gemma":[0.60451984,7.658886e-7,0.39536524,0.00003704767,0.000023415321,0.000038328784,4.8901364e-7,0.0000042561173,0.0000106203015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901086,0.000001561479,0.0001492628,0.00041502705,0.00023084533,0.00019246923],"domain_scores_gemma":[0.99958974,0.00013283659,0.00003675035,0.00011854658,0.000039066475,0.00008305372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037986532,0.0001225846,0.00015541266,0.000023206418,0.00014036299,0.00038779763,0.0006791168,0.000028641478,1.8656637e-7],"category_scores_gemma":[0.000085060135,0.000076813485,0.000055184788,0.0004212711,0.0001346745,0.00039544908,0.00032240548,0.00008170367,6.9124627e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010194218,0.00031258407,0.0000539709,0.011049756,0.000062272964,1.0463216e-7,0.0066011907,0.15111205,0.0046378984,0.7777757,0.0012792391,0.04710505],"study_design_scores_gemma":[0.00005154792,0.00004833591,0.00006928589,0.00013082128,0.000013586181,0.0000013017302,0.000025538051,0.95213324,0.00062812865,0.046765514,0.000030594587,0.00010212767],"about_ca_topic_score_codex":0.0000019753463,"about_ca_topic_score_gemma":3.249353e-8,"teacher_disagreement_score":0.80102116,"about_ca_system_score_codex":0.000010246475,"about_ca_system_score_gemma":0.000025127121,"threshold_uncertainty_score":0.37395415},"labels":[],"label_agreement":null},{"id":"W4399799131","doi":"10.1007/s41060-024-00580-3","title":"Implicitly adaptive optimal proposal in variational inference for Bayesian learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Inference; Bayesian inference; Bayesian probability; Computer science; Machine learning; Artificial intelligence","score_opus":0.037284725672156835,"score_gpt":0.34915518342993906,"score_spread":0.3118704577577822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399799131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016701164,0.00014039486,0.99336433,0.004119809,0.00039336487,0.000053601114,0.000030362857,0.000012419642,0.00021561784],"genre_scores_gemma":[0.83362436,0.000099900346,0.16599293,0.000091764006,0.00016127544,0.0000011606357,0.000005015157,0.000003321618,0.000020287993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826664,0.000016303127,0.00038125424,0.00033922007,0.00080077344,0.00019581619],"domain_scores_gemma":[0.9984753,0.00022614036,0.00016413373,0.00016830544,0.0008701469,0.00009595374],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015536281,0.00009391477,0.00013000681,0.0005476767,0.00008729446,0.0010655448,0.002512532,0.00003079258,0.000006512694],"category_scores_gemma":[0.0005762473,0.00007446546,0.000029334456,0.00068549253,0.00015819051,0.004948841,0.00052896753,0.00023352203,0.0000019967863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031489482,0.00007391284,0.0019086945,0.000027442018,0.00006701115,0.0001301362,0.00070658984,0.0023518365,0.0008142507,0.8463666,0.00030804085,0.14721404],"study_design_scores_gemma":[0.00019422644,0.00016182692,0.0016847108,0.00015280058,0.000009394729,0.00015787312,0.00009244921,0.9732369,0.00010700789,0.022460502,0.0016392197,0.00010309729],"about_ca_topic_score_codex":0.0000102203585,"about_ca_topic_score_gemma":0.00000845814,"teacher_disagreement_score":0.97088504,"about_ca_system_score_codex":0.00009033743,"about_ca_system_score_gemma":0.0019174918,"threshold_uncertainty_score":0.99997145},"labels":[],"label_agreement":null},{"id":"W4402589817","doi":"10.1088/2632-2153/ad7cc1","title":"Benchmarking of quantum fidelity kernels for Gaussian process regression","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmarking; Fidelity; Gaussian process; Kriging; Process (computing); Regression; Gaussian; Computer science; Statistics; Artificial intelligence; Mathematics; Machine learning; Physics; Business; Quantum mechanics; Programming language; Telecommunications","score_opus":0.009658262107674597,"score_gpt":0.2903121539643625,"score_spread":0.2806538918566879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402589817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13379158,0.002055517,0.8535436,0.008572927,0.0003599683,0.0002623534,0.0000035345915,0.00067641964,0.000734119],"genre_scores_gemma":[0.98773646,0.000053065378,0.012062604,0.00003571424,0.000023456028,0.000022470342,0.0000010742223,0.0000068803433,0.000058256825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983818,0.000015351556,0.00025039804,0.0006618026,0.0003152194,0.00037545766],"domain_scores_gemma":[0.99919397,0.000082004444,0.000113150665,0.00029182635,0.00025510252,0.000063934756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009876388,0.0001427483,0.00019972652,0.00063685805,0.000393393,0.00023680355,0.0009839672,0.00010490992,0.000006212485],"category_scores_gemma":[0.00047314775,0.00010395096,0.000028744651,0.0025549564,0.00060063903,0.00060642994,0.00031888948,0.00032822473,0.0000025624881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042827605,0.000022343844,0.0058424775,0.00036362337,0.0000059824874,0.000010337683,0.00048002173,0.000034901113,0.0075386004,0.58848464,0.00003642937,0.39717633],"study_design_scores_gemma":[0.00012312597,0.000363956,0.0010550104,0.00033449408,0.0000067315405,0.000068775145,0.000086442364,0.8522093,0.010857466,0.1323548,0.0023550722,0.00018476408],"about_ca_topic_score_codex":0.000018644354,"about_ca_topic_score_gemma":0.000002871847,"teacher_disagreement_score":0.8539449,"about_ca_system_score_codex":0.00002465149,"about_ca_system_score_gemma":0.0003286442,"threshold_uncertainty_score":0.4238997},"labels":[],"label_agreement":null},{"id":"W4403605665","doi":"10.1088/1742-5468/ad642a","title":"Learning curves for deep structured Gaussian feature models*","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Feature (linguistics); Gaussian; Statistical physics; Artificial intelligence; Mathematics; Gaussian process; Computer science; Physics; Quantum mechanics","score_opus":0.010938888038048936,"score_gpt":0.27672626044470294,"score_spread":0.265787372406654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403605665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009520452,0.009963177,0.9883295,0.0009142077,0.00044627782,0.00009574968,0.000008453021,0.000027945738,0.000119443],"genre_scores_gemma":[0.7744696,0.00075217645,0.22433364,0.00024220855,0.00010526962,0.000008540937,0.0000019246327,0.000012479667,0.000074124546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900186,0.00009738313,0.00026459497,0.00020802491,0.00022434199,0.00020381073],"domain_scores_gemma":[0.9991629,0.0003834437,0.00009830446,0.00010230755,0.0000905552,0.00016248555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068599224,0.00013821217,0.00021987624,0.000073839874,0.000114613686,0.00025611703,0.00028237354,0.00006208535,0.00003074603],"category_scores_gemma":[0.00012046636,0.00009658791,0.00006104739,0.00010313288,0.000024130057,0.00039231603,0.00007587529,0.00028804288,0.0000010965038],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057837548,0.000018080444,1.08679004e-7,0.00023312868,0.000039587725,0.000044780765,0.00088551384,0.00005223467,0.00090829347,0.9534423,0.00039184213,0.043926295],"study_design_scores_gemma":[0.00015891899,0.000564582,0.0000014619022,0.00032246253,0.000026511867,0.00016776244,0.00022017362,0.16394295,0.002775688,0.8308604,0.0008453392,0.000113776034],"about_ca_topic_score_codex":2.5582875e-7,"about_ca_topic_score_gemma":1.6432176e-7,"teacher_disagreement_score":0.7743744,"about_ca_system_score_codex":0.000027864853,"about_ca_system_score_gemma":0.00008181868,"threshold_uncertainty_score":0.39387405},"labels":[],"label_agreement":null},{"id":"W4403674456","doi":"10.1109/tsp.2024.3484908","title":"Towards Inversion-Free Sparse Bayesian Learning: A Universal Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Queen's University; Memorial University of Newfoundland","funders":"Simon Fraser University; Natural Sciences and Engineering Research Council of Canada; Innovation for Defence Excellence and Security","keywords":"Bayesian probability; Computer science; Inversion (geology); Artificial intelligence; Pattern recognition (psychology); Algorithm; Machine learning; Geology","score_opus":0.019510845679390557,"score_gpt":0.23631520874135156,"score_spread":0.216804363061961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403674456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024623802,0.00026690398,0.9827267,0.0010802835,0.00027215452,0.00012871418,0.0000046576056,0.00086681644,0.014407525],"genre_scores_gemma":[0.9542705,0.000027091752,0.044265356,0.00016590292,0.00007346729,0.000018175571,0.0000014392666,0.00002934055,0.0011487188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791914,0.00006728161,0.00027125163,0.00076426007,0.00053132034,0.00044676743],"domain_scores_gemma":[0.9991913,0.000058930345,0.0000647228,0.0003683603,0.00010194572,0.00021477869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023082248,0.0002957314,0.00021869886,0.00042343888,0.00055254536,0.00096053805,0.0010784378,0.00014879172,0.00012940918],"category_scores_gemma":[0.0000045313714,0.00026796205,0.00015000363,0.0014453204,0.0001134793,0.0015715741,0.000013531259,0.00078111136,0.00008453996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038207614,0.00023920664,0.000004078311,0.0005185941,0.000060532846,0.00014841964,0.002863411,0.01660139,0.00089329685,0.00662736,0.0008057225,0.9711998],"study_design_scores_gemma":[0.0003310834,0.0002102261,0.000006993742,0.00035123207,0.000042973246,0.00009941711,0.000309473,0.9829563,0.0065379445,0.006055287,0.0026630035,0.00043606243],"about_ca_topic_score_codex":0.000021901673,"about_ca_topic_score_gemma":0.0000029652153,"teacher_disagreement_score":0.97076374,"about_ca_system_score_codex":0.00012701526,"about_ca_system_score_gemma":0.00062350143,"threshold_uncertainty_score":0.99997723},"labels":[],"label_agreement":null},{"id":"W4404456042","doi":"10.5705/ss.202024.0213","title":"Inference for Delay Differential Equations Using Manifold-Constrained Gaussian Processes","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Manifold (fluid mechanics); Applied mathematics; Gaussian; Mathematics; Differential equation; Computer science; Mathematical optimization; Mathematical analysis; Artificial intelligence; Physics","score_opus":0.03967192229583435,"score_gpt":0.3327578115398602,"score_spread":0.2930858892440259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404456042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002249457,0.00024892687,0.9957384,0.0007908009,0.0005567669,0.00035813075,0.00031268434,0.0003584085,0.0014109721],"genre_scores_gemma":[0.75526106,0.00001999049,0.24422832,0.00007880582,0.000115573566,0.00006333588,0.000029914112,0.000020114529,0.00018286695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979446,0.00003957496,0.0004914381,0.000713738,0.00029788955,0.0005127618],"domain_scores_gemma":[0.9975721,0.0014640623,0.00010546936,0.0004324538,0.00024236651,0.00018351221],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00015086109,0.0002652404,0.00026702625,0.00016903119,0.00029784732,0.001103929,0.00078221946,0.00009535927,0.00017446469],"category_scores_gemma":[0.0008388044,0.00023047293,0.00007777896,0.0006770668,0.00012500401,0.00063364074,0.00016048501,0.00017915816,0.000057217258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000079121055,0.00006179018,0.00001562442,0.000590853,0.00004515985,0.00004005968,0.00037680048,0.000022789782,0.00040754874,0.97291815,0.00047161835,0.025041679],"study_design_scores_gemma":[0.00041624514,0.00028543806,0.00026140775,0.00048515928,0.00011050347,0.00006277792,0.000048169553,0.76679283,0.00045602737,0.22683975,0.0036159405,0.0006257318],"about_ca_topic_score_codex":0.000019552954,"about_ca_topic_score_gemma":0.0000325494,"teacher_disagreement_score":0.76677006,"about_ca_system_score_codex":0.00005005552,"about_ca_system_score_gemma":0.0013312005,"threshold_uncertainty_score":0.999933},"labels":[],"label_agreement":null},{"id":"W4404848767","doi":"10.1109/dsc63325.2024.00013","title":"Chronohunt: Determining Optimal Pace for Automated Alert Analysis in Threat Hunting Using Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Research Canada; Ericsson (Canada)","funders":"","keywords":"Pace; Reinforcement learning; Computer science; Reinforcement; Computer security; Artificial intelligence; Engineering; Geography","score_opus":0.022345100933669204,"score_gpt":0.30064052235237826,"score_spread":0.27829542141870905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404848767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047524866,0.00014647978,0.9497491,0.00013186358,0.00010360057,0.0001508442,3.4564437e-7,0.0007637766,0.0014291387],"genre_scores_gemma":[0.81418395,0.000006198457,0.18542261,0.00005562939,0.000032266777,0.000018426495,0.0000034578218,0.0000114091235,0.0002660644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839,0.000024062707,0.00038522092,0.0005453448,0.00019236549,0.00046300565],"domain_scores_gemma":[0.9994372,0.00012039755,0.00008127227,0.000245618,0.00005303179,0.000062446394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037946584,0.00018718047,0.00026615267,0.00053009286,0.00016931532,0.000782182,0.00042213834,0.00006984749,0.000040107716],"category_scores_gemma":[0.00003839199,0.00016511751,0.00013947082,0.0018170476,0.000021308086,0.00087416975,0.00021859222,0.00015442543,0.000011276426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066466414,0.000021685599,0.012141433,0.0003052911,0.00027722216,0.00006765074,0.0024831982,0.91934085,0.001524914,0.015466942,0.000040552313,0.048323624],"study_design_scores_gemma":[0.00014213884,0.000062137435,0.0011172441,0.0001520641,0.00006637091,0.000009833199,0.00010870225,0.9967388,0.0010698508,0.000094162555,0.00020903938,0.0002296525],"about_ca_topic_score_codex":0.00012705935,"about_ca_topic_score_gemma":0.000045754387,"teacher_disagreement_score":0.7666591,"about_ca_system_score_codex":0.00011550258,"about_ca_system_score_gemma":0.00014882961,"threshold_uncertainty_score":0.7542599},"labels":[],"label_agreement":null},{"id":"W4405467872","doi":"10.3934/fods.2024053","title":"Deep learning with Gaussian continuation","year":2024,"lang":"en","type":"article","venue":"Foundations of Data Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Continuation; Artificial intelligence; Gaussian; Deep learning; Computer science; Psychology; Physics","score_opus":0.03058443628319491,"score_gpt":0.3090793699756104,"score_spread":0.2784949336924155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405467872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012921683,0.00011437556,0.9902937,0.0010871931,0.00015289127,0.000079153644,0.0000060659636,0.00013720806,0.006837235],"genre_scores_gemma":[0.82936496,0.000019672696,0.17041862,0.000018473038,0.00002175423,0.0000043463406,0.000030719304,0.0000036730767,0.0001177611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985371,0.00001732125,0.00018470782,0.000575302,0.00047452352,0.00021102632],"domain_scores_gemma":[0.9986544,0.0000787451,0.000076758864,0.0009413451,0.00017924716,0.00006947423],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00064812537,0.00008324618,0.00008452665,0.0002606801,0.00031397466,0.0010370926,0.0026830866,0.00001795908,0.00003942229],"category_scores_gemma":[0.00015568534,0.000063659005,0.000011418913,0.0023788665,0.0004113598,0.008400902,0.00054131553,0.00011842275,0.000082378545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015759999,0.00002768808,0.00048887474,0.000050422514,0.000008069388,0.0000058406545,0.0006223783,0.00018561397,0.0014688111,0.73685926,0.00010717414,0.2601743],"study_design_scores_gemma":[0.00011234332,0.00015137318,0.00619061,0.00018969148,0.000014012994,0.00004854551,0.00013604634,0.9701135,0.001167258,0.0073914877,0.014261913,0.00022322533],"about_ca_topic_score_codex":0.00002604396,"about_ca_topic_score_gemma":0.00003361671,"teacher_disagreement_score":0.9699279,"about_ca_system_score_codex":0.000026661151,"about_ca_system_score_gemma":0.00060197327,"threshold_uncertainty_score":0.99999994},"labels":[],"label_agreement":null},{"id":"W4406195386","doi":"10.1016/j.trpro.2024.12.016","title":"Robustness Analysis of Deep Learning Models for Population Synthesis","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robustness (evolution); Computer science; Population; Artificial intelligence; Machine learning; Medicine; Environmental health","score_opus":0.053768810876948125,"score_gpt":0.3499746490793387,"score_spread":0.29620583820239055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406195386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032600213,0.00007946403,0.9662733,0.0002848813,0.00002615214,0.00030758177,0.000009137866,0.000094482515,0.0003247587],"genre_scores_gemma":[0.96131563,0.000035769684,0.038223166,0.000010938306,0.000007633535,0.00023912638,0.000041298183,0.000007146748,0.00011930508],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985132,0.000052451338,0.00035458803,0.0003609675,0.00043706945,0.0002816982],"domain_scores_gemma":[0.9984214,0.00039298067,0.00009953927,0.00022027377,0.0008104037,0.00005543651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073653576,0.0000913608,0.0002428619,0.0009876495,0.00018892826,0.000076984645,0.0005024596,0.000077815195,0.000011293032],"category_scores_gemma":[0.00018585888,0.000087723165,0.00011236023,0.0037481114,0.000043426873,0.0005402363,0.000011168002,0.00015376782,5.7345153e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032457,0.000070890754,0.017227562,0.00058845076,0.0002217752,0.0000010780069,0.0007422795,0.4611425,0.00012345056,0.46823302,0.00002617665,0.05159037],"study_design_scores_gemma":[0.000112881964,0.000028749248,0.13051711,0.000046968347,0.000105788065,3.0583102e-8,0.00009802462,0.85749006,0.0007599347,0.010746109,0.000017208216,0.00007710732],"about_ca_topic_score_codex":0.000121085584,"about_ca_topic_score_gemma":0.0004240062,"teacher_disagreement_score":0.9287154,"about_ca_system_score_codex":0.000027168357,"about_ca_system_score_gemma":0.00012031981,"threshold_uncertainty_score":0.3577247},"labels":[],"label_agreement":null},{"id":"W4406563120","doi":"10.1007/s11222-025-10565-2","title":"Efficient modeling of quasi-periodic data with seasonal Gaussian process","year":2025,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"","keywords":"Gaussian process; Process (computing); Computer science; Mathematics; Applied mathematics; Gaussian; Econometrics; Algorithm; Physics","score_opus":0.01575318638010231,"score_gpt":0.27920365753802495,"score_spread":0.26345047115792264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406563120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018179657,0.00019787083,0.9803838,0.00017542503,0.00006627987,0.00007973105,0.000047755886,0.00003621072,0.0008332757],"genre_scores_gemma":[0.79452413,0.0000044887006,0.20538239,0.000052833857,0.000011753031,9.0690855e-7,0.0000093074805,0.000003909708,0.000010293877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998864,0.00001997159,0.00024582326,0.00042817058,0.0002173866,0.00022463908],"domain_scores_gemma":[0.9991724,0.00007545527,0.00010799783,0.00043389123,0.00014858271,0.00006165658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019969928,0.0001269921,0.00019178921,0.00007215451,0.00019616674,0.00017998833,0.00073119433,0.00002742442,0.0000019865956],"category_scores_gemma":[0.000038378643,0.00010220013,0.000008368184,0.0003682933,0.00007357173,0.00006862972,0.0004434065,0.000111279885,6.304325e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020280915,0.00016146811,0.0031677282,0.00088659377,0.000050794675,0.000023958766,0.0013199114,0.055876873,0.000014667159,0.78874826,0.000089014735,0.14964043],"study_design_scores_gemma":[0.00021664587,0.00006409637,0.0005685552,0.00027089662,0.00001296021,0.000009552991,0.00012888368,0.9901139,0.000012678865,0.008466617,0.000013793406,0.00012138697],"about_ca_topic_score_codex":0.00002431395,"about_ca_topic_score_gemma":0.000007897201,"teacher_disagreement_score":0.93423706,"about_ca_system_score_codex":0.000008366884,"about_ca_system_score_gemma":0.0003039454,"threshold_uncertainty_score":0.41676003},"labels":[],"label_agreement":null},{"id":"W4407590981","doi":"10.3390/s25041183","title":"Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Bootstrapping (finance); Parametric statistics; Interpretation (philosophy); Computer science; Data mining; Statistics; Econometrics; Mathematics","score_opus":0.021916503469353362,"score_gpt":0.27647297161840795,"score_spread":0.2545564681490546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407590981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018695032,0.00018343543,0.9736875,0.0010876757,0.00041251112,0.00012316178,0.0000057177845,0.00021426038,0.005590647],"genre_scores_gemma":[0.90577495,0.000029219747,0.09343208,0.0003777966,0.000027925156,0.0000031408833,0.000011025181,0.000006379108,0.00033748287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985701,0.000054877404,0.00028268923,0.00060517277,0.00018642846,0.00030074557],"domain_scores_gemma":[0.9984771,0.00014962678,0.00010342152,0.0011259767,0.00007887855,0.00006498993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021833272,0.00015407242,0.00017582279,0.00033523468,0.0001353594,0.00032807974,0.0013967045,0.00004428164,0.00000943099],"category_scores_gemma":[0.00017225274,0.00014741695,0.000041195868,0.0010601515,0.00004188386,0.0005839205,0.00050618016,0.00017582973,0.00005785694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004966279,0.00021470673,0.0013673735,0.00049586414,0.00024761664,0.0002641875,0.0016873373,0.023146398,0.0022188511,0.048589475,0.004126239,0.9175923],"study_design_scores_gemma":[0.00015063236,0.000020625128,0.00063518994,0.00010109956,0.0000132644645,0.0000274777,0.00005937325,0.9906762,0.0023902312,0.004115569,0.0016220262,0.00018828078],"about_ca_topic_score_codex":0.000026423473,"about_ca_topic_score_gemma":0.0000029296248,"teacher_disagreement_score":0.96752983,"about_ca_system_score_codex":0.000034313198,"about_ca_system_score_gemma":0.0000907597,"threshold_uncertainty_score":0.6011489},"labels":[],"label_agreement":null},{"id":"W4408235748","doi":"10.1109/tro.2025.3548865","title":"Informative Path Planning for Active Regression With Gaussian Processes via Sparse Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Computer science; Artificial intelligence; Gaussian process; Regression; Mathematical optimization; Kriging; Path (computing); Gaussian; Machine learning; Mathematics; Robot; Statistics","score_opus":0.01572525728015791,"score_gpt":0.26112170544617563,"score_spread":0.24539644816601772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408235748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005266206,0.000019500512,0.9974256,0.0008106644,0.00027021318,0.00041916466,0.000013318879,0.00017229622,0.00081656215],"genre_scores_gemma":[0.54216033,0.000029710189,0.45705867,0.00029521366,0.000015160475,0.000109073924,0.0000061860037,0.00001309927,0.00031252936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989297,0.000020877176,0.0002488412,0.00032151683,0.00020106301,0.00027802915],"domain_scores_gemma":[0.9990293,0.00014294517,0.00015675453,0.00030865916,0.00028616152,0.00007622998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006369989,0.0002225445,0.00019942604,0.00024206012,0.00043458585,0.00018227068,0.0003967459,0.00010065332,0.0000051633983],"category_scores_gemma":[0.000010309692,0.00016748035,0.000044857316,0.0009293072,0.000055269094,0.00096946885,0.0000038464887,0.00020880839,0.000003119514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102080165,0.00011474444,0.0000075474045,0.00019932973,0.000037621805,0.0000029901737,0.0010462032,0.98177487,0.000012355048,0.0016511042,0.000081985105,0.014969158],"study_design_scores_gemma":[0.00075263286,0.00040146912,0.00004098045,0.00084622804,0.00005053444,0.000012422004,0.00028984703,0.98012376,0.015637266,0.0014995871,0.00005610584,0.00028915273],"about_ca_topic_score_codex":0.000003794334,"about_ca_topic_score_gemma":0.0000062635672,"teacher_disagreement_score":0.5421077,"about_ca_system_score_codex":0.000069257076,"about_ca_system_score_gemma":0.00036508142,"threshold_uncertainty_score":0.68296504},"labels":[],"label_agreement":null},{"id":"W4409141507","doi":"10.21105/joss.07069","title":"Dynamax: A Python package for probabilistic state space modeling with JAX","year":2025,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Python (programming language); R package; Computer science; Programming language","score_opus":0.01644812374642047,"score_gpt":0.2735963559167433,"score_spread":0.25714823217032284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409141507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019689944,0.00019203432,0.97627634,0.003003886,0.00009414102,0.00046599572,0.0000042265847,0.000039358816,0.00023410615],"genre_scores_gemma":[0.79933995,0.00002746214,0.19870006,0.0004023645,0.000036858935,0.000016109732,6.2855554e-7,0.000021918546,0.0014546724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875504,0.00008829545,0.00038245306,0.00019819697,0.00027729128,0.00029874273],"domain_scores_gemma":[0.9982093,0.00029909846,0.00037373407,0.0005241178,0.0005026765,0.00009105199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009953228,0.00017155652,0.0003028077,0.00009705102,0.0003084121,0.00071338564,0.0029067905,0.000036904585,0.000005423295],"category_scores_gemma":[0.00025541338,0.00009647776,0.00007193647,0.00047642278,0.000061435785,0.00074371416,0.00051537977,0.00027275304,0.0000031127342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003553885,0.0009455423,0.0055180104,0.001952194,0.0010181506,0.00015948233,0.035237122,0.45784765,0.0005320954,0.093023315,0.010362493,0.38985008],"study_design_scores_gemma":[0.00561903,0.002514442,0.0015813549,0.0035674355,0.00040425526,0.0008025058,0.0020407971,0.546419,0.0009123405,0.4276446,0.0073737386,0.0011204841],"about_ca_topic_score_codex":0.000029140718,"about_ca_topic_score_gemma":0.000025239886,"teacher_disagreement_score":0.77965,"about_ca_system_score_codex":0.000061183666,"about_ca_system_score_gemma":0.0005283636,"threshold_uncertainty_score":0.68791944},"labels":[],"label_agreement":null},{"id":"W4410556665","doi":"10.1088/1475-7516/2025/05/053","title":"Deep inference of simulated strong lenses in ground-based surveys","year":2025,"lang":"en","type":"article","venue":"Journal of Cosmology and Astroparticle Physics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Physics; Inference; Statistical physics; Theoretical physics; Data science; Artificial intelligence; Computer science","score_opus":0.015308776461822472,"score_gpt":0.2786597069098351,"score_spread":0.2633509304480126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410556665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49019325,0.00008053997,0.5094103,0.00023086005,0.00004609989,0.000018668694,2.8380472e-7,0.0000032843313,0.0000167188],"genre_scores_gemma":[0.9937947,0.000010365054,0.0061057536,0.0000705583,0.000011911724,4.5798308e-7,1.9578097e-7,0.000002040587,0.0000040429813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990826,0.00014685589,0.00037922594,0.00011226549,0.00010373175,0.0001753244],"domain_scores_gemma":[0.99909556,0.00028277442,0.00024886944,0.00013763258,0.00019137873,0.000043783333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039316597,0.000085768006,0.00025289634,0.000087380955,0.000035764555,0.00003215224,0.00029415512,0.000038604212,0.0000021256067],"category_scores_gemma":[0.000081948,0.00007338676,0.000039615108,0.0004043661,0.00011671402,0.0003145312,0.00006628178,0.00017622353,5.838257e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000134033,0.0006657447,0.56891257,0.00011360892,0.000072025636,0.0000674797,0.00032494648,0.04435057,0.0080146855,0.22525488,0.000018622139,0.15207084],"study_design_scores_gemma":[0.0022291392,0.00088848814,0.5344218,0.00021230469,0.000029552799,0.000016495684,0.0000640663,0.2370681,0.036233865,0.1886521,0.000012187493,0.00017188248],"about_ca_topic_score_codex":0.0000139177155,"about_ca_topic_score_gemma":0.000007984031,"teacher_disagreement_score":0.50360143,"about_ca_system_score_codex":0.000013770011,"about_ca_system_score_gemma":0.00019083772,"threshold_uncertainty_score":0.29926252},"labels":[],"label_agreement":null},{"id":"W4411225508","doi":"10.1017/9781009504942.014","title":"Choosing the kernel matrix as the covariance matrix for a Gaussian process","year":2025,"lang":"en","type":"other","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo; McMaster University","funders":"","keywords":"Covariance matrix; Gaussian process; Mathematics; Estimation of covariance matrices; Matrix (chemical analysis); Kernel (algebra); Gaussian; Applied mathematics; Algorithm; Combinatorics; Materials science; Chemistry; Computational chemistry","score_opus":0.009922065007682255,"score_gpt":0.30261977577658156,"score_spread":0.2926977107688993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411225508","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0457215e-7,0.0012838267,0.62606436,0.012435803,0.00050264306,0.00092391827,0.000036890608,0.00034516244,0.3584072],"genre_scores_gemma":[0.0016586883,0.0001639229,0.038679615,0.0023320757,0.00047531657,0.00035578688,0.0000063048446,0.00015316067,0.95617515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977189,0.00005990642,0.00036958902,0.0008346319,0.00042651547,0.0005904861],"domain_scores_gemma":[0.99771917,0.0002179871,0.00039190892,0.0014539189,0.0001313734,0.000085660235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003721606,0.00046848884,0.000393714,0.00015387798,0.00042850027,0.0008242865,0.004321582,0.00028941478,0.00064148725],"category_scores_gemma":[0.00010008414,0.00023552601,0.00018573458,0.00071616116,0.0001406148,0.00019134166,0.00037082794,0.00039209853,0.00030956874],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007550319,0.000030558313,0.0000071987492,0.00044091197,0.000069704955,0.000005237258,0.00022289612,0.000012257405,0.0000024004946,0.67033654,0.32045567,0.008409038],"study_design_scores_gemma":[0.0004235608,0.00006157074,0.00003166409,0.00067416776,0.00007235044,0.00004606519,0.00011589209,0.009496905,0.00010292664,0.10464888,0.88373977,0.00058624713],"about_ca_topic_score_codex":0.00028168323,"about_ca_topic_score_gemma":0.00009060844,"teacher_disagreement_score":0.59776795,"about_ca_system_score_codex":0.000038465354,"about_ca_system_score_gemma":0.0010067704,"threshold_uncertainty_score":0.9604472},"labels":[],"label_agreement":null},{"id":"W4411225574","doi":"10.1017/9781009504942.006","title":"Gaussian processes and other kernel methods","year":2025,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo; McMaster University","funders":"","keywords":"Kernel (algebra); Gaussian function; Statistical physics; Gaussian; Mathematics; Computer science; Applied mathematics; Physics; Pure mathematics; Quantum mechanics","score_opus":0.02360019763271685,"score_gpt":0.2465786348678696,"score_spread":0.22297843723515276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411225574","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.6366816e-7,0.00047307322,0.3094416,0.0000732072,0.0001227839,0.00019402623,0.00006520149,0.0001797968,0.6894494],"genre_scores_gemma":[0.0001449711,0.00015368451,0.033359025,0.00036580532,0.00005405063,0.0000013408902,0.000004282904,0.000025181924,0.96589166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982674,0.000052390475,0.00021788932,0.00090366957,0.00022022445,0.0003384218],"domain_scores_gemma":[0.99841434,0.00013996281,0.0002471634,0.0007888821,0.00020618178,0.00020345667],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014865847,0.00045635516,0.0004680339,0.0002673136,0.000240848,0.0002640478,0.00144872,0.00051706354,0.0000039996307],"category_scores_gemma":[0.000027541544,0.000475509,0.00010608034,0.000038677634,0.00024307478,0.00024124846,0.0010933115,0.0005872879,0.000006517845],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011818631,0.0000068813124,0.0000026872697,0.0005432248,0.000076082,0.00008588367,0.00007342909,1.182878e-7,0.000008009482,0.968059,0.010247836,0.020885007],"study_design_scores_gemma":[0.00029046796,0.00004522146,0.000009465431,0.00050184695,0.00010082544,0.000032788037,0.000012547517,0.00028375364,0.00042521054,0.001082708,0.9966564,0.00055874255],"about_ca_topic_score_codex":0.000080522885,"about_ca_topic_score_gemma":0.00000347786,"teacher_disagreement_score":0.9864086,"about_ca_system_score_codex":0.00007388929,"about_ca_system_score_gemma":0.0004905065,"threshold_uncertainty_score":0.9997697},"labels":[],"label_agreement":null},{"id":"W4411487056","doi":"10.1016/j.csda.2025.108235","title":"Enhancing approximate modular Bayesian inference by emulating the conditional posterior","year":2025,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Los Alamos National Laboratory; Sandia National Laboratories; Laboratory Directed Research and Development; Research and Development; Simon Fraser University","keywords":"Inference; Posterior probability; Bayesian probability; Bayesian inference; Modular design; Computer science; Mathematics; Artificial intelligence; Machine learning; Econometrics; Programming language","score_opus":0.015057138753383492,"score_gpt":0.30322771600744497,"score_spread":0.2881705772540615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411487056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000497116,0.00013382226,0.99520296,0.0011908705,0.0000792933,0.00012586989,0.002556221,0.000072717085,0.00014115758],"genre_scores_gemma":[0.61126715,0.00000809663,0.38352364,0.0006161033,0.000018393486,0.000011739691,0.004463464,0.000005307902,0.0000860938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764615,0.00012308879,0.0005881567,0.00076645403,0.00056105346,0.00031510208],"domain_scores_gemma":[0.99730504,0.0009145969,0.00027466437,0.0010659253,0.00035162678,0.000088168985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044650325,0.00022375118,0.00031571227,0.00025322553,0.0006106224,0.0008459974,0.0021114254,0.000053238793,0.000086753265],"category_scores_gemma":[0.0003182054,0.0001852656,0.0000680508,0.0019470083,0.0001301941,0.000646227,0.0010046841,0.00019473875,0.000019998783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007146034,0.00012220358,0.0060012257,0.00011236917,0.0012921217,0.000018452005,0.00027333255,0.099673994,0.00021408573,0.8634182,0.0054190285,0.02344782],"study_design_scores_gemma":[0.000117679956,0.000009981608,0.016221095,0.000020087,0.00023938264,0.0000025774236,0.000013012407,0.8087213,0.000028670307,0.17412923,0.00032884572,0.00016813692],"about_ca_topic_score_codex":0.00014515327,"about_ca_topic_score_gemma":0.00007651029,"teacher_disagreement_score":0.7090473,"about_ca_system_score_codex":0.000050718572,"about_ca_system_score_gemma":0.00030121976,"threshold_uncertainty_score":0.81579727},"labels":[],"label_agreement":null},{"id":"W4412599868","doi":"10.1016/j.csda.2025.108253","title":"Bayesian optimization sequential surrogate (BOSS) algorithm: Fast Bayesian inference for a broad class of Bayesian hierarchical models","year":2025,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Statistical Sciences Institute; Ministry of Colleges and Universities","keywords":"Bayesian probability; Boss; Bayesian inference; Inference; Computer science; Algorithm; Class (philosophy); Variable-order Bayesian network; Bayesian hierarchical modeling; Bayesian statistics; Surrogate model; Artificial intelligence; Mathematics; Machine learning; Engineering","score_opus":0.02687383401988679,"score_gpt":0.3155520691185523,"score_spread":0.2886782350986655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412599868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009618257,0.000076006036,0.9915836,0.0006770985,0.0001702746,0.00036453528,0.006712949,0.00009992943,0.0003060279],"genre_scores_gemma":[0.24668224,0.000043324402,0.7459247,0.00017560693,0.00004062129,0.00003209193,0.007012124,0.000015869065,0.00007339599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959956,0.00019968019,0.0011725164,0.0013038297,0.0007908661,0.0005375016],"domain_scores_gemma":[0.9958743,0.0009535196,0.0005417858,0.0013670856,0.0010289514,0.00023435723],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057241705,0.00040428864,0.0007736351,0.0009417118,0.0003946251,0.0005716823,0.0024919792,0.00016465811,0.00007182453],"category_scores_gemma":[0.00027705365,0.0004235606,0.00020117634,0.0031946222,0.0002522179,0.0011642845,0.0009245519,0.0002543674,0.0000033166586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019134946,0.00013724326,0.00040016216,0.00011241622,0.0006764671,0.0000076088054,0.000100307814,0.6614484,0.0000020490916,0.28118178,0.0007518966,0.055162508],"study_design_scores_gemma":[0.0004829539,0.00005250863,0.00063329726,0.00004508687,0.0005665473,0.0000022488596,0.000009725563,0.7531348,0.000009834315,0.24467556,0.00009639598,0.00029107087],"about_ca_topic_score_codex":0.00026095982,"about_ca_topic_score_gemma":0.00018020923,"teacher_disagreement_score":0.24667262,"about_ca_system_score_codex":0.000099726174,"about_ca_system_score_gemma":0.0009980281,"threshold_uncertainty_score":0.9998216},"labels":[],"label_agreement":null},{"id":"W4412834632","doi":"10.1088/2632-2153/adf521","title":"Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Perimeter Institute; TRIUMF","funders":"Office of Advanced Cyberinfrastructure; Basic Energy Sciences; National Research Council Canada; Mitacs","keywords":"Statistical physics; Symmetry breaking; Symmetry (geometry); Saddle point; Mathematics; Theoretical physics; Physics; Quantum mechanics; Geometry","score_opus":0.008793077527168259,"score_gpt":0.23569560271463158,"score_spread":0.22690252518746332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412834632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8173177,0.0035075806,0.1640905,0.012225119,0.00017703601,0.00008877535,1.4213029e-7,0.00034590022,0.0022471966],"genre_scores_gemma":[0.99420094,0.0006256926,0.0049335696,0.00009026749,0.000013775105,0.000016412721,2.5593175e-7,0.0000053107583,0.000113762544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847436,0.00007093449,0.00022913897,0.0005812858,0.0002904609,0.0003538005],"domain_scores_gemma":[0.9991861,0.00020018699,0.00012440023,0.0002851366,0.00014262542,0.00006156463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006213727,0.00015903787,0.00023505649,0.00087329076,0.0009787031,0.00020998025,0.00094312,0.0000858258,0.0000014781368],"category_scores_gemma":[0.00082665577,0.000110378576,0.00001963668,0.003527568,0.0010660576,0.0005070525,0.0012098666,0.0007111169,8.52937e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042248676,0.000013385054,0.045415685,0.000035382214,0.0000067512524,0.000006354465,0.0005536112,0.000017744327,0.0026747729,0.56143045,0.000004616756,0.389837],"study_design_scores_gemma":[0.0013100242,0.0016168127,0.033621244,0.0009487325,0.00005335342,0.00033142878,0.0034030161,0.59524983,0.049593076,0.25108477,0.061687082,0.0011006396],"about_ca_topic_score_codex":0.00013527059,"about_ca_topic_score_gemma":0.000035104327,"teacher_disagreement_score":0.59523207,"about_ca_system_score_codex":0.000021265909,"about_ca_system_score_gemma":0.00018918708,"threshold_uncertainty_score":0.7527492},"labels":[],"label_agreement":null},{"id":"W4413311598","doi":"10.1145/3729878.3746619","title":"Surrogate Model Assisted Evolutionary Algorithms: Performance Bound and Incremental Gaussian Process Model Updates","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Universitas Brawijaya","keywords":"Computer science; Gaussian process; Process (computing); Algorithm; Surrogate model; Gaussian; Machine learning","score_opus":0.014574536039092244,"score_gpt":0.260359836303127,"score_spread":0.24578530026403472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413311598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10856516,0.0002643128,0.86911273,0.0016900599,0.00008718209,0.0002030648,0.000009963088,0.0002656692,0.019801883],"genre_scores_gemma":[0.8899588,0.00008595581,0.10701701,0.000587591,0.000012072595,0.00003804166,0.000008908521,0.000007976688,0.0022836155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832714,0.000019184392,0.00032108426,0.00061810506,0.00030023555,0.0004142262],"domain_scores_gemma":[0.99922687,0.000021017813,0.00008423212,0.00039440923,0.00014945571,0.00012399722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001766748,0.00024531537,0.00021470811,0.00018600824,0.00041433892,0.00035155212,0.00078172056,0.000094545896,0.000013046369],"category_scores_gemma":[0.00001184356,0.00020949238,0.000038508868,0.00057235564,0.000132711,0.0015496007,0.00040936956,0.00017597547,0.000011950271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116701754,0.0009417364,0.022854004,0.0017353707,0.00023634697,0.000031231604,0.0019578985,0.04731811,0.002025134,0.7457155,0.007077114,0.16999085],"study_design_scores_gemma":[0.00036985165,0.000036108035,0.004307827,0.000084596984,0.000011150772,0.000019511966,0.00004841504,0.95998126,0.0009821224,0.0338712,0.000036174275,0.00025180104],"about_ca_topic_score_codex":0.000019434403,"about_ca_topic_score_gemma":0.000012008183,"teacher_disagreement_score":0.91266316,"about_ca_system_score_codex":0.0000705309,"about_ca_system_score_gemma":0.00050483306,"threshold_uncertainty_score":0.8542852},"labels":[],"label_agreement":null},{"id":"W4414230370","doi":"10.1016/j.energy.2025.138470","title":"Few-shot and continuous online learning for forecasting in the energy industry","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Fundação de Amparo à Pesquisa do Estado de São Paulo; Computer Modelling Group","keywords":"Interpretability; Extrapolation; Normalization (sociology); Time series; Autoregressive integrated moving average; Range (aeronautics); Artificial neural network; Autoencoder; Energy (signal processing)","score_opus":0.024774695697261265,"score_gpt":0.2589573903525069,"score_spread":0.23418269465524566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414230370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035302028,0.0005807923,0.95536906,0.0027536252,0.0001747799,0.00004986801,0.0000014203033,0.00005880702,0.005709649],"genre_scores_gemma":[0.9908182,0.00002647458,0.0060323197,0.0013509127,0.00006529285,0.000028777677,0.000004300862,0.000004428974,0.0016693042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923205,0.00004654565,0.00016404995,0.0002496224,0.000081774946,0.00022596466],"domain_scores_gemma":[0.9994858,0.00022074452,0.00005964086,0.00016897282,0.00003798896,0.00002684236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016703334,0.00009876881,0.00012466282,0.00009054823,0.00013464062,0.00016237247,0.0004646361,0.000105234925,0.000002475477],"category_scores_gemma":[0.00010051966,0.000072747585,0.000024716599,0.0003844347,0.000029202192,0.0001396748,0.0001394354,0.00019155844,1.153842e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055261557,0.00004020871,0.0022764076,0.000020453883,0.000007725043,0.00001046706,0.00030671761,0.0003564893,0.00007637582,0.5888188,0.0004957849,0.40758505],"study_design_scores_gemma":[0.0016558244,0.0003782531,0.012833336,0.00042150283,0.000020200634,0.000093575596,0.0012155912,0.52067477,0.0024037112,0.10195848,0.35772285,0.0006218969],"about_ca_topic_score_codex":0.00018588787,"about_ca_topic_score_gemma":0.00021729374,"teacher_disagreement_score":0.95551616,"about_ca_system_score_codex":0.000011723549,"about_ca_system_score_gemma":0.000061692765,"threshold_uncertainty_score":0.29665604},"labels":[],"label_agreement":null},{"id":"W4414633511","doi":"10.1109/cdc57313.2025.11312426","title":"Gaussian behaviors: representations and data-driven control","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaussian; Model predictive control; Gaussian process; Kalman filter; Gaussian random field; Dynamical systems theory; Subspace topology; Linear system; Stochastic control; Control theory (sociology)","score_opus":0.01962572285767628,"score_gpt":0.3157437507483046,"score_spread":0.2961180278906283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414633511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083682453,0.00008414385,0.96674675,0.00919702,0.00010829355,0.000119850374,0.000010254416,0.00012687089,0.022769991],"genre_scores_gemma":[0.94754535,0.000018332465,0.049885176,0.00094820344,0.000014227541,0.000012617783,0.000006668121,0.0000023045038,0.001567095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991472,0.000022490154,0.00015328715,0.0004304512,0.00009553946,0.0001510614],"domain_scores_gemma":[0.99885935,0.000055876095,0.000034540484,0.00094878464,0.0000405738,0.000060852915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008797232,0.00007887561,0.00010263578,0.00008353989,0.00012360542,0.00030963303,0.0010220184,0.00004218466,0.000027288701],"category_scores_gemma":[0.00003249304,0.00006454644,0.000013428619,0.0003066895,0.000048792946,0.0006878153,0.00046311264,0.000103065184,0.000017376276],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002745367,0.000067392,0.014637863,0.00002501891,0.000025052452,0.000016068578,0.00013790901,0.00001285193,0.00024239393,0.8766382,0.010028884,0.09816565],"study_design_scores_gemma":[0.0025109993,0.00012668094,0.35649815,0.00014740309,0.000121855235,0.000060158778,0.00019797655,0.5151072,0.00088925246,0.10261162,0.020991113,0.0007375981],"about_ca_topic_score_codex":0.00006296537,"about_ca_topic_score_gemma":0.00005062463,"teacher_disagreement_score":0.94670856,"about_ca_system_score_codex":0.000006834559,"about_ca_system_score_gemma":0.00008913885,"threshold_uncertainty_score":0.29857984},"labels":[],"label_agreement":null},{"id":"W4415319725","doi":"","title":"Towards a unified Gaussian process kernel-based correlation matrix representation for mixed-categorical variables","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Representation (politics); Process (computing); Gaussian process; Correlation; Matrix (chemical analysis); Covariance matrix","score_opus":0.014066770936595778,"score_gpt":0.2556078505137101,"score_spread":0.24154107957711432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415319725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043894504,0.00012791551,0.9736335,0.0119274175,0.00024573316,0.0005026819,0.000023465143,0.00029878542,0.008851013],"genre_scores_gemma":[0.8744093,0.000011035185,0.12314054,0.00012940173,0.000016180875,0.00034153814,0.0002146413,0.000023187637,0.0017141551],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963689,0.0014801729,0.00046399146,0.0007492134,0.0005610209,0.00037673852],"domain_scores_gemma":[0.9962897,0.0008534461,0.00042115533,0.001166174,0.0011131706,0.00015640439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028665932,0.00021494088,0.000232574,0.00021197795,0.0009234002,0.0004917195,0.0016398898,0.0000880043,0.00011322533],"category_scores_gemma":[0.0007951197,0.00022761602,0.00012419326,0.0013247839,0.000085025014,0.0004971597,0.00041064914,0.00027948423,0.000012945121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027892118,0.0004842393,0.0012965611,0.00009660836,0.000020985466,0.000005034243,0.003255776,0.002059479,0.0004870432,0.95102733,0.0009933262,0.0402457],"study_design_scores_gemma":[0.0012362445,0.0000047706276,0.0024829106,0.000119554614,0.000030953153,0.00003088467,0.0002920639,0.8655286,0.01974956,0.10301306,0.007039444,0.00047199294],"about_ca_topic_score_codex":0.00022499179,"about_ca_topic_score_gemma":0.00006926154,"teacher_disagreement_score":0.87001985,"about_ca_system_score_codex":0.00013910902,"about_ca_system_score_gemma":0.00062006305,"threshold_uncertainty_score":0.92819124},"labels":[],"label_agreement":null},{"id":"W4415464057","doi":"10.1038/s41467-025-64658-7","title":"Selecting fitted models under epistemic uncertainty using a stochastic process on quantile functions","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; European Commission; Government of Ontario","keywords":"Context (archaeology); Selection (genetic algorithm); Model selection; Nonparametric statistics; Replication (statistics); Quantile; Process (computing); Value (mathematics)","score_opus":0.03886914729248006,"score_gpt":0.3369107446174339,"score_spread":0.29804159732495383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415464057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004849644,0.0010816973,0.98030645,0.008336928,0.0002348111,0.0002656474,0.000009048959,0.0002876,0.0046281964],"genre_scores_gemma":[0.9822198,0.000018127887,0.016418947,0.0010433951,0.00001665125,0.000053900836,0.00001591649,0.000009920424,0.00020333938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987596,0.00010271725,0.00028056165,0.00037932812,0.0002010256,0.00027674384],"domain_scores_gemma":[0.9970698,0.00042694123,0.00014296376,0.0018987637,0.00040062534,0.00006095382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019096033,0.00017739659,0.00017765936,0.00026386647,0.00095165387,0.00021975898,0.0023521527,0.00022295317,0.0000037093785],"category_scores_gemma":[0.00015450073,0.0001670162,0.00006464697,0.0019454208,0.00008722901,0.0004184099,0.00040909054,0.0011099563,0.000010699718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007374281,0.00016687659,0.00007069908,0.000038383576,0.000042523934,4.0021658e-7,0.0003683157,0.15402794,0.00024229297,0.8425431,0.00045032927,0.0020417464],"study_design_scores_gemma":[0.00017375985,0.00002181553,0.00021652492,0.00023115297,0.000023726641,0.000009386291,0.00019911118,0.90648955,0.0000616351,0.09224085,0.00015954695,0.00017291427],"about_ca_topic_score_codex":0.00004075613,"about_ca_topic_score_gemma":0.00016982872,"teacher_disagreement_score":0.97737014,"about_ca_system_score_codex":0.00015435928,"about_ca_system_score_gemma":0.00052725995,"threshold_uncertainty_score":0.73194486},"labels":[],"label_agreement":null},{"id":"W4415645328","doi":"10.36227/techrxiv.176170993.37005709/v1","title":"PAC-Bayes Certificates for Bayesian Inverse Problems: A Case Study on the Heat Equation","year":2025,"lang":"","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Bayesian probability; Inverse problem; Bayesian inference; Inverse; Partial differential equation; Inference; Heat equation; Bayesian statistics","score_opus":0.07658943475100448,"score_gpt":0.3014791952017241,"score_spread":0.22488976045071962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415645328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015453219,0.0001513287,0.93256956,0.041836567,0.0007784554,0.004969836,0.000015627293,0.00018287015,0.004042552],"genre_scores_gemma":[0.98999476,0.000024188033,0.004103091,0.002227897,0.00008911447,0.00093185855,0.0000029888195,0.000024168497,0.0026019479],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958618,0.00034676518,0.001003148,0.0014681849,0.00048335627,0.0008366889],"domain_scores_gemma":[0.9962249,0.001207003,0.00021982972,0.0016851776,0.00047364796,0.00018947618],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001341455,0.0006016735,0.0004884396,0.00035330583,0.0015296629,0.0019081628,0.001580018,0.00018645926,0.00017247566],"category_scores_gemma":[0.0003884671,0.00039073598,0.00022940476,0.0018605812,0.00021615412,0.000698556,0.00042997644,0.00038764338,0.00008226144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025621525,0.0071766656,0.006920609,0.0016224198,0.0008159236,0.0007928817,0.04400768,0.0016543553,0.00055403175,0.7278399,0.029471943,0.17888737],"study_design_scores_gemma":[0.001549711,0.0024623324,0.0004070382,0.0005240242,0.00021082906,0.00014522148,0.01918206,0.9089352,0.0024013622,0.061611522,0.0017989351,0.00077175203],"about_ca_topic_score_codex":0.0009263325,"about_ca_topic_score_gemma":0.0011126855,"teacher_disagreement_score":0.97454154,"about_ca_system_score_codex":0.00014232885,"about_ca_system_score_gemma":0.0006474249,"threshold_uncertainty_score":0.99985445},"labels":[],"label_agreement":null},{"id":"W4415796655","doi":"10.52202/079017-2783","title":"Learning Diffusion Priors from Observations by Expectation Maximization","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fonds De La Recherche Scientifique - FNRS","keywords":"Prior probability; Maximization; Diffusion; Expectation–maximization algorithm; Term (time); Minification","score_opus":0.010926755493521977,"score_gpt":0.22026015942877503,"score_spread":0.20933340393525304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415796655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056497816,0.00018361518,0.93918383,0.0020074563,0.00021751387,0.000057915146,0.0000017221511,0.00052403344,0.0013260756],"genre_scores_gemma":[0.9372424,0.000056431338,0.06038,0.00012686689,0.000038955903,0.000012180326,0.000054916505,0.000007610789,0.0020806603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924994,0.000023256787,0.00014414285,0.0002987202,0.00016706217,0.000116911455],"domain_scores_gemma":[0.999673,0.00006550925,0.000030633895,0.00014623889,0.00004239175,0.000042187603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000046599802,0.000081350896,0.00005986199,0.00006080204,0.000127077,0.000521205,0.000255236,0.000043330285,0.00011129565],"category_scores_gemma":[0.000031237683,0.000068679525,0.000025087586,0.00047119174,0.000010048236,0.0008983785,0.00007468463,0.00009948824,0.00009220704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050068934,0.00013786444,0.02020887,0.000104865416,0.000044069682,0.000022090273,0.013240617,0.00092303957,0.06515583,0.46146762,0.020107,0.41858312],"study_design_scores_gemma":[0.00010717926,0.00005423068,0.014746209,0.00007494204,0.0000066853963,0.0000021510623,0.0002668182,0.9560449,0.0032861219,0.017113797,0.008079849,0.00021710353],"about_ca_topic_score_codex":0.000080118414,"about_ca_topic_score_gemma":0.000007089853,"teacher_disagreement_score":0.9551219,"about_ca_system_score_codex":0.000025670219,"about_ca_system_score_gemma":0.000045751494,"threshold_uncertainty_score":0.50259924},"labels":[],"label_agreement":null},{"id":"W4416435293","doi":"10.48550/arxiv.2511.01064","title":"Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homogeneous space; Inference; Symmetry (geometry); Divergence (linguistics); Class (philosophy); Key (lock); Bayesian inference","score_opus":0.044558728474328804,"score_gpt":0.3093164954479073,"score_spread":0.2647577669735785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416435293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30453777,0.0011052241,0.6876381,0.0040730466,0.00069349527,0.001269014,0.00011706569,0.000026568543,0.00053970946],"genre_scores_gemma":[0.96389455,0.00096141343,0.033844814,0.000511202,0.00016658583,0.00028701447,0.000018728864,0.000011392402,0.0003043222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9956248,0.00038922764,0.0012962022,0.0013787829,0.00065750506,0.0006535],"domain_scores_gemma":[0.9939562,0.0034868026,0.00060993904,0.0012945884,0.0005438578,0.0001086237],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014856327,0.0005513073,0.0008513898,0.00030626627,0.00024269985,0.00033135747,0.0032520683,0.00045656745,0.00004084129],"category_scores_gemma":[0.0018397877,0.0004295845,0.00021818173,0.0009698803,0.00043929263,0.00045532195,0.0016919633,0.00082599913,0.000008201983],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012878072,0.0004338081,0.25467488,0.0019551888,0.00012466477,0.00001060313,0.0039034165,0.000988756,0.00029040038,0.7292111,0.00012025508,0.00815815],"study_design_scores_gemma":[0.0014110489,0.00028276062,0.53647774,0.0016705964,0.00012873739,0.000013847868,0.0001533884,0.28528392,0.0007501743,0.17243275,0.000653405,0.00074163376],"about_ca_topic_score_codex":0.00023267447,"about_ca_topic_score_gemma":0.000057745045,"teacher_disagreement_score":0.6593568,"about_ca_system_score_codex":0.00004991747,"about_ca_system_score_gemma":0.0012964747,"threshold_uncertainty_score":0.9998156},"labels":[],"label_agreement":null},{"id":"W4416541551","doi":"10.48550/arxiv.2504.11835","title":"Particle Data Cloning for Complex Ordinary Differential Equations","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Ode; Ordinary differential equation; Frequentist inference; Particle filter; Inference; Cloning (programming); Statistical inference; Global optimization","score_opus":0.21055536980044232,"score_gpt":0.35851283317030547,"score_spread":0.14795746336986315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416541551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03184234,0.0001882432,0.96313685,0.0026233734,0.00088895834,0.00040389562,0.00022206723,0.00022312523,0.00047117323],"genre_scores_gemma":[0.9542699,0.00002162222,0.04391015,0.00028405775,0.00022026573,0.00011264148,0.00040626543,0.0000114280465,0.0007636577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979089,0.000052242416,0.00040704344,0.0010223696,0.00019830164,0.00041114583],"domain_scores_gemma":[0.9970058,0.00025625865,0.0002078929,0.0022773936,0.00014896863,0.00010368416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018490074,0.00026050466,0.00032494715,0.00007418958,0.00030362565,0.00035859414,0.0036851354,0.00015237708,0.00005486234],"category_scores_gemma":[0.00019718992,0.0002507603,0.00009874952,0.00025256278,0.00005883883,0.0003934856,0.006186524,0.0003389403,0.000043445645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116145995,0.0016458898,0.28848204,0.004028351,0.0009281411,0.00007040479,0.0026105247,0.0017330111,0.00489181,0.52094036,0.029905587,0.1446477],"study_design_scores_gemma":[0.000510094,0.00007361716,0.088167876,0.0003089635,0.00009534745,0.0000029684797,0.000022175527,0.8857242,0.0007440439,0.018628543,0.0051548537,0.00056732533],"about_ca_topic_score_codex":0.00007376508,"about_ca_topic_score_gemma":0.00003587676,"teacher_disagreement_score":0.9224276,"about_ca_system_score_codex":0.000038004273,"about_ca_system_score_gemma":0.00038960157,"threshold_uncertainty_score":0.99999446},"labels":[],"label_agreement":null},{"id":"W4416932885","doi":"10.48550/arxiv.2512.00170","title":"We Still Don't Understand High-Dimensional Bayesian Optimization","year":2025,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Research England; Natural Sciences and Engineering Research Council of Canada; National Science Foundation; Government of Canada; Canadian Institute for Advanced Research","keywords":"Curse of dimensionality; Bayesian optimization; Bayesian probability; Exploit; Gaussian process; Locality; Computation; Optimization problem","score_opus":0.045638139324294696,"score_gpt":0.18542309768829415,"score_spread":0.13978495836399946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416932885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033605834,0.0002847949,0.9863724,0.0015908047,0.0021797859,0.00069608184,0.00013782214,0.0003074562,0.005070232],"genre_scores_gemma":[0.95758957,0.0019460423,0.027545942,0.00035908047,0.00011789113,0.0000016316635,0.000063551255,0.000035396894,0.012340903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938623,0.00037441985,0.0007817427,0.0034643423,0.00039186142,0.0011253367],"domain_scores_gemma":[0.9952016,0.0002992852,0.0008520817,0.0022885108,0.0007262321,0.00063232216],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041487804,0.0011166842,0.0009907983,0.00089024205,0.0007385879,0.00071664725,0.0036348894,0.0009713589,0.0010910524],"category_scores_gemma":[0.00006940929,0.0013157037,0.00045830398,0.002839298,0.0005170047,0.001340027,0.0037412455,0.0013312631,0.000120473356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000958291,0.00020636285,0.00019214718,0.00035637277,0.00016752775,0.00030890483,0.00039792026,0.6806266,0.000004688799,0.31608462,0.00039899294,0.0011600682],"study_design_scores_gemma":[0.0012469515,0.00015928574,0.00013971428,0.0010575543,0.00027396347,0.000015492034,0.00025081885,0.893836,0.00010292582,0.101331584,0.00038361162,0.0012020813],"about_ca_topic_score_codex":0.0003671683,"about_ca_topic_score_gemma":0.0001046837,"teacher_disagreement_score":0.9588265,"about_ca_system_score_codex":0.0008279889,"about_ca_system_score_gemma":0.0023235397,"threshold_uncertainty_score":0.9998221},"labels":[],"label_agreement":null},{"id":"W4416958515","doi":"10.3847/1538-4357/ae0d87","title":"Gaussian Process Methods for Very Large Astrometric Data Sets","year":2025,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Velocity dispersion; Gaussian process; Gaussian; Dispersion (optics); Radial velocity; Stars; Milky Way; Tensor (intrinsic definition)","score_opus":0.03078282036648877,"score_gpt":0.3748417231400589,"score_spread":0.34405890277357015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416958515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018896833,0.00017277038,0.9916999,0.005164222,0.00049246324,0.00016523882,0.000018787518,0.0000502971,0.00034663655],"genre_scores_gemma":[0.43856844,0.0000143503585,0.5604304,0.0005402455,0.00027238013,0.0000151070835,0.0000055733526,0.000009904766,0.00014361272],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99824226,0.00017535486,0.0003291713,0.00041601437,0.0002756211,0.00056160084],"domain_scores_gemma":[0.9980503,0.00035569488,0.00019655212,0.0010735425,0.00018105788,0.00014290372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000973841,0.00019001745,0.0002725919,0.00018903372,0.0006694961,0.0007079463,0.0046408586,0.00006736801,0.00001095976],"category_scores_gemma":[0.00026688786,0.00011685749,0.00011549082,0.00145445,0.00006933968,0.0008804693,0.0008226169,0.0007044191,0.000015832587],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000524124,0.0003057114,0.00010981501,0.000075394804,0.00013701475,0.000010841026,0.00018655257,0.000049152382,0.0014268336,0.21048072,0.004767081,0.78239846],"study_design_scores_gemma":[0.002685033,0.00077997445,0.021653231,0.00032748352,0.00021012513,0.00026920903,0.00032696902,0.4248844,0.002868244,0.52136046,0.023921909,0.00071297266],"about_ca_topic_score_codex":0.0000013205231,"about_ca_topic_score_gemma":3.57722e-7,"teacher_disagreement_score":0.7816855,"about_ca_system_score_codex":0.000040868872,"about_ca_system_score_gemma":0.00038867237,"threshold_uncertainty_score":0.8623947},"labels":[],"label_agreement":null},{"id":"W4417051846","doi":"10.1109/lcsys.2025.3641128","title":"Learning-Based Shrinking Disturbance-Invariant Tubes for State- and Input-Dependent Uncertainty","year":2025,"lang":"","type":"article","venue":"IEEE Control Systems Letters","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polytope; Projection (relational algebra); Control theory (sociology); Block (permutation group theory); Set (abstract data type); Process (computing); Tube (container); Model predictive control; Gaussian","score_opus":0.008111585821831413,"score_gpt":0.22669855713231357,"score_spread":0.21858697131048216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417051846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023525717,0.002758988,0.9534777,0.012624734,0.0049827797,0.0022664792,0.000085829815,0.00015813812,0.00011966748],"genre_scores_gemma":[0.99291843,0.000049216982,0.0004050754,0.0050410423,0.00037801813,0.00052746956,0.0000055153573,0.000042368647,0.00063288055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944509,0.0005353255,0.0013430406,0.0016727817,0.00064785004,0.0013500805],"domain_scores_gemma":[0.9968495,0.0010642867,0.00051775837,0.0008781623,0.00038433212,0.00030599363],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014259322,0.0008000438,0.001222293,0.00039862166,0.00092961045,0.0026066257,0.0013346934,0.00024559902,0.000006002841],"category_scores_gemma":[0.00021663129,0.00074405846,0.0002718009,0.0006048361,0.00029882276,0.00057778653,0.0001437602,0.00066294277,0.000017055872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009871963,0.00031129865,0.013830188,0.007461235,0.0011429127,0.00027779242,0.0022244458,0.89746606,0.020724516,0.013655373,0.004572206,0.037346754],"study_design_scores_gemma":[0.006537207,0.00045409816,0.001138879,0.0021723432,0.0001935823,0.000021717147,0.00009062257,0.98319685,0.00051399495,0.0005155392,0.0042492705,0.00091589283],"about_ca_topic_score_codex":0.00060326763,"about_ca_topic_score_gemma":0.00006679864,"teacher_disagreement_score":0.9693927,"about_ca_system_score_codex":0.0003571478,"about_ca_system_score_gemma":0.0006537591,"threshold_uncertainty_score":0.99950105},"labels":[],"label_agreement":null},{"id":"W4417094045","doi":"10.48550/arxiv.2505.11294","title":"Bidirectional Information Flow (BIF) -- A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Modular design; Information flow; Gaussian process; Process (computing); Bayesian optimization; Information exchange; Bayesian probability; Sample (material); Interaction information","score_opus":0.024763455093046616,"score_gpt":0.2776121220878525,"score_spread":0.2528486669948059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417094045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023618483,0.000056842513,0.99036753,0.0026930068,0.0013950869,0.0010249314,0.00024307313,0.00042730602,0.0014303647],"genre_scores_gemma":[0.60635155,0.000049264978,0.39038992,0.0009041525,0.00036076515,0.0009249423,0.0007824434,0.000026459253,0.00021047397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725044,0.00006782013,0.0007636769,0.0008537249,0.0005104625,0.0005538825],"domain_scores_gemma":[0.9977688,0.00022864724,0.00042852014,0.00081145525,0.00055970217,0.00020283191],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035464487,0.00045852395,0.00044175496,0.00050048484,0.00045942466,0.00053124403,0.0014794873,0.00043090954,0.000052149033],"category_scores_gemma":[0.0004442545,0.00043866766,0.00022391518,0.00079009717,0.000081279766,0.0008526256,0.0007515341,0.00062705,0.000023484443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077568206,0.00027200635,0.006365032,0.0019228954,0.00010635068,0.000002805873,0.0019233598,0.9263713,0.0000039517986,0.029145889,0.00064126356,0.033167575],"study_design_scores_gemma":[0.00045009825,0.00006881882,0.002980216,0.00035807717,0.000032556996,0.0000064253018,0.000027271813,0.9830458,0.0002461375,0.010113166,0.0021731495,0.00049823825],"about_ca_topic_score_codex":0.00004642717,"about_ca_topic_score_gemma":0.000009150004,"teacher_disagreement_score":0.6039897,"about_ca_system_score_codex":0.00016860539,"about_ca_system_score_gemma":0.0012730145,"threshold_uncertainty_score":0.9998065},"labels":[],"label_agreement":null},{"id":"W629116014","doi":"","title":"Data Fusion Using Weighted Likelihood","year":2012,"lang":"en","type":"article","venue":"European Journal of Pure and Applied Mathematics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Mathematics; Estimator; Likelihood function; Sensor fusion; Population; Data set; Function (biology); Statistics; Algorithm; Data mining; Estimation theory; Artificial intelligence; Computer science","score_opus":0.04275030446220321,"score_gpt":0.25847207315425613,"score_spread":0.21572176869205292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W629116014","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033499893,0.00052213226,0.959829,0.00013596828,0.00013403203,0.000044302546,0.0000028262252,0.00001779605,0.005814051],"genre_scores_gemma":[0.46594104,0.000067292654,0.53367436,0.0000755536,0.00022409877,6.826539e-8,6.4614665e-7,0.000010102639,0.000006840947],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905866,0.00003041701,0.0003711974,0.0001115641,0.00021707693,0.00021107499],"domain_scores_gemma":[0.9989546,0.000033859393,0.0003823802,0.00040869418,0.000053043983,0.00016741028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010214488,0.00011229152,0.00017744846,0.00006379895,0.00008843856,0.0001451968,0.0009319081,0.000017797043,0.000010715424],"category_scores_gemma":[0.000017819288,0.00007875757,0.000023332459,0.00015048447,0.000023188433,0.0005339448,0.0005191466,0.0001607556,0.000019490104],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022760969,0.0010300925,0.0003637014,0.00074481504,0.00015875936,0.0001627201,0.012047829,0.0000057521347,0.02868548,0.3845053,0.0067545776,0.5655182],"study_design_scores_gemma":[0.013343742,0.0020537619,0.008613992,0.0063738693,0.0016588711,0.02204386,0.011628904,0.18170482,0.033609644,0.551649,0.16092105,0.006398504],"about_ca_topic_score_codex":5.0496755e-8,"about_ca_topic_score_gemma":5.4853437e-8,"teacher_disagreement_score":0.5591197,"about_ca_system_score_codex":0.0000062080953,"about_ca_system_score_gemma":0.0000321951,"threshold_uncertainty_score":0.32116404},"labels":[],"label_agreement":null},{"id":"W6892580772","doi":"10.5281/zenodo.10372964","title":"andreweckford/Figures-for-Optimal-Single-Letter-Codes-are-Kelly-Bets: Code associated with the first submission","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Code (set theory); Source code; Key (lock); Dead code; Software","score_opus":0.030879253799698213,"score_gpt":0.22832776866702556,"score_spread":0.19744851486732734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892580772","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011074505,0.00037123283,0.5355789,0.014074984,0.00050844497,0.0023359165,0.0017325608,0.009952334,0.43533483],"genre_scores_gemma":[0.051240142,0.0012767517,0.014159998,0.006689412,0.003113387,0.0000057541483,0.006203268,0.068161994,0.8491493],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99732184,0.00020134042,0.00028962264,0.00086696947,0.0006524932,0.00066771713],"domain_scores_gemma":[0.99782735,0.000094913805,0.0004921053,0.0009796435,0.00042124602,0.00018471247],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00070910127,0.00037491042,0.00034498348,0.00032811123,0.0020464729,0.0020110675,0.0033195454,0.00022976399,0.0019431632],"category_scores_gemma":[0.00046767227,0.0002775017,0.000096042844,0.0011166465,0.0002206559,0.00026339514,0.0012742135,0.00045878993,0.002130628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002621044,0.000091950395,0.0000047006693,0.00013381799,0.0000880337,0.000028011214,0.0003242816,0.000031200463,0.00004910689,0.0018107132,0.9829016,0.014510405],"study_design_scores_gemma":[0.00055383326,0.00033847656,0.0002152875,0.0005830794,0.000029738087,0.00003200504,0.00004179642,0.0012104929,0.00010463108,0.00010863926,0.9963818,0.00040019662],"about_ca_topic_score_codex":0.000016327209,"about_ca_topic_score_gemma":0.00002438269,"teacher_disagreement_score":0.5214189,"about_ca_system_score_codex":0.00018872283,"about_ca_system_score_gemma":0.000015499727,"threshold_uncertainty_score":0.9999677},"labels":[],"label_agreement":null},{"id":"W6901570368","doi":"10.60692/gkf5y-4hz29","title":"Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Missing data; Imputation (statistics); Bayesian probability; Time series; Gaussian process; Gaussian; Temporal database","score_opus":0.028381642443445987,"score_gpt":0.21779150287866972,"score_spread":0.18940986043522373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901570368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003233791,0.000012310808,0.9849775,0.000579621,0.00034708795,0.00038794952,0.00017502934,0.00060821726,0.009678492],"genre_scores_gemma":[0.96201104,8.316488e-8,0.037067745,0.00021090216,0.0000836239,0.0000619536,0.00016476904,0.000014999269,0.00038490776],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738216,0.0001789458,0.00077198265,0.00048893667,0.0007297383,0.00044826223],"domain_scores_gemma":[0.9976867,0.000010027755,0.00051675213,0.0014864198,0.00012342664,0.000176667],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00072253594,0.00028354043,0.0003055621,0.0003540863,0.0009041202,0.0012442241,0.0019266165,0.00007353982,0.000072004324],"category_scores_gemma":[0.000018221073,0.000258892,0.00006382358,0.00092277396,0.000042993626,0.0032661406,0.0011879784,0.00024041657,0.00033382745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004146032,0.00011116817,0.06473534,0.008805411,0.0006648442,0.0004632818,0.7256632,0.039093982,0.00002016319,0.08332947,0.010185623,0.06651295],"study_design_scores_gemma":[0.00097600184,0.00013728964,0.006931725,0.00014041278,0.00003720461,0.002167836,0.012328371,0.97398037,0.00017188789,0.00007658161,0.0021750643,0.00087725825],"about_ca_topic_score_codex":0.000006588227,"about_ca_topic_score_gemma":7.2201864e-8,"teacher_disagreement_score":0.95877725,"about_ca_system_score_codex":0.00015790295,"about_ca_system_score_gemma":0.00020725912,"threshold_uncertainty_score":0.99998635},"labels":[],"label_agreement":null},{"id":"W6901801966","doi":"10.60692/3hjpy-70555","title":"PyMC: a modern, and comprehensive probabilistic programming framework in Python","year":2023,"lang":"en","type":"article","venue":"Conicet","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"","keywords":"Probabilistic logic; Python (programming language); Statistical model; Variety (cybernetics); Syntax; Computation; Bayesian probability","score_opus":0.02633658509636526,"score_gpt":0.27802633377433744,"score_spread":0.25168974867797217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901801966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38364422,0.0012150145,0.6068177,0.0048012235,0.00042728163,0.00088971783,0.0000047220997,0.0010675114,0.001132546],"genre_scores_gemma":[0.96853125,0.000051097937,0.031042645,0.00022395948,0.000026218748,0.000058839738,0.0000018870471,0.000009486647,0.000054590386],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988787,0.000032938457,0.0001823788,0.00041241854,0.00014329859,0.00035025328],"domain_scores_gemma":[0.9992878,0.00017338322,0.000053921343,0.00034492614,0.00005833459,0.00008163156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001291822,0.0001245887,0.0001762388,0.000111634836,0.00007202403,0.00024039915,0.00042133415,0.0000788001,0.0000041269413],"category_scores_gemma":[0.000110239496,0.000114830385,0.000022141185,0.0007939828,0.00007156118,0.0002497473,0.00030305953,0.00020087743,0.000050106413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014721815,0.000097446726,0.00823895,0.0005790648,0.000019416439,0.00025066498,0.0071301856,0.00036344293,0.00023882448,0.46988466,0.0004138864,0.51276875],"study_design_scores_gemma":[0.00060367613,0.00017116067,0.045954056,0.0004846833,0.0000076195897,0.000048879123,0.00022080404,0.34545898,0.000092554765,0.59929216,0.007162635,0.00050279876],"about_ca_topic_score_codex":0.000014949592,"about_ca_topic_score_gemma":0.000017728586,"teacher_disagreement_score":0.584887,"about_ca_system_score_codex":0.00002157967,"about_ca_system_score_gemma":0.00008461388,"threshold_uncertainty_score":0.46826473},"labels":[],"label_agreement":null},{"id":"W6901964892","doi":"10.60692/trdg8-f5t82","title":"PyMC: a modern, and comprehensive probabilistic programming framework in Python","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"","keywords":"Probabilistic logic; Python (programming language); Statistical model; Variety (cybernetics); Syntax; Computation; Bayesian probability","score_opus":0.03472991084086661,"score_gpt":0.23163860400143169,"score_spread":0.19690869316056508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901964892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25699213,0.0000065840777,0.7410545,0.00018171071,0.000195266,0.00048237597,0.000006632406,0.0006567033,0.00042415014],"genre_scores_gemma":[0.9875899,3.230748e-7,0.012160232,0.00008818926,0.000020297826,0.000114566246,0.000003321046,0.000005987647,0.000017183465],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987395,0.00003870047,0.0004380664,0.00022398843,0.00024588604,0.00031385702],"domain_scores_gemma":[0.99926466,0.00002129359,0.00016263299,0.00034869488,0.000118637305,0.000084063286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020606394,0.00015512932,0.00019956463,0.00025876611,0.00010387149,0.00055626786,0.00032375968,0.00010010353,0.0000010941764],"category_scores_gemma":[0.000032896973,0.00013002096,0.000027554988,0.0009153699,0.00003126762,0.0011051681,0.00020240995,0.00013246667,0.00023055573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004844781,0.000010236849,0.18222992,0.005880276,0.000044488912,0.00008574985,0.5699644,0.002354494,0.0000033477381,0.13690451,0.0000987098,0.10237543],"study_design_scores_gemma":[0.00096920464,0.00011664094,0.21532771,0.0014169954,0.0000099078625,0.00013420814,0.010007809,0.76816833,0.0000500248,0.0026922356,0.00048820398,0.00061870663],"about_ca_topic_score_codex":0.0000048299644,"about_ca_topic_score_gemma":2.5589136e-7,"teacher_disagreement_score":0.7658139,"about_ca_system_score_codex":0.000050445586,"about_ca_system_score_gemma":0.00004346093,"threshold_uncertainty_score":0.5364104},"labels":[],"label_agreement":null},{"id":"W6912405193","doi":"10.5281/zenodo.4678962","title":"The Shallow Gibbs Network, Double Backpropagation and Differential Machine learning","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gibbs sampling; Backpropagation; Artificial neural network; Mean squared error; Feedforward neural network; Markov chain; Extreme learning machine; Cluster analysis","score_opus":0.021835971598890707,"score_gpt":0.22033762501264598,"score_spread":0.19850165341375528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912405193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016650684,0.001168391,0.8838561,0.0075831236,0.00039518363,0.0004251348,0.000017089978,0.0012770925,0.0886272],"genre_scores_gemma":[0.99508345,0.00045382968,0.0017151418,0.00009730714,0.00015512515,5.49865e-8,0.00020368377,0.000277956,0.0020134319],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986581,0.00021350797,0.00017157945,0.00037144907,0.00026535243,0.0003199844],"domain_scores_gemma":[0.9990697,0.00003197162,0.00008219185,0.00034616966,0.0003577218,0.000112280715],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037731667,0.00010500738,0.00009238332,0.000039401024,0.003299177,0.002713251,0.0008799407,0.000038827977,0.0010193555],"category_scores_gemma":[0.00014817098,0.000085497646,0.000028357856,0.000474602,0.00008942672,0.0003462518,0.0016140358,0.0002709807,0.0005842531],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065116765,0.000096913696,0.000107455955,0.000089318164,0.00005890222,0.0000450904,0.0011565929,0.00026657208,0.0032126913,0.33300036,0.015696077,0.6462049],"study_design_scores_gemma":[0.0006513165,0.0001423977,0.0025701562,0.00003441597,0.000008797848,0.00021799559,0.00007327303,0.042168353,0.0005816486,0.0034476994,0.9498973,0.00020667858],"about_ca_topic_score_codex":0.0000058539167,"about_ca_topic_score_gemma":0.000002004386,"teacher_disagreement_score":0.9784328,"about_ca_system_score_codex":0.000031899177,"about_ca_system_score_gemma":0.0000076176107,"threshold_uncertainty_score":0.99989384},"labels":[],"label_agreement":null},{"id":"W6920787118","doi":"10.6084/m9.figshare.14516293","title":"Additional file 1 of Dynamic model updating (DMU) approach for statistical learning model building with missing data","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Missing data; Table (database); Model building; Statistical model; Regression analysis; Data modeling; Data file","score_opus":0.05560412676296453,"score_gpt":0.28210849593789916,"score_spread":0.22650436917493463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920787118","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.4588874e-7,0.000008355728,0.49739859,0.000016829243,0.0000014499051,0.00003898367,0.50171775,0.000028560287,0.0007892648],"genre_scores_gemma":[0.0022679456,1.0899697e-7,0.5207232,0.000028252056,0.000009187139,0.00008871013,0.47676113,0.000008484144,0.000112944894],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987589,0.00001828164,0.00019449975,0.0005349585,0.0002441767,0.0002491464],"domain_scores_gemma":[0.99838597,0.0006877295,0.00016254085,0.00045062293,0.00023713167,0.00007603255],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003747799,0.00012600291,0.00016211183,0.000036800302,0.0001788541,0.00018906895,0.00075645285,0.00005088283,0.28158697],"category_scores_gemma":[0.002073951,0.00011754541,0.00002535603,0.00020983625,0.000010581615,0.0005738237,0.00053132296,0.00017086563,0.00001368389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033225506,0.000037084203,2.1663138e-7,0.00039660072,0.000012361414,0.0000061994133,0.000044428707,0.048237503,0.000028105022,0.0020320418,0.9343627,0.01483943],"study_design_scores_gemma":[0.000096638556,0.00001886272,0.000009791718,0.0011208956,0.0000043303157,0.000022446498,0.000021780235,0.9927174,0.000025017276,0.0019376815,0.0038649787,0.00016019159],"about_ca_topic_score_codex":3.8641775e-7,"about_ca_topic_score_gemma":0.0000010620565,"teacher_disagreement_score":0.9444799,"about_ca_system_score_codex":0.000021970494,"about_ca_system_score_gemma":0.0007036415,"threshold_uncertainty_score":0.7190697},"labels":[],"label_agreement":null},{"id":"W6928997170","doi":"10.3969/j.issn.1673-5765.2024.01.008","title":"癫痫及惊厥性癫痫持续状态医疗质量控制指标 Medical Quality Control Indicators for Epilepsy and Convulsive Status Epilepticus","year":2024,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Epilepsy; Accreditation; Status epilepticus; Quality (philosophy); Control (management); Health care; Commission","score_opus":0.13926872520592334,"score_gpt":0.5457462763740935,"score_spread":0.4064775511681701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6928997170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17515947,0.051953707,0.759386,0.0041402103,0.0031904024,0.0017380089,0.00033948646,0.00029421356,0.0037984983],"genre_scores_gemma":[0.98996323,0.006327222,0.002255243,0.0009398329,0.00021530343,0.00013011774,0.0000079613355,0.00003880949,0.0001222525],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99554443,0.00033353045,0.0012556298,0.00093537424,0.0011762927,0.00075473054],"domain_scores_gemma":[0.9954916,0.001999277,0.00066634384,0.0006271644,0.00033006133,0.000885526],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027646779,0.0004110058,0.0009964062,0.00089858886,0.00031744796,0.003991899,0.00430354,0.0002268348,0.0022794013],"category_scores_gemma":[0.0013196963,0.00033464743,0.00022534709,0.0014297023,0.0003606089,0.0034882694,0.001203234,0.0006121276,0.000020214027],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028659357,0.00043213012,0.29982677,0.001472948,0.0007398964,0.00032940583,0.0013203907,0.000017115368,0.00465221,0.11438104,0.042882886,0.5336586],"study_design_scores_gemma":[0.0030127673,0.00013879099,0.741017,0.0020513309,0.00021577455,0.00015076238,0.0001710343,0.010263815,0.0058898157,0.18102339,0.054480657,0.0015848774],"about_ca_topic_score_codex":0.00027962116,"about_ca_topic_score_gemma":0.000031353935,"teacher_disagreement_score":0.8148038,"about_ca_system_score_codex":0.00009535772,"about_ca_system_score_gemma":0.0009644593,"threshold_uncertainty_score":0.99991053},"labels":[],"label_agreement":null},{"id":"W6930206008","doi":"10.5281/zenodo.12103317","title":"Tabaluga noten pdf","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ninth; Download; The arts; Digital audio; Eleventh","score_opus":0.02563736327878423,"score_gpt":0.2407464714178054,"score_spread":0.21510910813902115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930206008","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010535351,0.0017042018,0.09972757,0.0022162017,0.0010376851,0.0005396877,0.00030326893,0.0014944351,0.8929664],"genre_scores_gemma":[0.015493817,0.0013311266,0.004490109,0.00071982475,0.002175014,1.360954e-7,0.0018156637,0.0134722395,0.9605021],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99574333,0.00035536927,0.0005382911,0.0015595874,0.00088798424,0.0009154161],"domain_scores_gemma":[0.9971745,0.000022806755,0.00029589445,0.0014260573,0.0006279348,0.00045278796],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00074842066,0.00053060317,0.00043572023,0.0006942912,0.0016675144,0.0065963813,0.0048190923,0.00033564534,0.28194594],"category_scores_gemma":[0.00038932278,0.00053416943,0.00018010501,0.0016516036,0.00034708477,0.0004144578,0.0049420125,0.0010059981,0.7777405],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012497444,0.00011314141,2.46551e-7,0.00056730007,0.00012919857,0.00014421175,0.0009253178,0.0000048158336,0.00015822216,0.035279773,0.8318458,0.13081947],"study_design_scores_gemma":[0.00028501748,0.00028006628,0.000027038308,0.00057408086,0.00006478389,0.00023438984,0.000072727125,0.0017324188,0.0000617021,0.0028452724,0.9932206,0.0006018555],"about_ca_topic_score_codex":0.000023132568,"about_ca_topic_score_gemma":4.803302e-7,"teacher_disagreement_score":0.49579453,"about_ca_system_score_codex":0.00022827122,"about_ca_system_score_gemma":0.0000257472,"threshold_uncertainty_score":0.999711},"labels":[],"label_agreement":null},{"id":"W6931023993","doi":"10.5281/zenodo.13263914","title":"Fig. 1. The Devonian actinopterygian Cheirolepis canadensis Whiteaves, 1881 from Miguasha, Canada. A. MHNM 05-132 in A microanatomical and histological study of the postcranial dermal skeleton of the Devonian actinopterygian Cheirolepis canadensis","year":2015,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Devonian; Transversal (combinatorics); Postcrania; Dorsum; Dorsal fin; Fish fin; Apex (geometry)","score_opus":0.015736876315175903,"score_gpt":0.20810129983371725,"score_spread":0.19236442351854136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931023993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.863395,0.0013358643,0.00043571787,0.0297574,0.0013565148,0.0045943838,0.0033350773,0.00049313944,0.09529694],"genre_scores_gemma":[0.9943333,0.000035346522,0.00014311618,0.0004417227,0.00010936579,4.0053538e-7,0.00008447192,0.0007735454,0.0040787305],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959544,0.000890394,0.0006426168,0.0008778516,0.0009818705,0.00065286754],"domain_scores_gemma":[0.99706995,0.000071856084,0.00066778186,0.0014924785,0.00039755533,0.00030040112],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048457485,0.00044837955,0.00059383665,0.00024779444,0.00088986405,0.00039872117,0.004553695,0.00026592065,0.0013001087],"category_scores_gemma":[0.0004984232,0.00029324615,0.0000906416,0.0011230545,0.00046795062,0.00013375249,0.002746466,0.00091761205,0.000032166317],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001522288,0.0006415767,0.002371004,0.00028357728,0.00049569854,0.00019225456,0.014196814,0.000027108288,0.0014809298,0.00045122727,0.9466738,0.033033762],"study_design_scores_gemma":[0.0013876336,0.00025901108,0.06344184,0.00033589816,0.000105239495,0.0002535329,0.0017985605,0.000455463,0.00052083784,0.000065590975,0.9307274,0.0006489674],"about_ca_topic_score_codex":0.38591036,"about_ca_topic_score_gemma":0.45363882,"teacher_disagreement_score":0.13093834,"about_ca_system_score_codex":0.00056446024,"about_ca_system_score_gemma":0.00039370832,"threshold_uncertainty_score":0.99995196},"labels":[],"label_agreement":null},{"id":"W6958071835","doi":"10.6068/dp14ba7bf422d36","title":"Trend 1995 - 2012. Statistics Canada. CANSIM: Energy - Energy Consumption and Disposition | Country: Canada | Table: Supply and demand of refined petroleum products for non-energy use | Variable: Asphalt, Availability | Units: Megalitres, 1995-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-078.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Energy consumption; Consumption (sociology); Official statistics; Summary statistics; Petroleum; Descriptive statistics; Energy source; Production (economics); Energy (signal processing)","score_opus":0.017965314031757662,"score_gpt":0.2231869220904837,"score_spread":0.20522160805872605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958071835","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.8373757e-7,0.0039778333,0.007861098,0.000012303073,0.0004982414,0.0002492852,0.9869553,0.00004929817,0.0003964993],"genre_scores_gemma":[0.000084412226,0.002028951,0.0030556098,0.00025590014,0.00015068991,0.000036745478,0.9905268,0.00010413865,0.003756787],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99541885,0.00031457198,0.0009586626,0.0017122108,0.0008392496,0.00075646455],"domain_scores_gemma":[0.9948294,0.00076040725,0.00092151086,0.002749446,0.00019821801,0.0005409872],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005103846,0.00072425295,0.0010277343,0.00012654415,0.00018825747,0.00048656235,0.0017985614,0.00039008807,0.00031882213],"category_scores_gemma":[0.000110700756,0.0006734868,2.6703043e-7,0.0003005987,0.00030340784,0.0011008041,0.00080735754,0.00028921294,0.0000011254725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001249892,0.00007626518,0.000016301023,0.0011702012,0.00016820448,0.00008891975,0.000001365983,0.000008104612,0.000010640512,0.021120014,0.9763916,0.0008233636],"study_design_scores_gemma":[0.00084348355,0.00015698856,0.000011346211,0.000084916675,0.00026844034,0.00014494834,0.000008492708,0.018418962,6.669806e-7,0.000005978319,0.9793176,0.0007381588],"about_ca_topic_score_codex":0.99912745,"about_ca_topic_score_gemma":0.9982162,"teacher_disagreement_score":0.021114036,"about_ca_system_score_codex":0.00016812376,"about_ca_system_score_gemma":0.009735229,"threshold_uncertainty_score":0.9995716},"labels":[],"label_agreement":null},{"id":"W6991627127","doi":"","title":"High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders","year":2025,"lang":"en","type":"article","venue":"University of Southern Denmark Research Portal (University of Southern Denmark)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Gaussian process; Inference; Covariate; Generative model; Bayesian probability; Surrogate model; Bayesian inference; Probabilistic logic; Space (punctuation)","score_opus":0.012857521699037486,"score_gpt":0.23478779618597115,"score_spread":0.22193027448693367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991627127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2668468,0.000093967516,0.6980342,0.0037152146,0.00011022054,0.00091328163,0.00048955303,0.00015193904,0.029644748],"genre_scores_gemma":[0.9337762,0.000016657617,0.056023646,0.000035182282,0.00002002539,3.4052917e-7,0.00003479418,0.00001860035,0.010074526],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9964268,0.00027034266,0.00028482903,0.00086333125,0.001515977,0.00063872384],"domain_scores_gemma":[0.99717444,0.00021520598,0.00043521973,0.0007257096,0.00116148,0.00028795944],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009530445,0.00030636773,0.0004804373,0.00093207514,0.00095240894,0.0000815456,0.0022196763,0.00026635412,0.0027355913],"category_scores_gemma":[0.00006271624,0.00034106665,0.00018014126,0.0018990213,0.0010330089,0.00062518293,0.0005987378,0.0005584211,0.00028624292],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013646252,0.007831422,0.13123025,0.0065992502,0.0069743367,0.005805458,0.23335356,0.04391632,0.004582219,0.41069034,0.023230348,0.11214023],"study_design_scores_gemma":[0.022087524,0.0024989627,0.044946093,0.0045224787,0.0008598787,0.00019144478,0.5316054,0.30907044,0.0011726607,0.07575548,0.0030377673,0.004251831],"about_ca_topic_score_codex":0.0017607153,"about_ca_topic_score_gemma":0.0009405297,"teacher_disagreement_score":0.6669294,"about_ca_system_score_codex":0.000114720846,"about_ca_system_score_gemma":0.0016752351,"threshold_uncertainty_score":0.99990416},"labels":[],"label_agreement":null},{"id":"W7008247519","doi":"","title":"Bayesian sparse factor regression trees","year":2018,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Principal component analysis; Pattern recognition (psychology); Regression; Random forest; Dimension (graph theory); Bayesian probability; Artificial neural network; Variance (accounting); Sparse approximation","score_opus":0.019270393338728632,"score_gpt":0.25426093472300654,"score_spread":0.2349905413842779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008247519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6452434,0.0014250218,0.0013009269,0.0002374358,0.014172288,0.0025434168,0.0019492953,0.0036119467,0.32951626],"genre_scores_gemma":[0.9676649,0.00030067397,0.016625136,0.00032648092,0.00019085717,0.000118298965,0.00037360488,0.00020301508,0.014197047],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9933954,0.00029422197,0.0011959288,0.002364209,0.001448064,0.0013021699],"domain_scores_gemma":[0.9950138,0.00016122099,0.0011290493,0.0022686992,0.0007050948,0.00072214194],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00049809524,0.0013295602,0.001079455,0.0006837168,0.0016440223,0.0006488199,0.0039101676,0.0012713223,0.0006866066],"category_scores_gemma":[0.00046968454,0.0011782534,0.0005052854,0.0012894892,0.00010615025,0.0024562995,0.0005133573,0.0016511695,0.0012656654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001257077,0.00032650086,0.0000497476,0.000550554,0.00016727165,0.0002497444,0.000058909096,0.000002334367,0.01221463,0.24588443,0.00013671462,0.7402335],"study_design_scores_gemma":[0.0027227914,0.0015368576,0.01319457,0.0058555612,0.0003322004,0.0002512817,0.0003724027,0.0017367735,0.30692005,0.37582657,0.28357172,0.0076792357],"about_ca_topic_score_codex":0.00015340146,"about_ca_topic_score_gemma":0.0014230174,"teacher_disagreement_score":0.7325542,"about_ca_system_score_codex":0.00043257396,"about_ca_system_score_gemma":0.00022063231,"threshold_uncertainty_score":0.9999456},"labels":[],"label_agreement":null},{"id":"W7009955722","doi":"","title":"Fondations of Machine Learning, part 1","year":2019,"lang":"en","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; AXA Research Fund","keywords":"Model selection; Selection (genetic algorithm); Series (stratigraphy); Econometric model; Maximum likelihood; Time series","score_opus":0.013243436653824244,"score_gpt":0.23605458000313972,"score_spread":0.22281114334931548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009955722","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016610842,0.0006334369,0.20610525,0.0070838192,0.001980127,0.0006146612,0.00044243634,0.00038035138,0.78275824],"genre_scores_gemma":[0.0051580705,0.0009241835,0.018239502,0.0044986163,0.0007417835,0.00024031162,0.002189641,0.00027731596,0.9677306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99746305,0.000129122,0.0005766997,0.00079051644,0.00065874716,0.0003818839],"domain_scores_gemma":[0.99762243,0.000087961394,0.00090733054,0.000996806,0.00022249264,0.00016295107],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025160707,0.00042678523,0.0005751297,0.0004505427,0.0001649529,0.00027516246,0.0015029863,0.0003447754,0.012362245],"category_scores_gemma":[0.000090589725,0.0004131593,0.00017175144,0.0006062744,0.0001183687,0.0042453525,0.00039564807,0.00043370374,0.004371296],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004973951,0.00013886033,0.000051153922,0.00023435711,0.00006503806,0.0000101279775,0.00002891687,0.000027720032,0.000021622518,0.50486284,0.4891381,0.0054163206],"study_design_scores_gemma":[0.00050988526,0.00018878313,0.00031652066,0.0005655868,0.000037874208,0.000021677917,0.000010770516,0.00091154565,0.00032065393,0.002256059,0.9943557,0.0005049427],"about_ca_topic_score_codex":0.000109209584,"about_ca_topic_score_gemma":0.00036851788,"teacher_disagreement_score":0.5052176,"about_ca_system_score_codex":0.000049565013,"about_ca_system_score_gemma":0.00031534507,"threshold_uncertainty_score":0.99983203},"labels":[],"label_agreement":null},{"id":"W7017935454","doi":"","title":"Clustering Gaussian Processes: A Modified EM Algorithm for Functional Data Analysis with Application to British Columbia Coastal Rainfall Patterns","year":2018,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Functional data analysis; Covariance; Kernel (algebra); Generalization; Gaussian process; Series (stratigraphy); Probability density function; Cluster (spacecraft); Time series; Gaussian","score_opus":0.014372882867084996,"score_gpt":0.21572708011146496,"score_spread":0.20135419724437997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017935454","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006459227,0.000014593311,0.9845753,0.00004262529,0.00017441875,0.00092558376,0.0010579776,0.00018115442,0.012382473],"genre_scores_gemma":[0.05379048,0.000034559038,0.40263727,0.0003547833,0.0005235407,0.00012619037,0.014823587,0.00014798301,0.5275616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968609,0.000037170612,0.00033648056,0.0017390361,0.0005047895,0.00052158546],"domain_scores_gemma":[0.99750894,0.000044830107,0.0004082382,0.0011068687,0.0006554994,0.0002755957],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013253906,0.00036854736,0.000526445,0.00029058722,0.00046615256,0.0018408182,0.0025376726,0.0002632937,0.0022367393],"category_scores_gemma":[0.000015387668,0.0005205318,0.00012027865,0.0025683371,0.000037560934,0.0011139329,0.0005720359,0.00021696006,0.00001693177],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115492316,0.00012621525,0.0010177187,0.000717173,0.00071394944,0.000053470532,0.00080861873,0.0003214759,0.0000055322075,0.000076865006,0.0013712182,0.9946723],"study_design_scores_gemma":[0.0048589003,0.0013587094,0.0344935,0.0017318114,0.0033174457,0.0001002144,0.008523669,0.78111744,0.00006207588,0.00057292613,0.15971792,0.0041453983],"about_ca_topic_score_codex":0.003956763,"about_ca_topic_score_gemma":0.54285103,"teacher_disagreement_score":0.99052685,"about_ca_system_score_codex":0.00011178705,"about_ca_system_score_gemma":0.00055549445,"threshold_uncertainty_score":0.9997246},"labels":[],"label_agreement":null},{"id":"W7019253924","doi":"","title":"Forecasting battles : New machine learning methods for predicting armed conflict","year":2025,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Lagging; Salient; Field (mathematics); Feature (linguistics); Geospatial analysis; Process (computing); Time series; Armed conflict; Core (optical fiber)","score_opus":0.04293247774335235,"score_gpt":0.3313612054497515,"score_spread":0.28842872770639916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019253924","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073914276,0.0006578331,0.9819434,0.011037777,0.00046623498,0.000503064,0.00006752019,0.000739828,0.0038451706],"genre_scores_gemma":[0.06963777,0.00005005755,0.9279757,0.0002537563,0.000052777967,0.00016379658,0.00032430937,0.000014654848,0.0015271436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979131,0.0000428832,0.00067804166,0.00073743163,0.0001780953,0.00045040133],"domain_scores_gemma":[0.9975835,0.00020358698,0.0005351244,0.0010198599,0.0005429624,0.000114935865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008561094,0.00025517345,0.0003671298,0.0009376388,0.00039294967,0.0002145517,0.0019915707,0.0002154446,0.000021941505],"category_scores_gemma":[0.005103995,0.00024461496,0.00008776234,0.0019366771,0.00021787852,0.0010982427,0.0010471129,0.00043935527,0.000003736471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146922885,0.00008275461,0.0054625524,0.00026768012,0.00007179234,0.0000015876963,0.00006844874,0.00023090925,0.00075893797,0.4340387,0.0038610909,0.55514085],"study_design_scores_gemma":[0.00075344683,0.000089074325,0.00027578854,0.00025962736,0.000039969866,0.0000097216225,0.00003758057,0.5619737,0.013859893,0.005051569,0.41738966,0.0002599731],"about_ca_topic_score_codex":0.00012676395,"about_ca_topic_score_gemma":0.000025166864,"teacher_disagreement_score":0.5617428,"about_ca_system_score_codex":0.000055706274,"about_ca_system_score_gemma":0.0005481471,"threshold_uncertainty_score":0.99751085},"labels":[],"label_agreement":null},{"id":"W7027044984","doi":"","title":"Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models","year":2003,"lang":"en","type":"article","venue":"TSpace","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Government of Ontario","keywords":"Graphical model; Inference; Divergence (linguistics); Energy (signal processing); Approximate inference; Approximations of π","score_opus":0.021684589458569426,"score_gpt":0.26471463011599955,"score_spread":0.2430300406574301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027044984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014242491,0.0002512962,0.99195397,0.0012103468,0.00008614751,0.000121160985,0.0000074426443,0.00009933466,0.0048460453],"genre_scores_gemma":[0.8743914,0.00006179825,0.12473485,0.00017580527,0.000014411189,0.00005897451,0.0000015916629,0.0000068288505,0.0005543678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991558,0.000040725714,0.00011591954,0.0003189885,0.00012859893,0.0002399558],"domain_scores_gemma":[0.99925965,0.00014738346,0.000058000423,0.00030955087,0.00011835643,0.00010706387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011335059,0.00011686153,0.00013210057,0.0000613142,0.00018853506,0.00010526349,0.00039490598,0.000060666174,0.000010680675],"category_scores_gemma":[0.00012132537,0.0001005958,0.000035306606,0.0003455626,0.000078183206,0.00029385404,0.00008691768,0.00005607947,9.973932e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028912186,0.000025578862,0.000056950466,0.0000122656365,0.000010529549,9.804534e-7,0.000685247,0.000055056393,0.00014484317,0.9966383,0.00048274288,0.0018846013],"study_design_scores_gemma":[0.00045371611,0.000079977566,0.00033183745,0.000018485334,0.000011054508,0.000012358392,0.00012334985,0.19585864,0.0019753561,0.7983374,0.0025800646,0.00021774402],"about_ca_topic_score_codex":0.00003789747,"about_ca_topic_score_gemma":0.000036000045,"teacher_disagreement_score":0.8729671,"about_ca_system_score_codex":0.000010832624,"about_ca_system_score_gemma":0.000068126115,"threshold_uncertainty_score":0.41021776},"labels":[],"label_agreement":null},{"id":"W7034331859","doi":"","title":"Tapaustutkimus simulaatiopelin käytöstä suomalaisen peruskoulun kuudennen luokan yhteiskuntaopin tunnilla","year":2021,"lang":"fi","type":"other","venue":"Työväentutkimus Vuosikirja","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Licensee; Quarter (Canadian coin)","score_opus":0.01790969605422471,"score_gpt":0.25942626886740455,"score_spread":0.24151657281317984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034331859","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024507314,0.026708513,0.08406924,0.0070856926,0.009220902,0.0035871358,0.0005633715,0.0015765461,0.86473787],"genre_scores_gemma":[0.21777277,0.0068308664,0.017325792,0.004224971,0.005248438,0.00031086348,0.0009031883,0.0014838427,0.74589926],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98397154,0.0009055972,0.0029492872,0.005357821,0.0029766334,0.0038390995],"domain_scores_gemma":[0.9889726,0.00058834493,0.0020956257,0.0054644374,0.0011297233,0.0017492869],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0011434911,0.0033343276,0.0033674368,0.0013782553,0.0011883536,0.003274204,0.0074822255,0.0025020526,0.045033213],"category_scores_gemma":[0.0004782132,0.003412089,0.0013747427,0.0036529396,0.00092626404,0.001031939,0.003446874,0.0027066115,0.008865468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040489883,0.0058073783,0.008811188,0.010919052,0.0044251783,0.0097693205,0.010930117,0.0035517307,0.0040596733,0.21012089,0.37209722,0.35910335],"study_design_scores_gemma":[0.0027328355,0.00063416327,0.0029054487,0.0032154354,0.00047132612,0.00043608158,0.00036615747,0.017441742,0.0008853569,0.0017701171,0.9644428,0.0046985205],"about_ca_topic_score_codex":0.0013563627,"about_ca_topic_score_gemma":0.0009185624,"teacher_disagreement_score":0.5923456,"about_ca_system_score_codex":0.0008032069,"about_ca_system_score_gemma":0.0026590843,"threshold_uncertainty_score":0.9995942},"labels":[],"label_agreement":null},{"id":"W7035138434","doi":"","title":"Формування інституту військового омбудсмена в Україні: роль і значення принципу незалежності","year":2025,"lang":"en","type":"article","venue":"The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Impartiality; Institution; Autonomy; Balance (ability); Democracy; Independence (probability theory); Control (management); Confidentiality","score_opus":0.04009827283520916,"score_gpt":0.32584947432182704,"score_spread":0.28575120148661787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7035138434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2810165,0.0034470325,0.23080812,0.21104589,0.008826155,0.002510512,0.00059228035,0.001675449,0.26007804],"genre_scores_gemma":[0.7893359,0.00013119841,0.006228929,0.00043297623,0.00013809446,0.0000053793533,0.00004867945,0.000013912778,0.20366493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956837,0.0002802532,0.0006162534,0.0011441307,0.001585526,0.00069012487],"domain_scores_gemma":[0.9963782,0.00039882524,0.00040143612,0.0011124709,0.0015260176,0.00018308831],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0012230148,0.0004245433,0.0005381651,0.00071533193,0.0013216683,0.0005860241,0.004724418,0.00023758535,0.00042980787],"category_scores_gemma":[0.00044373496,0.00034302875,0.00028498823,0.0023211879,0.0016671369,0.0016847679,0.001743615,0.00045067564,0.0001640844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010627086,0.0002639624,0.00082950504,0.00012447705,0.000092236514,0.00003377273,0.0019509372,0.00012146913,0.00096928986,0.97441155,0.016405178,0.0046913377],"study_design_scores_gemma":[0.001571987,0.0004211615,0.014932055,0.00023731696,0.00010368861,0.000069102854,0.005158973,0.005745259,0.005202449,0.18647043,0.779051,0.0010365413],"about_ca_topic_score_codex":0.00020298903,"about_ca_topic_score_gemma":0.00021767271,"teacher_disagreement_score":0.78794116,"about_ca_system_score_codex":0.00022447864,"about_ca_system_score_gemma":0.0011696408,"threshold_uncertainty_score":0.9999785},"labels":[],"label_agreement":null},{"id":"W7065946097","doi":"","title":"Faith-based arbitration in Ontario: promoting the option to exit and building fraternity","year":2014,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arbitration; Enforcement; Legislature; Duty; Argument (complex analysis); Safeguarding; Compulsory arbitration; Voluntariness; Subject (documents)","score_opus":0.014155165541775226,"score_gpt":0.23336978100054218,"score_spread":0.21921461545876694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7065946097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98955333,0.00004574621,0.0012116173,0.00012561335,0.00058582163,0.00083977316,0.000019904366,0.00016927916,0.0074489005],"genre_scores_gemma":[0.9850441,0.000011750726,0.0135988705,0.0006648827,0.000026259137,0.00017816435,0.00006986356,0.000049442795,0.00035662946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99655026,0.00028227048,0.00073190685,0.0011951906,0.00063821377,0.00060217665],"domain_scores_gemma":[0.99810356,0.00016764918,0.00048979785,0.00073881185,0.000260517,0.0002396648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013161809,0.0005555004,0.00047013516,0.00055724505,0.00090392475,0.0006710177,0.0012254713,0.00043705798,0.000023519478],"category_scores_gemma":[0.00034892964,0.00047727572,0.00010765681,0.0009302035,0.000031129173,0.0011488004,0.00017904051,0.0014938507,0.000044267148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015979764,0.0002937306,0.0014497419,0.0009954391,0.00006711226,0.00006885522,0.0003227413,0.00053184043,0.05260425,0.59161896,0.000004251856,0.3518833],"study_design_scores_gemma":[0.0032406196,0.0015167592,0.1937639,0.0068834815,0.00021959662,0.00008973723,0.00033686095,0.029959083,0.33167028,0.39937162,0.027470943,0.0054771164],"about_ca_topic_score_codex":0.009285402,"about_ca_topic_score_gemma":0.15792648,"teacher_disagreement_score":0.3464062,"about_ca_system_score_codex":0.00040860812,"about_ca_system_score_gemma":0.0001632724,"threshold_uncertainty_score":0.9997679},"labels":[],"label_agreement":null},{"id":"W7066546783","doi":"","title":"Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models","year":2023,"lang":"en","type":"article","venue":"Aaltodoc (Aalto University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Finnish Center for Artificial Intelligence; China Scholarship Council","keywords":"Hyperparameter; Inference; Marginal likelihood; Expectation propagation; Gaussian process; Laplace's method; Range (aeronautics); Focus (optics)","score_opus":0.02372406008414829,"score_gpt":0.23739643701885013,"score_spread":0.21367237693470184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066546783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119946904,0.000031657484,0.8593151,0.0007736399,0.00016595003,0.00028243478,0.000002744667,0.00090442086,0.01857717],"genre_scores_gemma":[0.99210554,0.00006486029,0.005434005,0.000126439,0.000022442206,0.0000041006774,0.0000061206297,0.00002103832,0.00221543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774486,0.00010064756,0.00025614933,0.00079125556,0.00033986015,0.00076724624],"domain_scores_gemma":[0.9988772,0.00017012261,0.0001634962,0.0004808269,0.00011306146,0.00019528037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028059699,0.00027259032,0.00029286274,0.00093742675,0.000274941,0.0002512971,0.0014418855,0.00014654509,0.000020880083],"category_scores_gemma":[0.00007891926,0.00028608175,0.0000760957,0.0034825506,0.000085758365,0.002229289,0.00057243276,0.00048780465,0.00015711674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000561066,0.00018735959,0.0073154136,0.0004895271,0.00004605456,0.0008475035,0.0052598636,0.12052156,0.000847228,0.81347525,0.00008152946,0.05087263],"study_design_scores_gemma":[0.00081213,0.00012836193,0.002106645,0.0001510824,0.000013027288,0.000013796413,0.0013827431,0.94854975,0.0004962024,0.044903338,0.00076206395,0.00068085256],"about_ca_topic_score_codex":0.00017574939,"about_ca_topic_score_gemma":0.00007502374,"teacher_disagreement_score":0.87215865,"about_ca_system_score_codex":0.00012617206,"about_ca_system_score_gemma":0.00030660187,"threshold_uncertainty_score":0.9999591},"labels":[],"label_agreement":null},{"id":"W7084135053","doi":"10.1109/infocomwkshps65812.2025","title":"IEEE INFOCOM 2025 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","year":2025,"lang":"en","type":"paratext","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Communications system; Bandwidth (computing); Focus (optics); Data compression; Transmission (telecommunications); Channel (broadcasting); Entropy (arrow of time); Image compression","score_opus":0.04551263992975755,"score_gpt":0.3086906982136493,"score_spread":0.26317805828389174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084135053","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000033573458,0.0002858349,0.5776912,0.0049028923,0.0038664676,0.0005696314,0.0000662014,0.00025183108,0.41236258],"genre_scores_gemma":[0.015442763,0.0049540293,0.1941464,0.012547293,0.0009930027,0.0005130367,0.00038992742,0.00008477807,0.7709288],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951968,0.00028141597,0.0012270203,0.0015139156,0.00077958114,0.0010012556],"domain_scores_gemma":[0.99126905,0.00076416595,0.0006278459,0.006294986,0.00068467227,0.00035928836],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00034404427,0.001076031,0.0011855632,0.0007226597,0.00066457683,0.002047984,0.012123486,0.0009291189,0.00194646],"category_scores_gemma":[0.000025258745,0.0009673097,0.00036258026,0.0015847703,0.00037505315,0.00077192514,0.0020295358,0.0021099092,0.018649623],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000942956,0.00020025171,0.0000051926268,0.00022123252,0.0001076599,0.000008204978,0.00020310053,0.00030311997,0.0000075772905,0.13845542,0.801715,0.058763772],"study_design_scores_gemma":[0.0009206915,0.00033193573,0.00010899524,0.0029098166,0.00006907378,0.00002116716,0.000034443976,0.12879844,0.00044501733,0.006831313,0.8573914,0.0021376824],"about_ca_topic_score_codex":0.00015840489,"about_ca_topic_score_gemma":0.00008580332,"teacher_disagreement_score":0.3835448,"about_ca_system_score_codex":0.00020083867,"about_ca_system_score_gemma":0.0018992282,"threshold_uncertainty_score":0.9992777},"labels":[],"label_agreement":null},{"id":"W7100288013","doi":"","title":"Christopher Pal Ecole Polytechnique de Montreal","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Transfer of learning; Domain (mathematical analysis); Task (project management); Probabilistic logic; Feature learning; Action (physics); Graphical model; Domain adaptation","score_opus":0.005744867129431609,"score_gpt":0.21278097262031673,"score_spread":0.2070361054908851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100288013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028818857,0.000019678175,0.9458804,0.0031404928,0.000052596366,0.000050871895,8.8421274e-7,0.0002790093,0.047694188],"genre_scores_gemma":[0.90419537,0.00001589191,0.09021987,0.00054972165,0.00004207153,0.000015280693,7.945882e-8,0.0000047431895,0.004956942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992737,0.000015663572,0.0001072673,0.00023268348,0.00010177777,0.00026887903],"domain_scores_gemma":[0.99944896,0.000030611467,0.000033147873,0.00035517995,0.00003072486,0.00010140116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000093389215,0.00008569118,0.00007756324,0.000035753877,0.000050850485,0.00007325105,0.00061736116,0.000054864413,0.00021634638],"category_scores_gemma":[0.000014875551,0.000049646103,0.00003489379,0.00012788543,0.000028120256,0.00030467298,0.00012712288,0.000041245312,0.00016174326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005116861,0.00007000268,0.0064514973,0.000010897781,0.000007639732,0.000041570762,0.00014968307,9.349451e-7,0.007908126,0.5220466,0.012264435,0.45104346],"study_design_scores_gemma":[0.0015422472,0.0005043256,0.2500796,0.00016280226,0.0000103460925,0.00026095967,0.000028172835,0.011217911,0.14862187,0.5526204,0.033681773,0.0012695948],"about_ca_topic_score_codex":0.00015410209,"about_ca_topic_score_gemma":0.000087545115,"teacher_disagreement_score":0.9013135,"about_ca_system_score_codex":0.00003535118,"about_ca_system_score_gemma":0.00010539975,"threshold_uncertainty_score":0.23688412},"labels":[],"label_agreement":null},{"id":"W7106714045","doi":"10.71781/32597","title":"Correction, guidée par les données, de distributions a priori biaisées en haute dimension pour l’astrophysique","year":2025,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut Périmètre de physique théorique; Université de Montréal; Flatiron Health; McGill University","keywords":"Distribution (mathematics); A priori and a posteriori; Maximum likelihood; ESPACE","score_opus":0.020462991351714012,"score_gpt":0.2871335389951319,"score_spread":0.26667054764341785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106714045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09651286,0.000461761,0.822336,0.0030283418,0.0040896805,0.0015988501,0.00025008226,0.00007461633,0.07164777],"genre_scores_gemma":[0.6605436,0.000312147,0.25413388,0.000082473474,0.00032926648,0.00021438279,0.0018722076,0.000043937565,0.08246808],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99805504,0.00011304409,0.00042076598,0.0007752395,0.00023518682,0.00040074944],"domain_scores_gemma":[0.99848497,0.00012566763,0.00031721327,0.0006215994,0.00031956978,0.00013096322],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00023396456,0.0003489547,0.0003897026,0.00015019679,0.000534287,0.001046978,0.0019593718,0.0003207462,0.00016787066],"category_scores_gemma":[0.00013038474,0.00032994951,0.00011623703,0.0005008659,0.000028895514,0.00053295644,0.00038157337,0.00043850602,0.00017616231],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065048494,0.00057927874,0.00092889764,0.00023266974,0.00016925498,0.00008508472,0.0026976552,0.000118287186,0.0062503763,0.01708584,0.036317572,0.93547004],"study_design_scores_gemma":[0.0029439656,0.0010418565,0.16636527,0.010743219,0.00079956086,0.00023622344,0.0027150866,0.030689888,0.28353265,0.029759455,0.4659484,0.005224449],"about_ca_topic_score_codex":0.00061873347,"about_ca_topic_score_gemma":0.0010933846,"teacher_disagreement_score":0.9302456,"about_ca_system_score_codex":0.00016164655,"about_ca_system_score_gemma":0.0017575887,"threshold_uncertainty_score":0.99999005},"labels":[],"label_agreement":null},{"id":"W7124174845","doi":"10.65109/gpbo1557","title":"Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics","year":2014,"lang":"","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traverse; Variety (cybernetics); Graph; Gaussian process; Enhanced Data Rates for GSM Evolution; Path (computing); Tree traversal; Observable","score_opus":0.004650635148509053,"score_gpt":0.1765239978522729,"score_spread":0.17187336270376385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124174845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008137473,0.0005195775,0.88845456,0.01159163,0.0021888458,0.0011963693,0.000044035878,0.00038466384,0.08748286],"genre_scores_gemma":[0.9781373,0.00007221,0.01507199,0.0013212347,0.00011885492,0.000027681688,0.000042313386,0.000066695415,0.0051416825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99440247,0.00014885614,0.00085742935,0.0016695423,0.00085208024,0.002069641],"domain_scores_gemma":[0.99592763,0.00010662545,0.00035061047,0.0013888313,0.000651103,0.0015751834],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005302053,0.0009066379,0.0007367264,0.0004382284,0.0007576201,0.0015191154,0.0024630842,0.00044272834,0.00036512068],"category_scores_gemma":[0.0000671956,0.0007050027,0.00014301589,0.001959068,0.00036903206,0.0011994079,0.00022907155,0.0011078309,0.00028854606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005228599,0.0003181295,0.011821427,0.0003140023,0.00026661175,0.0002896372,0.0027809804,0.002389821,0.000048122365,0.66623014,0.0021631434,0.3133257],"study_design_scores_gemma":[0.0008146255,0.00088069175,0.011810941,0.0004908002,0.00007369244,0.0004297131,0.00010255257,0.9780285,0.000054855343,0.002539091,0.003478634,0.0012959376],"about_ca_topic_score_codex":0.073679835,"about_ca_topic_score_gemma":0.5186712,"teacher_disagreement_score":0.9756386,"about_ca_system_score_codex":0.00051019475,"about_ca_system_score_gemma":0.0025862718,"threshold_uncertainty_score":0.9995401},"labels":[],"label_agreement":null},{"id":"W7125956533","doi":"10.1145/3757377.3794993","title":"10.1145/3757377.3794993","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Representation (politics); Gaussian; Component (thermodynamics)","score_opus":0.004478300184377838,"score_gpt":0.16966469506642803,"score_spread":0.1651863948820502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125956533","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009657441,0.000038246933,0.0036124182,0.0014093673,0.0000041699477,0.000097573364,0.0000026445327,0.00026194434,0.99447703],"genre_scores_gemma":[0.0011192674,4.4684168e-7,0.0074532093,0.00027462348,0.0000690609,0.000012315738,0.0000015926471,0.000009668849,0.99105984],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892414,0.000020987047,0.00016214178,0.00035404306,0.00020935954,0.00032934372],"domain_scores_gemma":[0.99918425,0.000026043885,0.000027610293,0.00053305546,0.000046215617,0.00018284714],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009882029,0.00013713984,0.00013639311,0.00006768537,0.000096450894,0.00021160983,0.001011636,0.00004769807,0.9515778],"category_scores_gemma":[0.000014641827,0.00012451998,0.000044720204,0.0004651547,0.000022518048,0.00034647706,0.000118127944,0.00008504947,0.9731182],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064793767,0.000029620669,2.3011208e-7,0.0000054939596,0.0000044239405,0.00001134334,0.00002754801,0.000033594035,0.000025106878,0.00032115023,0.07031001,0.929225],"study_design_scores_gemma":[0.000101414764,0.000112609196,0.0000746143,0.00001607311,0.0000027887975,0.000019977322,2.0936045e-7,0.00403965,0.00013739022,0.0003527083,0.99495345,0.00018911237],"about_ca_topic_score_codex":0.000010098794,"about_ca_topic_score_gemma":8.5578584e-8,"teacher_disagreement_score":0.9290359,"about_ca_system_score_codex":0.000018157694,"about_ca_system_score_gemma":0.000058102945,"threshold_uncertainty_score":0.50777775},"labels":[],"label_agreement":null},{"id":"W7132900222","doi":"","title":"Efficient implementation of Gaussian process priors within flexible Bayesian hierarchical models","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prior probability; Gaussian process; Bayesian probability; Inference; Smoothing; Bayesian inference; Flexibility (engineering); Gaussian; Function (biology)","score_opus":0.021152231540729635,"score_gpt":0.3585718235552623,"score_spread":0.33741959201453264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132900222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2796271,0.0017277793,0.69627666,0.002222339,0.002953674,0.0023494244,0.000060281356,0.00047682086,0.014305881],"genre_scores_gemma":[0.9812923,0.00008831914,0.013800507,0.00008546786,0.00018689434,0.00018583257,0.00016442784,0.00014099055,0.0040552705],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919922,0.00020379461,0.0020995168,0.002350251,0.0020654998,0.0012887481],"domain_scores_gemma":[0.9956745,0.00010926557,0.0015569828,0.0013192896,0.00074664416,0.0005933089],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008719316,0.001221662,0.0012560495,0.0010672776,0.00045367633,0.0009184799,0.0023295642,0.00068792456,0.00045287245],"category_scores_gemma":[0.000046948036,0.0011535928,0.00044941084,0.003154794,0.00028259208,0.00052197836,0.00035499802,0.0014443764,0.00012747271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002854286,0.00069645623,0.0001953982,0.014016427,0.00041871154,0.00012514263,0.5013227,0.06667174,0.0019893236,0.38200325,0.0002574871,0.032017954],"study_design_scores_gemma":[0.00085645355,0.0007848733,0.00064232666,0.0031175911,0.00035984183,0.00005504776,0.05047891,0.85549915,0.032560933,0.054036442,0.000050284278,0.0015581729],"about_ca_topic_score_codex":0.0006101861,"about_ca_topic_score_gemma":0.00042819313,"teacher_disagreement_score":0.78882736,"about_ca_system_score_codex":0.00023232854,"about_ca_system_score_gemma":0.0041544735,"threshold_uncertainty_score":0.9990914},"labels":[],"label_agreement":null},{"id":"W7132974855","doi":"","title":"Machine Learning Perspectives in Compression, Distributed Computing, and Brain Imaging","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Exploit; Coding (social sciences); Gaussian process; Probabilistic logic; Inference; Process (computing); Subnetwork; Neuroimaging","score_opus":0.011267742902285435,"score_gpt":0.3202973159932543,"score_spread":0.30902957309096885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132974855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2120133,0.12649393,0.6234042,0.023460303,0.0024405324,0.0013985885,0.000055238434,0.0009620872,0.009771822],"genre_scores_gemma":[0.9918431,0.00085444085,0.0026072536,0.00009509685,0.00012444622,0.000011183712,0.00022928337,0.00006412286,0.004171087],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587303,0.00029327624,0.00068449747,0.0018018031,0.00052559323,0.00082178216],"domain_scores_gemma":[0.9981959,0.00035557486,0.00047094136,0.00046295053,0.0002550494,0.00025960454],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005815282,0.00077622203,0.0007661503,0.000557987,0.0005172554,0.0015900531,0.0009070627,0.00025439414,0.00013023455],"category_scores_gemma":[0.00032978336,0.00076125376,0.00013026553,0.0014627295,0.00019554186,0.0004625804,0.00066182815,0.0020327393,0.000059284608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019465726,0.00055057736,0.031908453,0.0050844136,0.00019733811,0.001218695,0.7206066,0.0024057624,0.002404032,0.08694698,0.0024192566,0.14606322],"study_design_scores_gemma":[0.0007211948,0.00012236103,0.022091988,0.00431449,0.000051588097,0.00010594077,0.0448911,0.91893613,0.00019637219,0.004051876,0.003429158,0.0010877992],"about_ca_topic_score_codex":0.0010461118,"about_ca_topic_score_gemma":0.00021856067,"teacher_disagreement_score":0.9165304,"about_ca_system_score_codex":0.00016594556,"about_ca_system_score_gemma":0.00033850153,"threshold_uncertainty_score":0.9994838},"labels":[],"label_agreement":null},{"id":"W7133084412","doi":"","title":"Advances in Scalable Bayesian Inference: Gaussian Processes &amp; Discrete Variable Models","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Kenneth M. Molson Foundation; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Molson Foundation","keywords":"Inference; Gaussian process; Approximate inference; Kernel (algebra); Bayesian inference; Bayesian probability; Fiducial inference; Computational complexity theory","score_opus":0.019570412363365756,"score_gpt":0.33198418513771133,"score_spread":0.31241377277434557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133084412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010321343,0.018619716,0.80860054,0.0010183149,0.0019382727,0.0015940166,0.00007246311,0.00036470397,0.16675983],"genre_scores_gemma":[0.8423751,0.024329664,0.06980321,0.00041162592,0.0003417559,0.0016193901,0.0011863637,0.0003048415,0.05962807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894493,0.00044667747,0.0019203592,0.003479655,0.0021878544,0.0025161165],"domain_scores_gemma":[0.99405843,0.0005453077,0.0016330673,0.0022705276,0.000771363,0.00072131836],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0010625303,0.0018063058,0.0018750336,0.0012331209,0.0013842245,0.0019620513,0.0049773264,0.0008186905,0.0057378113],"category_scores_gemma":[0.00065449654,0.0018706961,0.0002575017,0.008148437,0.00026383385,0.006325949,0.0010623717,0.0025696275,0.00018573395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011130591,0.0027074832,0.0038112893,0.030061278,0.00025248254,0.00039793417,0.16157387,0.17560528,0.00044834157,0.5426624,0.0015768475,0.079789765],"study_design_scores_gemma":[0.0031692823,0.0012191993,0.0006449489,0.008396643,0.00031586466,0.00012401374,0.022248778,0.33911672,0.00080562447,0.5193291,0.0965898,0.008040039],"about_ca_topic_score_codex":0.0018685684,"about_ca_topic_score_gemma":0.0046629137,"teacher_disagreement_score":0.8413429,"about_ca_system_score_codex":0.0005443417,"about_ca_system_score_gemma":0.006639641,"threshold_uncertainty_score":0.99991584},"labels":[],"label_agreement":null},{"id":"W7135090694","doi":"10.1109/camsap66162.2025.11423976","title":"Bayesian Koopman Time Series Forecasting","year":2025,"lang":"","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Operator (biology); Bayesian probability; Bayesian inference; Nonlinear system; Sampling (signal processing); Set (abstract data type); Series (stratigraphy); Dynamical systems theory; Linear map","score_opus":0.012585239373307466,"score_gpt":0.2303950061959236,"score_spread":0.21780976682261613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135090694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015984106,0.00037692243,0.69015515,0.007945403,0.00077373785,0.00022937071,0.00000388974,0.00023569819,0.30012],"genre_scores_gemma":[0.71051115,0.00006897723,0.10007367,0.0017778116,0.00017166323,0.000022609152,0.0000032959208,0.00002355656,0.18734728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965134,0.00009447058,0.00081335864,0.0011569816,0.00038841303,0.0010333423],"domain_scores_gemma":[0.9979901,0.00012468894,0.00022437201,0.001111627,0.0002984199,0.00025082994],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041987796,0.0005293907,0.00053314125,0.00035988286,0.0007597643,0.0017454068,0.0020899305,0.00024726836,0.0018192112],"category_scores_gemma":[0.00015038486,0.00049316586,0.00019143245,0.0021855654,0.00025548215,0.0019056998,0.0011898865,0.00039123584,0.000695457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030476109,0.0001568413,0.0012737206,0.00053002744,0.00011123646,0.000098555385,0.0007281342,0.000039436043,0.00021384956,0.5947989,0.015515679,0.38650313],"study_design_scores_gemma":[0.0006756328,0.00038603196,0.0013185694,0.001184197,0.000080275895,0.00013582277,0.00012780048,0.83626586,0.005602184,0.13070536,0.022360317,0.0011579499],"about_ca_topic_score_codex":0.000045180852,"about_ca_topic_score_gemma":0.00003294238,"teacher_disagreement_score":0.8362264,"about_ca_system_score_codex":0.0000860097,"about_ca_system_score_gemma":0.0009023084,"threshold_uncertainty_score":0.999752},"labels":[],"label_agreement":null},{"id":"W7136382334","doi":"10.1109/itsc60802.2025.11423545","title":"Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling","year":2025,"lang":"","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Pedestrian; Stress (linguistics); Quantum; Robot; Support vector machine","score_opus":0.029299117834672102,"score_gpt":0.27830388408017026,"score_spread":0.24900476624549817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7136382334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48161915,0.0004326581,0.51676536,0.00018605872,0.00019219957,0.00033949787,0.0000054219763,0.000035535086,0.00042409985],"genre_scores_gemma":[0.9963151,0.0001234349,0.0030045223,0.00002597135,0.000014541533,0.000019042738,0.0000029635348,0.000010471029,0.0004839482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969536,0.00018547011,0.0012130362,0.0008080346,0.00035373494,0.00048609637],"domain_scores_gemma":[0.9987251,0.00014583673,0.00030500008,0.00050787086,0.00020056668,0.00011561662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004577678,0.00033099714,0.00055539946,0.00052623905,0.00024241407,0.00023246698,0.0007216751,0.00012438644,0.00007257471],"category_scores_gemma":[0.000019000838,0.0003217043,0.00008730098,0.0022421677,0.000058837555,0.00056759623,0.00008173689,0.00058567396,0.0000024304115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001212829,0.004642849,0.2980898,0.0016915352,0.00013437534,0.00889937,0.053271025,0.5359288,0.000014735409,0.044914953,0.0000069863613,0.05228432],"study_design_scores_gemma":[0.001759069,0.000657043,0.0015155857,0.0004354745,0.000055071276,0.000050718027,0.01482922,0.9783438,0.00010213083,0.0019515167,0.000016751743,0.00028360658],"about_ca_topic_score_codex":0.02258829,"about_ca_topic_score_gemma":0.027554087,"teacher_disagreement_score":0.51469594,"about_ca_system_score_codex":0.000040997664,"about_ca_system_score_gemma":0.00055190915,"threshold_uncertainty_score":0.9999235},"labels":[],"label_agreement":null},{"id":"W759726671","doi":"","title":"Piecewise bounds for estimating bernoulli-logistic latent Gaussian models","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Piecewise; Mathematics; Minimax; Applied mathematics; Upper and lower bounds; Quadratic equation; Bernoulli's principle; Gaussian; Mathematical optimization","score_opus":0.1019456910048756,"score_gpt":0.27647351257715397,"score_spread":0.17452782157227836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W759726671","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030511,0.000041608437,0.95663804,0.00042214405,0.00028312602,0.00021793197,0.0000035569972,0.00027768465,0.041810784],"genre_scores_gemma":[0.48743707,0.000002188259,0.5117814,0.0001969324,0.000029249328,0.00003981229,0.0000014418387,0.0000080191585,0.0005038991],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985206,0.000014348779,0.0003089996,0.0004891259,0.00019035483,0.0004765485],"domain_scores_gemma":[0.99902225,0.000052261963,0.00011696996,0.0005282592,0.000109840825,0.00017041023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021406503,0.00019277386,0.0001942304,0.000082166036,0.00022344157,0.00026512347,0.0010210982,0.000074313444,0.000067985435],"category_scores_gemma":[0.00004239372,0.00015195085,0.000082165745,0.00023907138,0.000064141575,0.000819277,0.0002026503,0.00009461674,0.000052028354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058343053,0.000081786224,0.00012142496,0.00006631381,0.000013147598,0.000009317765,0.00084523053,0.00029743582,0.000031056985,0.97018063,0.00046340187,0.027884448],"study_design_scores_gemma":[0.0001731186,0.000108899054,0.00024869596,0.0000267536,0.000007725826,0.000015116604,0.000014672385,0.6427277,0.0002872838,0.35609746,0.000102553895,0.00019005423],"about_ca_topic_score_codex":0.00009474816,"about_ca_topic_score_gemma":0.000016710774,"teacher_disagreement_score":0.64243025,"about_ca_system_score_codex":0.000029444569,"about_ca_system_score_gemma":0.00011857746,"threshold_uncertainty_score":0.6196376},"labels":[],"label_agreement":null}]}