{"meta":{"query_hash":"5648e54e999c","filters":{"venue":"Uncertainty in Artificial Intelligence"},"cohort_total":35,"direct_labels_cover":0,"predictions_cover":35,"exported":35,"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/5648e54e999c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Uncertainty+in+Artificial+Intelligence"},"results":[{"id":"W2161202741","doi":"10.5555/1036843.1036873","title":"From fields to trees","year":2004,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":45,"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":"Gibbs sampling; Markov chain Monte Carlo; Graphical model; Tree (set theory); Sampling (signal processing); Computer science; Focus (optics); Markov chain; Importance sampling; Posterior probability; Algorithm; Mathematics; Belief propagation; Statistics; Artificial intelligence; Bayesian probability; Monte Carlo method; Machine learning; Combinatorics","score_opus":0.04739542792282484,"score_gpt":0.3219355585509258,"score_spread":0.27454013062810095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161202741","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":[0.02321407,0.00005140634,0.9681598,0.006199467,0.00061251776,0.00018950923,0.0000042038623,0.00009956339,0.0014694381],"genre_scores_gemma":[0.68355876,0.0000077446675,0.3148934,0.0013154785,0.00015549504,0.000019054993,0.0000014397219,0.000005988549,0.00004262269],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99836385,0.00007507892,0.00040080526,0.000549086,0.00022843397,0.00038272474],"domain_scores_gemma":[0.9989518,0.00016664286,0.000046453286,0.00061542675,0.000058101105,0.00016160327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037113132,0.00016981101,0.00021111949,0.00016761005,0.000070090544,0.00014906221,0.0010409734,0.00011305069,0.00005402944],"category_scores_gemma":[0.00018116295,0.00015785938,0.000071280774,0.0007039856,0.00004564588,0.00021873742,0.00018852162,0.00022350412,0.000271456],"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":[0.000014134363,0.000071658644,0.000009125511,0.0000012705898,0.0000035709154,0.000029909766,0.0044996347,0.042433176,0.0010638203,0.49234366,0.000059702095,0.45947033],"study_design_scores_gemma":[0.000031346113,0.00008257264,0.00006229851,0.000045368106,0.0000018373273,0.0000019104332,0.00012716465,0.030478273,0.03197251,0.9363816,0.0005910659,0.00022405977],"about_ca_topic_score_codex":0.00386142,"about_ca_topic_score_gemma":0.004089796,"teacher_disagreement_score":0.6603447,"about_ca_system_score_codex":0.00010649533,"about_ca_system_score_gemma":0.000094543684,"threshold_uncertainty_score":0.6437319},"labels":[],"label_agreement":null},{"id":"W2398850217","doi":"","title":"Off-policy learning based on weighted importance sampling with linear computational complexity","year":2015,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Machine Learning and Algorithms","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 Alberta","funders":"","keywords":"Component (thermodynamics); Computational complexity theory; Computer science; Sampling (signal processing); Measure (data warehouse); Stochastic gradient descent; Importance sampling; Algorithm; Function (biology); Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Data mining; Artificial neural network; Statistics; Monte Carlo method","score_opus":0.09631687283916708,"score_gpt":0.3443424134911935,"score_spread":0.24802554065202642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398850217","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":[0.031075152,0.000026096297,0.9631496,0.0035783998,0.00018393126,0.0002105229,0.000004229126,0.00028709118,0.0014849413],"genre_scores_gemma":[0.8723392,0.0000020940984,0.1265895,0.0007171653,0.00022799442,0.000016079917,0.00003545838,0.000017924762,0.0000545711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973401,0.00025818998,0.00053419586,0.00065984705,0.00069767924,0.0005100168],"domain_scores_gemma":[0.9983453,0.00048293095,0.00021337703,0.00042603997,0.00029492428,0.0002373943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009793651,0.0002772136,0.00029904,0.0003877211,0.00022841743,0.00017669614,0.0007577104,0.00008302501,0.000034033157],"category_scores_gemma":[0.000579313,0.00024063016,0.0000614802,0.0014295962,0.00021404374,0.00020700344,0.00010787175,0.00071504497,0.00013355825],"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":[0.00008046426,0.00012844827,0.0019979542,0.0000059924632,0.000004750284,0.00002566432,0.000670577,0.85879475,0.0000050490307,0.08123843,0.000014866859,0.057033055],"study_design_scores_gemma":[0.00012767785,0.00037340223,0.00040085285,0.00007288471,0.0000020546051,0.0000076151578,0.0001707732,0.9410805,0.00014551404,0.056234118,0.0010901613,0.00029441394],"about_ca_topic_score_codex":0.0008314554,"about_ca_topic_score_gemma":0.0002953709,"teacher_disagreement_score":0.84126407,"about_ca_system_score_codex":0.00024629652,"about_ca_system_score_gemma":0.0005434809,"threshold_uncertainty_score":0.9812614},"labels":[],"label_agreement":null},{"id":"W2401533533","doi":"","title":"Off-policy TD(λ) with a true online equivalence","year":2014,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Online algorithm; Computer science; Equivalence (formal languages); Generalization; TRACE (psycholinguistics); Function approximation; Online learning; Algorithm; Function (biology); Artificial intelligence; Machine learning; Mathematics; Discrete mathematics; Artificial neural network","score_opus":0.02643517356787296,"score_gpt":0.26605371461280786,"score_spread":0.2396185410449349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401533533","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":[0.65032315,0.00075019576,0.33200988,0.0019343431,0.0010591823,0.0011769391,0.00007149895,0.0009840634,0.011690749],"genre_scores_gemma":[0.9985037,0.00006619768,0.00052510865,0.0002158398,0.00050308974,0.00006536404,0.000012174135,0.000038523536,0.00007003554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977524,0.00011685483,0.00069867005,0.00042276067,0.00035921906,0.0006500593],"domain_scores_gemma":[0.9985019,0.00058035983,0.000068144676,0.0005782662,0.00011263925,0.00015869197],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005592987,0.00032499994,0.00043942605,0.00027458844,0.00007780109,0.00007958747,0.00049510045,0.00014305164,0.00011577148],"category_scores_gemma":[0.0005786722,0.00028615663,0.0000866989,0.0008476314,0.00022811563,0.00017452193,0.00003878966,0.00036470577,0.00015880816],"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":[0.000113793256,0.00009877271,0.00035030453,0.00006802733,0.000018574801,0.000008336516,0.0009478783,0.7441798,0.00093881506,0.02680293,0.0000265217,0.22644629],"study_design_scores_gemma":[0.00013307414,0.00024482163,0.00028851305,0.00021345956,0.000011762153,0.000009164648,0.0012673005,0.9702102,0.0023612268,0.019231398,0.005526056,0.00050304725],"about_ca_topic_score_codex":0.0011364206,"about_ca_topic_score_gemma":0.006401729,"teacher_disagreement_score":0.34818053,"about_ca_system_score_codex":0.00027377403,"about_ca_system_score_gemma":0.000093281116,"threshold_uncertainty_score":0.99995905},"labels":[],"label_agreement":null},{"id":"W2401728283","doi":"","title":"State sequence analysis in hidden Markov models","year":2015,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Ottawa Hospital","funders":"","keywords":"Hidden Markov model; Viterbi algorithm; Sequence (biology); Inference; Forward algorithm; Computer science; State (computer science); Markov chain; Markov model; Hidden semi-Markov model; Sequence labeling; Artificial intelligence; Algorithm; Machine learning; Markov property; Variable-order Markov model","score_opus":0.16143884102844683,"score_gpt":0.3358911258933971,"score_spread":0.17445228486495026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401728283","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":[0.16812515,0.00009904057,0.8276244,0.00089348137,0.000444124,0.00043768215,0.000018409939,0.00016340385,0.002194311],"genre_scores_gemma":[0.99439603,0.000018250503,0.0051080566,0.00020865652,0.00004090423,0.00007252171,0.000009379162,0.000011908967,0.00013431396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963802,0.00046060092,0.0009976295,0.00087104045,0.0006617194,0.0006288056],"domain_scores_gemma":[0.9978887,0.00043501472,0.0002180255,0.0008611952,0.00034513907,0.00025194365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020316807,0.0002769792,0.0005361511,0.0011579939,0.000054505428,0.00030426178,0.001346501,0.00011589038,0.000036361307],"category_scores_gemma":[0.0003630797,0.00028952403,0.00014810974,0.0046881563,0.00012810109,0.0013255823,0.00031467588,0.00036797777,0.0002534621],"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":[0.00008123909,0.00025981647,0.003417168,0.0000123988475,0.00007090239,0.00019921831,0.009891306,0.36562017,0.00028153218,0.016937887,0.000066720684,0.60316163],"study_design_scores_gemma":[0.000057428475,0.00004694526,0.0001672291,0.00004060019,0.000010600589,0.0000072920193,0.00080554193,0.8543711,0.001247954,0.14285123,0.00007639819,0.0003177252],"about_ca_topic_score_codex":0.009547822,"about_ca_topic_score_gemma":0.02854497,"teacher_disagreement_score":0.8262709,"about_ca_system_score_codex":0.0005958209,"about_ca_system_score_gemma":0.00035733957,"threshold_uncertainty_score":0.9999557},"labels":[],"label_agreement":null},{"id":"W2402930259","doi":"","title":"Intelligent affect: rational decision making for socially aligned agents","year":2015,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Affect (linguistics); Computer science; Probabilistic logic; Planner; Deception; Action (physics); Encoding (memory); Cognition; Monte Carlo tree search; Function (biology); Range (aeronautics); Artificial intelligence; Human–computer interaction; Monte Carlo method; Social psychology; Psychology; Mathematics; Engineering","score_opus":0.1292949818094062,"score_gpt":0.41023667229085736,"score_spread":0.2809416904814511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402930259","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":[0.17541565,0.0002218876,0.8011575,0.003930798,0.0028062465,0.0021086433,0.000035240802,0.00019328269,0.014130756],"genre_scores_gemma":[0.9945097,0.000037395796,0.0038646664,0.00041513075,0.0006458433,0.00013154006,0.000032820728,0.000014662581,0.0003482702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99754286,0.00041016928,0.0005847022,0.0003890973,0.00060555036,0.00046765254],"domain_scores_gemma":[0.9980585,0.0009761539,0.00013354355,0.00017495306,0.00049987505,0.00015699261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032856974,0.00017010045,0.00020746625,0.00017979516,0.0005831034,0.00015334971,0.0004009673,0.00017460652,0.00036320995],"category_scores_gemma":[0.0031503397,0.0001808314,0.00010800223,0.00057977764,0.00031948002,0.00035552276,0.000054089785,0.00011100948,0.00021214805],"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":[0.00050511496,0.00016147087,0.00020981449,0.000004747006,0.000008603603,0.0000036697359,0.015194875,0.06460082,0.00009619294,0.8059589,0.0008631611,0.11239262],"study_design_scores_gemma":[0.00008736321,0.00016073942,0.000095747244,0.00010081581,0.00001187927,0.000001009518,0.016427498,0.0496811,0.00075994304,0.92206585,0.010298221,0.00030980358],"about_ca_topic_score_codex":0.0005297275,"about_ca_topic_score_gemma":0.01608373,"teacher_disagreement_score":0.819094,"about_ca_system_score_codex":0.0006682623,"about_ca_system_score_gemma":0.00092295796,"threshold_uncertainty_score":0.8975099},"labels":[],"label_agreement":null},{"id":"W2406813038","doi":"","title":"Learning and planning with timing information in Markov decision processes","year":2015,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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":"McGill University","funders":"","keywords":"Computer science; Markov decision process; Representation (politics); Artificial intelligence; Machine learning; Markov chain; Markov process; Set (abstract data type); State (computer science); Robot; Duration (music); Algorithm; Mathematics","score_opus":0.05173460047726749,"score_gpt":0.3098116824769045,"score_spread":0.258077081999637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406813038","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":[0.10940781,0.000050370443,0.8889863,0.00012520383,0.00012159586,0.00018277067,1.3539157e-7,0.00007260119,0.0010532015],"genre_scores_gemma":[0.9677154,0.000020715872,0.032123044,0.00007469608,0.0000216857,0.00001393381,0.000003606943,0.000005936438,0.000021007725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848247,0.000063480365,0.00048734288,0.00024973182,0.0003948413,0.00032213907],"domain_scores_gemma":[0.99897486,0.0003967999,0.00015302948,0.00016821835,0.0002135283,0.00009356726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008575333,0.00015042385,0.00016487144,0.00039162193,0.00007908745,0.0003292659,0.00036671959,0.000069037094,0.000004253252],"category_scores_gemma":[0.0018781758,0.0001337627,0.000010136366,0.0010183012,0.00006914234,0.0013847154,0.00017654337,0.0003398868,0.000030311314],"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":[0.000064154796,0.000010150669,0.0079688635,0.000020662595,0.0000015134314,0.000014319075,0.009325266,0.8806962,0.0000052000546,0.0015630256,0.0000069829675,0.100323655],"study_design_scores_gemma":[0.000084535335,0.0002094069,0.0002645901,0.0003148289,0.0000014145398,0.000010540587,0.0046955217,0.99036974,0.00036375716,0.0030242866,0.00046684375,0.00019456647],"about_ca_topic_score_codex":0.00016792207,"about_ca_topic_score_gemma":0.0001431985,"teacher_disagreement_score":0.85830754,"about_ca_system_score_codex":0.00013310387,"about_ca_system_score_gemma":0.0001864715,"threshold_uncertainty_score":0.54546845},"labels":[],"label_agreement":null},{"id":"W2572140284","doi":"","title":"On hyper-parameter estimation in empirical Bayes: a revisit of the MacKay algorithm","year":2016,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Bayesian Methods and Mixture Models","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 Ottawa","funders":"","keywords":"Bayes' theorem; Algorithm; Heuristic; Computer science; Gaussian; Estimation theory; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Bayesian probability","score_opus":0.05027605717255774,"score_gpt":0.33621117325410327,"score_spread":0.28593511608154554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572140284","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":[0.019763375,0.000030379804,0.97623396,0.003033797,0.0003167732,0.00028909292,0.0000042921274,0.000025125013,0.00030321485],"genre_scores_gemma":[0.7300455,0.00001779032,0.26925218,0.0005654122,0.000028104116,0.000023881528,3.1544297e-7,0.000008011084,0.000058835358],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99785095,0.0003799379,0.0006354963,0.0004759963,0.00033331072,0.000324281],"domain_scores_gemma":[0.9978941,0.0011074421,0.0001422851,0.0007238946,0.0000747625,0.000057506302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050989,0.00017120929,0.00026568433,0.00018853197,0.00004437579,0.000045329394,0.00094153406,0.00011501794,0.000039541756],"category_scores_gemma":[0.0009550345,0.00009700697,0.000104992614,0.00084189005,0.00015385155,0.00020033511,0.00016267039,0.00021581494,0.000047710953],"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":[0.000017581104,0.000092969116,0.00015289389,0.0000058748246,0.0000022717088,0.0000031777047,0.00058552914,0.0019688322,0.00047338803,0.13952558,0.000041041745,0.8571309],"study_design_scores_gemma":[0.00004184158,0.00007222609,0.00028074038,0.00023681611,0.000001937423,0.0000026417642,0.000012952516,0.43938422,0.018180415,0.5415896,0.00007127251,0.00012528643],"about_ca_topic_score_codex":0.00012040035,"about_ca_topic_score_gemma":0.00011612526,"teacher_disagreement_score":0.8570056,"about_ca_system_score_codex":0.00013653732,"about_ca_system_score_gemma":0.00008972989,"threshold_uncertainty_score":0.39558294},"labels":[],"label_agreement":null},{"id":"W2771250101","doi":"","title":"Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","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":"","keywords":"Computer science; Probabilistic logic; Programming language; Probabilistic argumentation; Theoretical computer science; Logic program; Probabilistic logic network; Description logic; Logic programming; Artificial intelligence; Multimodal logic; Autoepistemic logic","score_opus":0.1345770587479101,"score_gpt":0.33336050070344564,"score_spread":0.19878344195553554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771250101","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":[0.30927792,0.00045804918,0.6226623,0.01350199,0.005256677,0.005307405,0.00010012886,0.0019771988,0.041458342],"genre_scores_gemma":[0.99049276,0.000046634446,0.00812426,0.0005976542,0.0003086595,0.00019084959,0.000017127297,0.000025554893,0.00019652225],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959811,0.00028165363,0.00086007896,0.0012631847,0.00060156867,0.001012438],"domain_scores_gemma":[0.99585766,0.0002737207,0.0009120693,0.0021465367,0.0005344663,0.00027553597],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00087216945,0.00049574475,0.0006503666,0.0001873217,0.0009859616,0.0014181607,0.003964738,0.0002417439,0.00007970821],"category_scores_gemma":[0.0018582426,0.00042508365,0.00021020435,0.00034071133,0.00085116003,0.0005407892,0.00092530093,0.00046920413,0.00038363738],"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":[0.00016198847,0.0011113774,0.0047063637,0.00014380111,0.000033331948,0.00035929572,0.0038218393,0.004921155,0.0010090484,0.8212298,0.0008766261,0.16162534],"study_design_scores_gemma":[0.00023222985,0.00047331784,0.016255328,0.00073538936,0.00002893466,0.000035397323,0.0016937826,0.44057897,0.004515016,0.5302314,0.0037492067,0.001470997],"about_ca_topic_score_codex":0.0021145649,"about_ca_topic_score_gemma":0.009067516,"teacher_disagreement_score":0.6812148,"about_ca_system_score_codex":0.0002461276,"about_ca_system_score_gemma":0.00015757787,"threshold_uncertainty_score":0.9998201},"labels":[],"label_agreement":null},{"id":"W2772322972","doi":"","title":"Holographic Feature Representations of Deep Networks.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Human Pose and Action Recognition","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":"Holography; Computer science; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Computer vision; Computer graphics (images); Optics; Physics","score_opus":0.04953838544420267,"score_gpt":0.32935003013141245,"score_spread":0.27981164468720976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772322972","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":[0.047966707,0.00012002572,0.9412389,0.0021187721,0.0010873643,0.00027230952,0.0000035173812,0.00008613082,0.0071063037],"genre_scores_gemma":[0.9969295,0.00007071765,0.0027324443,0.00010352557,0.000080806945,0.000017203056,0.000005098928,0.000004539552,0.000056155586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890435,0.00006350613,0.0003171016,0.0003214586,0.00016800372,0.00022557226],"domain_scores_gemma":[0.99860483,0.00014953746,0.00024869043,0.00080381637,0.00013853963,0.00005456057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002704276,0.000104886014,0.00015443758,0.00019248537,0.00031048027,0.0002087441,0.0009751296,0.00009269966,0.000058725396],"category_scores_gemma":[0.00031394346,0.000102728656,0.00008573123,0.0002940524,0.00021637414,0.0004861385,0.0001440418,0.00021672178,0.000038146693],"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":[0.000023403654,0.00021147449,0.002670542,0.000011961017,0.000018928682,0.000038684993,0.0012420696,0.06285801,0.00076296733,0.253648,0.00020314901,0.6783108],"study_design_scores_gemma":[0.000054192322,0.00007768168,0.008934403,0.00006213823,0.0000076195256,0.000007726684,0.0004164084,0.7724527,0.010402956,0.20703547,0.00030326864,0.00024544253],"about_ca_topic_score_codex":0.00028612785,"about_ca_topic_score_gemma":0.0011044934,"teacher_disagreement_score":0.9489628,"about_ca_system_score_codex":0.000021481148,"about_ca_system_score_gemma":0.000024111197,"threshold_uncertainty_score":0.4189153},"labels":[],"label_agreement":null},{"id":"W2772489041","doi":"","title":"Triply Stochastic Gradients on Multiple Kernel Learning.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Kernel (algebra); Artificial intelligence; Multiple kernel learning; Kernel method; Mathematics; Support vector machine; Combinatorics","score_opus":0.04788244141461452,"score_gpt":0.31440473209314307,"score_spread":0.26652229067852856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772489041","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":[0.313288,0.0000437225,0.6747015,0.0032225407,0.002171392,0.0004519832,0.0000036533047,0.00038044187,0.0057367645],"genre_scores_gemma":[0.997956,0.0000071691074,0.00085482514,0.00018604253,0.00013869478,0.000025860334,0.0000032717624,0.00001499962,0.0008131372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977193,0.00015744986,0.00044445775,0.0006914313,0.00040543443,0.0005819205],"domain_scores_gemma":[0.99781895,0.0005430345,0.00026656673,0.0011288367,0.00008509016,0.00015750673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007584347,0.00024986235,0.0002669772,0.00022125774,0.00081206334,0.00056424603,0.0020954397,0.00010514536,0.000042105898],"category_scores_gemma":[0.0042008935,0.00023425561,0.00010279643,0.00022198567,0.00018107283,0.0003183968,0.00033303734,0.0005637891,0.0007578512],"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":[0.000084799765,0.0002464607,0.0021660975,0.0000073612705,0.000008590189,0.00004672413,0.0015334984,0.48717344,0.00008317592,0.062238723,0.00008590029,0.44632524],"study_design_scores_gemma":[0.00010964376,0.0002584267,0.0041110474,0.00008378758,0.0000028321813,0.0000042119564,0.000117138,0.9688435,0.00060186646,0.024331247,0.0012247543,0.0003115067],"about_ca_topic_score_codex":0.0016825828,"about_ca_topic_score_gemma":0.00066812313,"teacher_disagreement_score":0.684668,"about_ca_system_score_codex":0.00010669077,"about_ca_system_score_gemma":0.00006071182,"threshold_uncertainty_score":0.9740896},"labels":[],"label_agreement":null},{"id":"W2774018923","doi":"","title":"Stochastic Segmentation Trees for Multiple Ground Truths.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","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 Toronto","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence","score_opus":0.07325594585640749,"score_gpt":0.3439437889214061,"score_spread":0.2706878430649986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774018923","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":[0.016922804,0.000011938357,0.98099434,0.0009067465,0.00024022571,0.0006031186,0.000009579409,0.00013692661,0.0001743032],"genre_scores_gemma":[0.96798646,0.000006296223,0.031356566,0.00007684956,0.00008498259,0.0004062926,0.000005111831,0.000007684404,0.00006976427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988641,0.000021063246,0.00034199303,0.0003901855,0.00013538344,0.0002472725],"domain_scores_gemma":[0.998695,0.00022402754,0.0001921226,0.0007329,0.00009808676,0.000057825266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029520591,0.0001240843,0.000133785,0.00010948266,0.00058842526,0.00042082462,0.0010461872,0.00006664318,0.00001385698],"category_scores_gemma":[0.00024527352,0.00012457104,0.00007299185,0.00014088342,0.0001205256,0.0004218952,0.00009216493,0.000096311174,0.000036447833],"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":[0.00003508853,0.00014532018,0.00013780469,0.000007942359,0.00000655699,0.0000013661452,0.0006691084,0.028620262,0.008066508,0.3453508,0.000066759436,0.61689246],"study_design_scores_gemma":[0.00004841452,0.000095327305,0.00065037573,0.000017135411,0.0000033504134,0.0000017068512,0.00022097213,0.7997108,0.031795878,0.1670067,0.00027428145,0.00017505321],"about_ca_topic_score_codex":0.0006498058,"about_ca_topic_score_gemma":0.0012939487,"teacher_disagreement_score":0.95106363,"about_ca_system_score_codex":0.000093142946,"about_ca_system_score_gemma":0.00003933222,"threshold_uncertainty_score":0.50798595},"labels":[],"label_agreement":null},{"id":"W2809487708","doi":"","title":"Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return","year":2018,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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 Alberta","funders":"","keywords":"Variance (accounting); Temporal difference learning; Computer science; Reinforcement learning; Mean squared error; Lookup table; Function (biology); Algorithm; Artificial intelligence; Statistics; Mathematics","score_opus":0.10081530290189013,"score_gpt":0.3667202816540406,"score_spread":0.26590497875215047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809487708","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":[0.016845755,0.000056518416,0.98041606,0.0005722437,0.0008506401,0.00051907444,9.868444e-7,0.000049242997,0.0006894831],"genre_scores_gemma":[0.71684253,0.0000035164721,0.28287148,0.00010627588,0.0000766349,0.000026391655,4.915146e-7,0.0000070200463,0.00006563711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799544,0.00037470978,0.00063358666,0.0004044402,0.00023532312,0.0003565166],"domain_scores_gemma":[0.99687046,0.0017802938,0.00037629687,0.00073124847,0.00019807542,0.000043618616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023272978,0.00018037287,0.00029550478,0.00007973375,0.00037552303,0.00017378469,0.0015124287,0.00007041315,0.000005240654],"category_scores_gemma":[0.002208543,0.00011339364,0.00006906054,0.0007697128,0.0005769173,0.00015060267,0.00047185627,0.00025808075,0.0000029517446],"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":[0.00004864595,0.00004294043,0.007104582,0.00006798737,0.00003092308,6.8925004e-7,0.010404069,0.65884894,0.005068832,0.06448582,0.00003421429,0.25386235],"study_design_scores_gemma":[0.000028215805,0.00008760605,0.0011365286,0.00013793792,0.000007399344,0.0000026428925,0.00013730988,0.949399,0.026623124,0.022148438,0.00015669568,0.00013508541],"about_ca_topic_score_codex":0.00032705552,"about_ca_topic_score_gemma":0.00023664869,"teacher_disagreement_score":0.69999677,"about_ca_system_score_codex":0.000060663046,"about_ca_system_score_gemma":0.0000908607,"threshold_uncertainty_score":0.46240586},"labels":[],"label_agreement":null},{"id":"W2914852071","doi":"","title":"Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics.","year":2018,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","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":"University of Waterloo","funders":"","keywords":"Langevin dynamics; Variance (accounting); Dynamics (music); Statistical physics; Computer science; Stochastic process; Langevin equation; Stochastic dynamics; Physics; Mathematics; Statistics; Economics","score_opus":0.12525668512547758,"score_gpt":0.38339916574281296,"score_spread":0.25814248061733536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914852071","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":[0.25744343,0.000051020415,0.7313279,0.00070366525,0.0016572616,0.0007727726,0.000043789565,0.00018242856,0.007817753],"genre_scores_gemma":[0.98040795,0.000008757187,0.01827936,0.00016320542,0.00051175104,0.00007773328,0.000016935424,0.000042377345,0.0004919467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99738735,0.00026552382,0.0007980117,0.00056263065,0.00032859654,0.00065791584],"domain_scores_gemma":[0.9977675,0.0009756714,0.00019127593,0.0006378238,0.0002608886,0.00016681047],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001465293,0.00030716977,0.00045294594,0.00024235177,0.00015470848,0.00007112797,0.0004728461,0.00016271256,0.00029022602],"category_scores_gemma":[0.0025195596,0.00028571056,0.00013250831,0.0006581341,0.00034062503,0.000086701395,0.00011584022,0.0003149295,0.000013028751],"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":[0.00025656633,0.00037704545,0.000043410375,0.000056427263,0.000036319863,0.000027623986,0.004303928,0.0014011058,0.0023739606,0.8891155,0.00059321936,0.10141487],"study_design_scores_gemma":[0.00013106168,0.00033790697,0.000029458066,0.00022507028,0.000043938282,0.000016426095,0.0036074882,0.4660279,0.0068178396,0.52148044,0.00060133485,0.0006811678],"about_ca_topic_score_codex":0.00070184615,"about_ca_topic_score_gemma":0.013480355,"teacher_disagreement_score":0.7229645,"about_ca_system_score_codex":0.00038675626,"about_ca_system_score_gemma":0.00010023301,"threshold_uncertainty_score":0.9999595},"labels":[],"label_agreement":null},{"id":"W2963001956","doi":"","title":"Bayesian hyperparameter optimization for ensemble learning","year":2016,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Machine Learning and Data Classification","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":"École de Technologie Supérieure; Université Laval","funders":"","keywords":"Hyperparameter; Bayesian optimization; Hyperparameter optimization; Ensemble learning; Computer science; Machine learning; Artificial intelligence; Bayesian probability; Greedy algorithm; Mathematical optimization; Algorithm; Mathematics; Support vector machine","score_opus":0.04312508995708945,"score_gpt":0.30252744251481944,"score_spread":0.25940235255773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963001956","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":[0.0015171065,0.000019908253,0.9939186,0.0033063723,0.00025004995,0.00024101949,0.0000027446395,0.00015645837,0.0005877714],"genre_scores_gemma":[0.9030059,0.000028861396,0.09637958,0.00013367347,0.000075452896,0.00006668342,0.000012977444,0.000011203956,0.00028566655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998457,0.00013579341,0.00039972414,0.00049770996,0.0001744571,0.0003353251],"domain_scores_gemma":[0.99861825,0.00065689575,0.00012485738,0.00041336092,0.00011565208,0.00007101425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006736309,0.00013854944,0.00014929619,0.00018433097,0.00014358909,0.00014180639,0.00056102773,0.00008348846,0.000063962965],"category_scores_gemma":[0.0011973907,0.00010494859,0.00005776909,0.0003921206,0.00005867349,0.0004162545,0.00007902731,0.00013170045,0.00009084018],"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":[0.000026438101,0.000030494666,0.00024986736,0.0000060410607,0.00000282399,0.0000014397136,0.00023370469,0.2725374,0.0020020066,0.10324438,0.000044945435,0.6216205],"study_design_scores_gemma":[0.00004869244,0.00010112239,0.00003236564,0.000040670617,0.0000022495049,0.0000023410566,0.00006266275,0.96288556,0.0055846875,0.028042223,0.0030220435,0.00017537497],"about_ca_topic_score_codex":0.0000742128,"about_ca_topic_score_gemma":0.000081878876,"teacher_disagreement_score":0.9014888,"about_ca_system_score_codex":0.00009071382,"about_ca_system_score_gemma":0.000055087487,"threshold_uncertainty_score":0.42796794},"labels":[],"label_agreement":null},{"id":"W2963437270","doi":"","title":"Per-decision Multi-step Temporal Difference Learning with Control Variates.","year":2018,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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; University of British Columbia","funders":"","keywords":"Control variates; Temporal difference learning; Reinforcement learning; Variance (accounting); Computer science; Control (management); Variance reduction; Monte Carlo method; Artificial intelligence; Machine learning; Statistics; Mathematics; Hybrid Monte Carlo; Bayesian probability; Markov chain Monte Carlo","score_opus":0.04175902341515242,"score_gpt":0.29736944076871763,"score_spread":0.2556104173535652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963437270","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":[0.024280187,0.0000232633,0.973681,0.00029625397,0.00054851745,0.0004097868,0.000001129842,0.0002514954,0.00050839724],"genre_scores_gemma":[0.91474295,0.0000094348125,0.08444803,0.00023781971,0.0001397444,0.00002624314,0.000003935175,0.000022428932,0.00036943913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668896,0.00023484034,0.00078894803,0.0008340014,0.000687497,0.0007657619],"domain_scores_gemma":[0.99770385,0.00065604196,0.00028941644,0.00077351654,0.0004007421,0.00017643554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00092065445,0.00036139082,0.0003918751,0.0003060785,0.00031762547,0.000430544,0.001517513,0.00015544784,0.00015444499],"category_scores_gemma":[0.0006751445,0.00029851322,0.000076389006,0.0009073229,0.0004176887,0.00039926902,0.00027601467,0.0006631381,0.00063286873],"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":[0.00016779288,0.000114814626,0.009597239,0.0000087900435,0.000017471828,0.000032048378,0.002219321,0.83322,0.00066613225,0.022699425,0.000010766723,0.13124622],"study_design_scores_gemma":[0.00021600001,0.0007720465,0.0019823331,0.00012931618,0.0000073778715,0.000010932565,0.00036889198,0.992449,0.0011450279,0.0019423176,0.00056600274,0.00041076576],"about_ca_topic_score_codex":0.0011615485,"about_ca_topic_score_gemma":0.0012752519,"teacher_disagreement_score":0.89046276,"about_ca_system_score_codex":0.00018773622,"about_ca_system_score_gemma":0.00017620399,"threshold_uncertainty_score":0.9999467},"labels":[],"label_agreement":null},{"id":"W2963763058","doi":"","title":"Convex-constrained Sparse Additive Modeling and Its Extensions.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; Convexity; Additive model; Mathematical optimization; Mathematics; Regularization (linguistics); A priori and a posteriori; Computer science; Algorithm; Convex optimization; Regular polygon; Convex function; Artificial intelligence; Machine learning; Artificial neural network","score_opus":0.2580744315264659,"score_gpt":0.41992824452670474,"score_spread":0.16185381300023882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963763058","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":[0.4358971,0.000105554296,0.5547498,0.0010893224,0.000549273,0.0006609493,0.0001334661,0.000086929096,0.0067275753],"genre_scores_gemma":[0.96298236,0.00007852727,0.03665048,0.00010753469,0.00007965365,0.000028689752,0.0000023673979,0.000015154522,0.00005524433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983157,0.00011863608,0.0005615823,0.00042051284,0.00020639901,0.00037720747],"domain_scores_gemma":[0.9975196,0.0014794361,0.00017710263,0.00044108916,0.00023504674,0.0001476881],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008115286,0.00020584595,0.00037316093,0.0000861227,0.00036981906,0.00015466618,0.0003284412,0.00011977898,0.0003951579],"category_scores_gemma":[0.014770714,0.00018493099,0.00004809311,0.000076673576,0.00035150832,0.00014165316,0.00015233793,0.00030596508,0.000058143884],"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":[0.000053646345,0.00006699475,0.000028833478,0.000020423058,0.0000086019745,0.000037332513,0.00057878037,0.0006252253,0.0005990269,0.8667287,0.000020019994,0.13123237],"study_design_scores_gemma":[0.000028985129,0.000033740573,0.0000342258,0.000104843304,0.000008863623,0.0000045904494,0.0005530857,0.4741134,0.0021053737,0.5228595,0.000014559944,0.00013884276],"about_ca_topic_score_codex":0.00028043013,"about_ca_topic_score_gemma":0.00033122528,"teacher_disagreement_score":0.52708524,"about_ca_system_score_codex":0.000039782954,"about_ca_system_score_gemma":0.000069802445,"threshold_uncertainty_score":0.9935283},"labels":[],"label_agreement":null},{"id":"W2964434332","doi":"","title":"Low Frequency Adversarial Perturbation","year":2018,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","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":"University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Black box; Image (mathematics); Perturbation (astronomy); Cloud computing; Frequency domain; Transformation (genetics); Artificial intelligence; Theoretical computer science; Computer vision; Algorithm","score_opus":0.02908829838875739,"score_gpt":0.2972562858240615,"score_spread":0.2681679874353041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964434332","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":[0.014526994,0.000019375388,0.97444355,0.0016266447,0.0024276476,0.00025998385,0.0000016633577,0.00024580327,0.006448349],"genre_scores_gemma":[0.96538436,0.0000061351107,0.033125427,0.00045882488,0.0008611937,0.000019233306,0.000005335107,0.000015866757,0.0001236403],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735296,0.00028341188,0.00062831555,0.00071319327,0.00045529503,0.0005668411],"domain_scores_gemma":[0.9984267,0.00032101857,0.00017443772,0.0007079316,0.0002551172,0.000114826886],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010156066,0.0002474895,0.00024057369,0.00030414565,0.00027188243,0.00020053274,0.0014314012,0.00016213504,0.00029688483],"category_scores_gemma":[0.0011622154,0.00024884814,0.00008517656,0.001200056,0.00032145414,0.0007760671,0.0002977522,0.00043189226,0.00070664845],"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":[0.000038918468,0.00012314288,0.00027218394,0.000008710063,0.000009010066,0.000030398203,0.0039980174,0.062037535,0.0017015453,0.7074978,0.00008341512,0.22419935],"study_design_scores_gemma":[0.000057269805,0.00016470159,0.00015298568,0.000047705136,0.0000033520585,0.000005911546,0.00025671805,0.7077595,0.008261301,0.2825394,0.00043809184,0.00031306932],"about_ca_topic_score_codex":0.00055466633,"about_ca_topic_score_gemma":0.0007088163,"teacher_disagreement_score":0.95085734,"about_ca_system_score_codex":0.0002599062,"about_ca_system_score_gemma":0.00017771903,"threshold_uncertainty_score":0.99999636},"labels":[],"label_agreement":null},{"id":"W2965103194","doi":"","title":"Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem.","year":2019,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","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; York University","funders":"","keywords":"Computer science; Iterative reconstruction; Artificial intelligence; Algorithm; Inference; Fourier transform; Projection (relational algebra); Probabilistic logic; Computer vision; Mathematics","score_opus":0.01385795714457307,"score_gpt":0.2940716380388189,"score_spread":0.28021368089424586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965103194","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":[0.8190715,0.00012980524,0.17896742,0.0002732263,0.000033979897,0.00056306284,0.000010557141,0.000013585281,0.00093690987],"genre_scores_gemma":[0.99751115,0.000016743381,0.0018312611,0.00006915993,0.000022318236,0.000068339796,0.000028001776,0.000013825649,0.00043917596],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999009,0.000034003984,0.00022020156,0.00035200643,0.00013078454,0.00025397568],"domain_scores_gemma":[0.9991807,0.000031698353,0.00009430649,0.0005347771,0.00013179124,0.000026746107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026810932,0.00012039457,0.000115448995,0.000042745916,0.00009347216,0.00004056528,0.0003323625,0.000040826475,0.00003586172],"category_scores_gemma":[0.000022731007,0.000081883125,0.000041337295,0.00027626945,0.00035541196,0.000008804429,0.00007365975,0.00011436356,0.0000080072095],"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":[0.000121904,0.00010866318,0.0002775584,0.000018752686,0.000008852733,2.9572539e-7,0.00017916432,0.10198792,0.7571862,0.13199447,0.00012103475,0.00799523],"study_design_scores_gemma":[0.000042410815,0.00013765488,0.000021882543,0.000041732575,0.000010649159,0.000003668187,0.000502648,0.08594168,0.8032198,0.107190035,0.0026803766,0.0002074401],"about_ca_topic_score_codex":0.00007611325,"about_ca_topic_score_gemma":0.00024264815,"teacher_disagreement_score":0.1784397,"about_ca_system_score_codex":0.00002241818,"about_ca_system_score_gemma":0.00007308457,"threshold_uncertainty_score":0.3339097},"labels":[],"label_agreement":null},{"id":"W2965108857","doi":"","title":"Probability Distillation: A Caveat and Alternatives.","year":2019,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Algorithms and Data Compression","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":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Computer science; Distillation; Reliability engineering; Environmental science; Engineering; Chemistry; Chromatography","score_opus":0.04868565856059613,"score_gpt":0.2873961231225094,"score_spread":0.23871046456191325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965108857","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":[0.39804965,0.0001320586,0.59814787,0.00077597896,0.00056219,0.00049988646,0.000008794048,0.00009303798,0.0017305382],"genre_scores_gemma":[0.9884775,0.000028724633,0.011279211,0.00008848647,0.000044624936,0.000012221532,0.0000045943734,0.0000042094134,0.000060409617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986374,0.0000696213,0.00031787812,0.0005172344,0.00022273105,0.0002351732],"domain_scores_gemma":[0.9990624,0.00022506721,0.000070706126,0.0004990992,0.00006804581,0.00007466533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034103554,0.00013016796,0.0001611506,0.000083600404,0.00006463488,0.00015329814,0.0004955638,0.000050438626,0.00007388305],"category_scores_gemma":[0.00011749366,0.0001110756,0.000028038483,0.00032480666,0.00008898039,0.00046192604,0.00033657555,0.00014286547,0.000116470415],"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":[0.000034280278,0.000120364595,0.0064709615,0.000034151813,0.0000054744305,0.000017753193,0.0020741688,0.020373717,0.00061152334,0.5514335,0.000030808245,0.41879326],"study_design_scores_gemma":[0.00003450096,0.00006894036,0.0036231317,0.000054771528,9.724118e-7,0.000005082089,0.00010043637,0.77382183,0.0013939189,0.21993166,0.0007846065,0.00018014002],"about_ca_topic_score_codex":0.0004697556,"about_ca_topic_score_gemma":0.0002487921,"teacher_disagreement_score":0.7534481,"about_ca_system_score_codex":0.000068450674,"about_ca_system_score_gemma":0.00005222094,"threshold_uncertainty_score":0.45295313},"labels":[],"label_agreement":null},{"id":"W2966236725","doi":"","title":"On the Relationship Between Satisfiability and Markov Decision Processes.","year":2019,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"AI-based Problem Solving and Planning","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 Waterloo","funders":"","keywords":"Computer science; Satisfiability; Markov process; Markov chain; Markov decision process; Boolean satisfiability problem; Theoretical computer science; Algorithm; Mathematics; Machine learning; Statistics","score_opus":0.0628719743448886,"score_gpt":0.3038967491543022,"score_spread":0.2410247748094136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966236725","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":[0.7674216,0.00006496286,0.22889769,0.0021865903,0.00016945791,0.00036148683,0.0000042172433,0.00006492827,0.0008290809],"genre_scores_gemma":[0.99581826,0.0000039088054,0.0038368094,0.00024322112,0.000030817522,0.000015374324,0.00000293473,0.0000059006165,0.000042749078],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983831,0.00017985229,0.000395295,0.00046974444,0.00029783702,0.00027416126],"domain_scores_gemma":[0.98641187,0.012796313,0.00009688078,0.00055035576,0.000080620266,0.00006395024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016768753,0.00014595958,0.00015879299,0.000113786846,0.000193813,0.00018971841,0.0006209235,0.000091751324,0.00005407703],"category_scores_gemma":[0.0032980603,0.00010547179,0.000028649434,0.0007615022,0.00009091165,0.00021913524,0.0001343575,0.00038610742,0.00020806387],"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":[0.00004348467,0.000037782524,0.44987854,0.000045558427,0.00000389044,0.00000310771,0.002127273,0.01853644,0.000016006083,0.3856311,0.00012020149,0.14355661],"study_design_scores_gemma":[0.00003324817,0.00013985907,0.059436444,0.00028537025,0.0000028183863,0.000001783118,0.0001857865,0.10036164,0.0005040348,0.8386797,0.00013938124,0.00022998985],"about_ca_topic_score_codex":0.00019937835,"about_ca_topic_score_gemma":0.00017094953,"teacher_disagreement_score":0.45304853,"about_ca_system_score_codex":0.00006590353,"about_ca_system_score_gemma":0.00011642172,"threshold_uncertainty_score":0.43010145},"labels":[],"label_agreement":null},{"id":"W2966820259","doi":"","title":"Problem-dependent Regret Bounds for Online Learning with Feedback Graphs.","year":2019,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","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":"University of Victoria","funders":"","keywords":"Regret; Computer science; Online learning; Theoretical computer science; Artificial intelligence; Machine learning; World Wide Web","score_opus":0.1217124348623598,"score_gpt":0.41820771941942625,"score_spread":0.2964952845570664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966820259","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":[0.6132775,0.00019603437,0.37817386,0.0021209423,0.0007715348,0.003279109,0.00010524586,0.00017271122,0.0019030884],"genre_scores_gemma":[0.9801668,0.000040241925,0.0128747625,0.00011619413,0.00013005822,0.00012535369,0.000042344316,0.000045955465,0.0064583104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99430704,0.00026551436,0.0012069852,0.0012050467,0.0020728724,0.000942533],"domain_scores_gemma":[0.9950705,0.0025909685,0.0003498766,0.0007972589,0.0009959402,0.0001954597],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0035330437,0.00032699425,0.0005571393,0.0007320554,0.000245485,0.00044108668,0.0013466512,0.00016248878,0.00085655466],"category_scores_gemma":[0.0027257863,0.0002369631,0.00015157918,0.0021285051,0.00038099714,0.0005096626,0.00021227228,0.0007616902,0.0007842815],"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":[0.0009571567,0.00027911048,0.003943352,0.00002259702,0.000018432396,0.000021917524,0.0011058468,0.59843564,0.000788249,0.009224301,0.00013645818,0.38506693],"study_design_scores_gemma":[0.00040604576,0.0016915739,0.00066541723,0.00017666788,0.000010117503,0.000021242677,0.011161514,0.42944795,0.007196718,0.52970296,0.01876473,0.0007550914],"about_ca_topic_score_codex":0.00034973267,"about_ca_topic_score_gemma":0.0036672016,"teacher_disagreement_score":0.52047867,"about_ca_system_score_codex":0.00024371,"about_ca_system_score_gemma":0.00024290584,"threshold_uncertainty_score":0.99999374},"labels":[],"label_agreement":null},{"id":"W3089522502","doi":"","title":"Differentially Private Top-k Selection via Stability on Unknown Domain","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","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":"BC Cancer Agency; Simon Fraser University","funders":"","keywords":"Selection (genetic algorithm); Stability (learning theory); Computer science; Domain (mathematical analysis); Mathematics; Artificial intelligence; Machine learning","score_opus":0.06484861329735347,"score_gpt":0.2932797702087032,"score_spread":0.22843115691134974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089522502","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":[0.22613265,0.000014470834,0.7445517,0.02748033,0.0003846513,0.00039502434,0.000008445719,0.0007272624,0.00030544243],"genre_scores_gemma":[0.94388163,0.000017196448,0.05519222,0.00073758856,0.0001027333,0.000040167124,0.000009824685,0.00001556511,0.0000030578233],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966688,0.00025664645,0.00074172474,0.0011547292,0.0005317855,0.00064628583],"domain_scores_gemma":[0.995936,0.00035703325,0.00017568687,0.0032577817,0.00010975978,0.00016375385],"candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0006576391,0.00031851916,0.00033727274,0.00018768384,0.00016862972,0.0002375347,0.013189375,0.00020693219,0.00014452041],"category_scores_gemma":[0.010150315,0.00030766492,0.000095517724,0.0016775385,0.00022550668,0.0004611111,0.0127686355,0.00071555993,0.0002576525],"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":[0.00034239783,0.00068793556,0.0018769085,0.00007628203,0.000037136277,0.00005858142,0.0018961419,0.008387054,0.047185924,0.34513286,0.0022945786,0.5920242],"study_design_scores_gemma":[0.000035650206,0.0002225898,0.00016852566,0.000024811805,0.0000020181753,0.0000017143147,0.000049473936,0.4449967,0.10071411,0.45282185,0.0007143913,0.00024814735],"about_ca_topic_score_codex":0.00021213574,"about_ca_topic_score_gemma":0.00044165927,"teacher_disagreement_score":0.717749,"about_ca_system_score_codex":0.0003407758,"about_ca_system_score_gemma":0.00010332879,"threshold_uncertainty_score":0.99993753},"labels":[],"label_agreement":null},{"id":"W3089548822","doi":"","title":"Stable Policy Optimization via Off-Policy Divergence Regularization.","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","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; Université de Montréal","funders":"","keywords":"Reinforcement learning; Benchmark (surveying); Regularization (linguistics); Computer science; Divergence (linguistics); Stability (learning theory); Trust region; Mathematical optimization; Adversarial system; Optimization problem; Range (aeronautics); Term (time); Artificial intelligence; Machine learning; Algorithm; Mathematics; Engineering","score_opus":0.03451898926511433,"score_gpt":0.2957511071451971,"score_spread":0.26123211788008277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089548822","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":[0.0004019767,0.000060473118,0.9822041,0.015005858,0.0003166337,0.00033000248,0.000004398284,0.00029995694,0.0013766245],"genre_scores_gemma":[0.898549,0.000103588296,0.09778571,0.002491688,0.0008326095,0.000029182262,0.0000231595,0.000033354056,0.00015170121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969914,0.00026354403,0.00075367553,0.0008144684,0.0005448907,0.00063200115],"domain_scores_gemma":[0.9984587,0.00020656482,0.00025254444,0.00059238594,0.00023569861,0.00025412056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047300465,0.0002948607,0.00031314068,0.00042956293,0.00029128723,0.00025496873,0.0016763826,0.00015132723,0.00016334302],"category_scores_gemma":[0.0027101946,0.00031948695,0.00008551493,0.0042422637,0.00016503087,0.00093872054,0.00066792034,0.0004372454,0.00018371834],"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":[0.000021858397,0.00003764155,0.00015867241,0.0000100332545,0.000005327982,0.000011632816,0.0018673489,0.70338804,0.00025918148,0.22934973,0.000029433495,0.06486109],"study_design_scores_gemma":[0.0000474762,0.000069643356,0.000034467637,0.000023789138,0.0000035509872,0.0000032117955,0.00016636586,0.95748353,0.0020979133,0.039056186,0.00069619494,0.00031766904],"about_ca_topic_score_codex":0.0015522802,"about_ca_topic_score_gemma":0.00008636462,"teacher_disagreement_score":0.89814705,"about_ca_system_score_codex":0.00034520653,"about_ca_system_score_gemma":0.0005280908,"threshold_uncertainty_score":0.99992573},"labels":[],"label_agreement":null},{"id":"W3089673743","doi":"","title":"C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","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":"McGill University","funders":"","keywords":"Mutual information; Estimator; Minimax; Conditional independence; Conditional mutual information; Computer science; Mathematical optimization; Focus (optics); Independence (probability theory); Estimation; Conditional expectation; Artificial intelligence; Machine learning; Mathematics; Statistics; Engineering","score_opus":0.05566346586866754,"score_gpt":0.3117709213048471,"score_spread":0.2561074554361796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089673743","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":[0.07240032,0.0000051715106,0.9262177,0.0006632618,0.00018190396,0.00018490833,0.000006036071,0.000063550324,0.0002771552],"genre_scores_gemma":[0.9481994,9.4187266e-7,0.051443376,0.00023469851,0.000063927044,0.000005144623,0.00004651737,0.0000049241294,0.0000011062712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983968,0.00008677072,0.0007049931,0.00021320359,0.00038955885,0.00020863146],"domain_scores_gemma":[0.9990751,0.00020278465,0.00031144524,0.00018362244,0.0001561916,0.00007085166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035039705,0.00013117777,0.00017425025,0.0001947705,0.00009868669,0.000084066625,0.0004541229,0.000085109794,0.00006243505],"category_scores_gemma":[0.00089391763,0.00014566322,0.000054141656,0.00077266525,0.000070294875,0.0017633912,0.000109967186,0.0002110917,0.000056233417],"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":[0.000028846733,0.000015340507,0.00012544674,0.000017753651,0.0000033382112,0.0000012918125,0.00337575,0.7790578,0.00046144158,0.16538453,0.0000054018574,0.051523063],"study_design_scores_gemma":[0.000050109702,0.000063672764,0.00016173672,0.000027466196,0.000004041712,0.0000024388705,0.00038144228,0.95019585,0.008236492,0.04068549,0.000054209748,0.00013704952],"about_ca_topic_score_codex":0.00018910544,"about_ca_topic_score_gemma":0.000022871818,"teacher_disagreement_score":0.87579906,"about_ca_system_score_codex":0.00011127046,"about_ca_system_score_gemma":0.0001318223,"threshold_uncertainty_score":0.59399736},"labels":[],"label_agreement":null},{"id":"W3089723243","doi":"","title":"Learning Intrinsic Rewards as a Bi-Level Optimization Problem","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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":"Computer science; Intrinsic motivation; Mathematical optimization; Mathematics; Psychology; Social psychology","score_opus":0.06927971827994504,"score_gpt":0.29235963903827544,"score_spread":0.22307992075833039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089723243","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":[0.0017441077,0.000026188905,0.9894383,0.004360224,0.00025217902,0.0004057195,7.745625e-7,0.00035664046,0.0034158616],"genre_scores_gemma":[0.87512684,0.00006660161,0.12310248,0.0012452804,0.00015427345,0.000032371507,0.000013197925,0.00002597584,0.000232998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717504,0.00020559815,0.00079756265,0.0006971014,0.00057220383,0.00055248995],"domain_scores_gemma":[0.9986991,0.00022883051,0.00024812508,0.00039802745,0.00021872524,0.00020714405],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059359445,0.00026743743,0.00028843607,0.00022525236,0.00019228098,0.0003696927,0.0012260439,0.00013690312,0.00016362652],"category_scores_gemma":[0.0014169761,0.00027911065,0.00008541073,0.0017511689,0.0001121979,0.0005606853,0.0004332732,0.0006762156,0.0006226582],"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":[0.000029797087,0.000026498941,0.00013916391,0.000019508203,0.000008003498,0.000025260935,0.004339095,0.87366813,0.00015487506,0.053313013,0.000035262747,0.06824138],"study_design_scores_gemma":[0.00005195508,0.0003956192,0.000017492499,0.000061402985,0.00000448466,0.000005598848,0.000560008,0.99076337,0.0021182273,0.004667889,0.0010343521,0.00031961416],"about_ca_topic_score_codex":0.00026137644,"about_ca_topic_score_gemma":0.000030173753,"teacher_disagreement_score":0.8733827,"about_ca_system_score_codex":0.00017400827,"about_ca_system_score_gemma":0.00022692952,"threshold_uncertainty_score":0.9999661},"labels":[],"label_agreement":null},{"id":"W3090054376","doi":"","title":"Amortized Bayesian Optimization over Discrete Spaces","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Machine Learning and Algorithms","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":"Bayesian probability; Amortized analysis; Computer science; Bayesian optimization; Mathematical optimization; Mathematics; Artificial intelligence; Data structure; Programming language","score_opus":0.027766262182025745,"score_gpt":0.29204230372859835,"score_spread":0.2642760415465726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090054376","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":[0.0032728945,0.000043771324,0.9863282,0.008486524,0.00029543537,0.00018142046,0.0000025203624,0.00023656563,0.0011526742],"genre_scores_gemma":[0.94497144,0.000022049991,0.0536009,0.0011082375,0.00020872832,0.000013772505,0.000008223096,0.000013203566,0.000053439933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814373,0.00014751026,0.00045556267,0.0005607036,0.0003237062,0.00036877228],"domain_scores_gemma":[0.9991722,0.00013402324,0.00012823903,0.00033039492,0.000056602912,0.00017850276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030341864,0.00019430589,0.00023030155,0.000110523004,0.00011796668,0.00031643038,0.0008130986,0.00007373893,0.00025224264],"category_scores_gemma":[0.00040214637,0.00017955032,0.00007818028,0.0009669163,0.00008058999,0.0003573909,0.00019041852,0.0003229004,0.000100201934],"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":[0.000024241706,0.000029742101,0.00033601816,0.0000084374915,0.0000049825576,0.000023061508,0.0019961623,0.8936639,0.00015074392,0.03905076,0.000062980784,0.064649],"study_design_scores_gemma":[0.000044718523,0.00008781779,0.000056503635,0.00002262436,0.0000027116707,0.0000016017511,0.00017935604,0.9904122,0.0011216082,0.0072310423,0.0006107681,0.00022907696],"about_ca_topic_score_codex":0.00050678157,"about_ca_topic_score_gemma":0.00009083973,"teacher_disagreement_score":0.94169855,"about_ca_system_score_codex":0.00004473312,"about_ca_system_score_gemma":0.00006111203,"threshold_uncertainty_score":0.73218495},"labels":[],"label_agreement":null},{"id":"W3090228367","doi":"","title":"Batch norm with entropic regularization turns deterministic autoencoders into generative models","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","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":"Autoencoder; Computer science; Generative model; Regularization (linguistics); Generative grammar; Encoder; Algorithm; Artificial neural network; Artificial intelligence; Encoding (memory); Matrix norm; Source code; Normalization (sociology); Theoretical computer science; Eigenvalues and eigenvectors","score_opus":0.04116235678626784,"score_gpt":0.25221367207682416,"score_spread":0.21105131529055632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090228367","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":[0.0020394158,0.000060651357,0.99151444,0.0051794765,0.00022537916,0.00036353708,0.0000032162195,0.00010707707,0.00050681375],"genre_scores_gemma":[0.9072923,0.00003114641,0.091257185,0.0011208716,0.00020339443,0.00003688584,0.000009506556,0.000015970229,0.000032755037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976653,0.00020212906,0.0005358087,0.0007710872,0.0003817734,0.0004438931],"domain_scores_gemma":[0.99885464,0.00016541604,0.00015876093,0.0004105969,0.00020948194,0.00020109382],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020354046,0.00029458638,0.00032322117,0.00011330048,0.00017759936,0.00030303324,0.00081921084,0.000096386364,0.000043893062],"category_scores_gemma":[0.00018109955,0.00025213938,0.0000703008,0.000995218,0.00018683694,0.00074133807,0.0001747249,0.00025092502,0.00005192933],"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":[0.00005225503,0.000050432525,0.000028744733,0.000012300505,0.000015574491,0.000036618516,0.009294292,0.88448524,0.0015783514,0.07177978,0.000038468424,0.032627933],"study_design_scores_gemma":[0.00005584618,0.00024668244,0.000012450505,0.000031747808,0.0000095743135,0.0000026496755,0.0005237999,0.9081054,0.010871869,0.07970461,0.00014080903,0.00029458434],"about_ca_topic_score_codex":0.00030897648,"about_ca_topic_score_gemma":0.0010239517,"teacher_disagreement_score":0.9052529,"about_ca_system_score_codex":0.00013747839,"about_ca_system_score_gemma":0.00020126977,"threshold_uncertainty_score":0.9999931},"labels":[],"label_agreement":null},{"id":"W3090466896","doi":"","title":"Differentially Private Small Dataset Release Using Random Projections","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","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":"Simon Fraser University; BC Cancer Agency","funders":"","keywords":"Computer science","score_opus":0.1354467866766076,"score_gpt":0.3239428926810161,"score_spread":0.18849610600440853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090466896","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":[0.04464773,0.00005037317,0.93746454,0.015911182,0.00042570516,0.00058084214,0.00026448516,0.000592376,0.00006278605],"genre_scores_gemma":[0.7939322,0.0000720178,0.20450504,0.0010111517,0.00015750926,0.000053431373,0.00023800855,0.000026874086,0.000003742664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706274,0.00020142974,0.00076836307,0.0009985071,0.00035149712,0.0006174685],"domain_scores_gemma":[0.9950271,0.00031446663,0.0002005838,0.0041978313,0.000085747604,0.00017425002],"candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00047072655,0.0002963561,0.00034952755,0.00024300846,0.00021532016,0.00034420178,0.016172279,0.00015973169,0.00005881908],"category_scores_gemma":[0.018367602,0.00029113353,0.000081926504,0.0016094721,0.00023857785,0.0005753145,0.023409877,0.0006011005,0.00014030196],"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":[0.0012601126,0.0015665028,0.0014655313,0.00032472407,0.00020024768,0.0008431658,0.0036347415,0.15526854,0.06187988,0.15320252,0.040981095,0.57937294],"study_design_scores_gemma":[0.000099664336,0.000069910944,0.000016166121,0.00004710016,0.000008763412,0.000007810716,0.00010206726,0.841666,0.018363351,0.13773933,0.0015756909,0.00030413477],"about_ca_topic_score_codex":0.0009006776,"about_ca_topic_score_gemma":0.00053459994,"teacher_disagreement_score":0.7492845,"about_ca_system_score_codex":0.00016965496,"about_ca_system_score_gemma":0.00018863936,"threshold_uncertainty_score":0.9999541},"labels":[],"label_agreement":null},{"id":"W3090786554","doi":"","title":"Non Parametric Graph Learning for Bayesian Graph Neural Networks","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Advanced Graph Neural Networks","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; McGill University","funders":"","keywords":"Computer science; Adjacency matrix; Inference; Graph; Theoretical computer science; Artificial intelligence; Machine learning; Statistical relational learning; Data mining; Relational database","score_opus":0.04336867985110546,"score_gpt":0.2901535011615251,"score_spread":0.24678482131041965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090786554","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":[0.0106334165,0.0002379478,0.9849193,0.0022589646,0.00072624016,0.00080036133,0.0000018135154,0.00032711047,0.00009486596],"genre_scores_gemma":[0.98393875,0.00008121945,0.013921,0.0015821868,0.00030647693,0.00011288664,0.0000122155525,0.00003418943,0.000011065288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646944,0.00014479541,0.0008725834,0.0011143332,0.000369234,0.0010296338],"domain_scores_gemma":[0.99783915,0.000882119,0.00026547533,0.00050006656,0.00016288724,0.00035029498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044359703,0.00040940367,0.00047578482,0.00044228273,0.00030891143,0.0002621942,0.0015835775,0.00019808377,0.000015893072],"category_scores_gemma":[0.0005406115,0.00041446075,0.00029502166,0.0051453756,0.00019101851,0.0005782825,0.00026721784,0.0009109877,0.00001847114],"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":[0.00008500252,0.000042807118,0.00058262737,0.000012682224,0.000008623659,0.000023985494,0.0004291863,0.81509036,0.00009443361,0.020580912,0.00007759252,0.16297182],"study_design_scores_gemma":[0.00007803739,0.00040942925,0.00008410281,0.000025942776,0.0000071192467,0.000004406406,0.00017191848,0.93551326,0.00088082516,0.06214985,0.00023922019,0.0004358636],"about_ca_topic_score_codex":0.00008285142,"about_ca_topic_score_gemma":0.00013641687,"teacher_disagreement_score":0.97330534,"about_ca_system_score_codex":0.00006188462,"about_ca_system_score_gemma":0.00003783708,"threshold_uncertainty_score":0.9998307},"labels":[],"label_agreement":null},{"id":"W3091221243","doi":"","title":"Semi-supervised Sequential Generative Models","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","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 British Columbia","funders":"","keywords":"MNIST database; Computer science; Estimator; Latent variable; Generative model; Generative grammar; Machine learning; Artificial intelligence; Trajectory; Forcing (mathematics); Variance (accounting); Variable (mathematics); Deep learning; Mathematics; Statistics","score_opus":0.09722466294154446,"score_gpt":0.28411242239710377,"score_spread":0.1868877594555593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091221243","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":[0.0032414766,0.00012086965,0.9871388,0.006927011,0.00042139,0.0002999617,0.000008136414,0.00013187429,0.0017104533],"genre_scores_gemma":[0.9621336,0.00004676253,0.034620907,0.0026769033,0.0004413199,0.000029723229,0.000006912974,0.000014590159,0.000029291145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975247,0.000258744,0.00058793824,0.0007696978,0.00035088064,0.00050805084],"domain_scores_gemma":[0.9989495,0.00015210411,0.00009876578,0.00040507654,0.00016486827,0.00022965555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032575836,0.00027409825,0.00032482084,0.000099112156,0.00016441189,0.0002840159,0.0010908842,0.000106731044,0.0001529564],"category_scores_gemma":[0.00017719675,0.00026150205,0.00012933658,0.000949108,0.00012919992,0.00081401155,0.0003085817,0.00029440972,0.00018936831],"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":[0.000031594263,0.00005135535,0.000008186119,0.0000048743195,0.000013338066,0.000032092707,0.0039203116,0.8264562,0.008275614,0.10155272,0.00026818737,0.059385523],"study_design_scores_gemma":[0.00004073197,0.00008585105,0.0000025772783,0.000014346578,0.000004302084,0.0000015591635,0.00034930836,0.8884271,0.05070469,0.059737813,0.0003571258,0.00027462098],"about_ca_topic_score_codex":0.00030737653,"about_ca_topic_score_gemma":0.00021858043,"teacher_disagreement_score":0.9588921,"about_ca_system_score_codex":0.0000896528,"about_ca_system_score_gemma":0.00014312967,"threshold_uncertainty_score":0.9999837},"labels":[],"label_agreement":null},{"id":"W3183287321","doi":"","title":"PROVIDE: A Probabilistic Framework for Unsupervised Video Decomposition","year":2021,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","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":"Computer science; Inference; Leverage (statistics); Artificial intelligence; Probabilistic logic; Unsupervised learning; Benchmark (surveying); Machine learning; Decomposition; Pattern recognition (psychology)","score_opus":0.04810708885814038,"score_gpt":0.32039773356764245,"score_spread":0.2722906447095021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183287321","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":[0.0028347499,0.00015812245,0.99184525,0.0034942275,0.00070881733,0.0006054102,0.000008900217,0.0000870105,0.00025748662],"genre_scores_gemma":[0.7329317,0.00002127809,0.26600444,0.00058061903,0.00022421211,0.00017670017,0.000014222248,0.000012516219,0.00003433622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777615,0.00020492848,0.0005734894,0.0007321488,0.0002341453,0.0004791248],"domain_scores_gemma":[0.99771184,0.0010903832,0.00010307177,0.0005908507,0.00039565403,0.00010818105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005066144,0.00021480228,0.00028462018,0.00010400878,0.00019872632,0.0003434424,0.00060967973,0.0001306028,0.00007025931],"category_scores_gemma":[0.0016592038,0.000213604,0.00014172832,0.000880632,0.00009402125,0.0003757043,0.00015661688,0.00021080894,0.000046223777],"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":[0.000046993704,0.00021418127,0.000025326697,0.00003110941,0.000013673106,0.000024324354,0.00085655495,0.19244249,0.0032868683,0.62216806,0.0000839702,0.18080643],"study_design_scores_gemma":[0.000029627543,0.00006102262,0.00002230782,0.00007638909,0.0000057051984,0.0000037493476,0.00015260038,0.50836104,0.025547799,0.46508276,0.0004807885,0.00017620719],"about_ca_topic_score_codex":0.00009179114,"about_ca_topic_score_gemma":0.0004793052,"teacher_disagreement_score":0.73009694,"about_ca_system_score_codex":0.00014563021,"about_ca_system_score_gemma":0.00025309192,"threshold_uncertainty_score":0.8710518},"labels":[],"label_agreement":null},{"id":"W3183593932","doi":"","title":"Efficient Greedy Coordinate Descent via Variable Partitioning","year":2021,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Face and Expression Recognition","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":"Coordinate descent; Greedy algorithm; Partition (number theory); Convergence (economics); Algorithm; Computer science; Descent (aeronautics); Mathematical optimization; Mathematics; Variable (mathematics); Rate of convergence; Key (lock); Combinatorics","score_opus":0.03679210610531011,"score_gpt":0.2756887765513355,"score_spread":0.2388966704460254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183593932","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":[0.05058338,0.00009687208,0.94506204,0.0012219874,0.0008606243,0.00016359866,0.000003930283,0.00012955231,0.0018780092],"genre_scores_gemma":[0.9808239,0.00001858874,0.018383568,0.00051278796,0.000066311746,0.000040594572,0.000014104048,0.000008993659,0.00013115615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979837,0.00015638853,0.0005121426,0.0005732976,0.000285401,0.0004890575],"domain_scores_gemma":[0.99886745,0.00017287431,0.000095046445,0.00046322396,0.00027494592,0.00012645652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000506329,0.00017153553,0.00020000926,0.00013777762,0.00020942249,0.00023098,0.00045503143,0.000094684525,0.0003544139],"category_scores_gemma":[0.00023357493,0.0001741413,0.00006929364,0.0011431287,0.000069990245,0.00017036636,0.00024124832,0.00024967155,0.0005197065],"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":[0.000024457942,0.000503664,0.00013957838,0.00002624858,0.000010895727,0.00012485958,0.0013290731,0.6721378,0.023727585,0.14535047,0.0002579593,0.15636744],"study_design_scores_gemma":[0.000042398708,0.000034117376,0.000054052278,0.0001462111,0.0000037976238,0.000018778821,0.00037040125,0.83431494,0.093391865,0.070667714,0.0007262152,0.0002294979],"about_ca_topic_score_codex":0.0003361228,"about_ca_topic_score_gemma":0.00022228398,"teacher_disagreement_score":0.9302405,"about_ca_system_score_codex":0.000127691,"about_ca_system_score_gemma":0.00015055687,"threshold_uncertainty_score":0.71012765},"labels":[],"label_agreement":null},{"id":"W3184302936","doi":"","title":"Identifying Regions of Trusted Predictions","year":2021,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Machine Learning and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Pointwise; Machine learning; Artificial intelligence; Process (computing); Domain (mathematical analysis); Data mining; Confidence interval; Sample complexity; Sample (material); Statistics; Mathematics","score_opus":0.05836014756836001,"score_gpt":0.32275409738340266,"score_spread":0.26439394981504266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184302936","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":[0.011703762,0.00014306307,0.9837835,0.0015058593,0.00070809986,0.00008521359,0.0000041525827,0.00011577723,0.0019505966],"genre_scores_gemma":[0.97895986,0.000043670847,0.020482406,0.00008854523,0.00007752008,0.000011620894,0.0000066580587,0.0000066012553,0.0003231109],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984262,0.00015669323,0.0005060093,0.00038282495,0.00026354974,0.00026472614],"domain_scores_gemma":[0.9988704,0.00022906066,0.00011537623,0.0005249449,0.00018954449,0.000070721144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036410821,0.00011323339,0.00018983493,0.00018898108,0.00011848461,0.00009219883,0.00054334407,0.000065371154,0.00008682872],"category_scores_gemma":[0.00047527117,0.00011754345,0.000090324866,0.0013777084,0.00009107994,0.00020274054,0.00018034302,0.00028799273,0.000043606386],"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":[0.0000075951702,0.00024420593,0.00043939048,0.000026073374,0.00001636315,0.000078688485,0.0031362022,0.13418524,0.003117717,0.72105724,0.00011107754,0.13758023],"study_design_scores_gemma":[0.00003355698,0.00004140624,0.00041825636,0.000108961925,0.00000546745,0.000026821108,0.0009631577,0.8492627,0.025378486,0.12291241,0.0006844457,0.00016429415],"about_ca_topic_score_codex":0.0004715599,"about_ca_topic_score_gemma":0.00036134466,"teacher_disagreement_score":0.9672561,"about_ca_system_score_codex":0.000048315236,"about_ca_system_score_gemma":0.00015086246,"threshold_uncertainty_score":0.4793283},"labels":[],"label_agreement":null},{"id":"W3185541071","doi":"","title":"Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts","year":2021,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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":"Reinforcement learning; Computer science; Robustness (evolution); Machine learning; Artificial intelligence; Transfer of learning; Reuse; Context (archaeology); Task (project management); Bayesian probability; Engineering","score_opus":0.03181191723208893,"score_gpt":0.2972237819968313,"score_spread":0.2654118647647424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185541071","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":[0.017802097,0.00014352263,0.9778465,0.00074098824,0.00034482637,0.0003008406,5.034385e-7,0.0000747107,0.0027460174],"genre_scores_gemma":[0.99531746,0.00009237086,0.0037506698,0.00040663997,0.00008703486,0.00002862503,0.000013035425,0.000018654568,0.00028549664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963885,0.00032359466,0.0012738063,0.0006292852,0.0006361855,0.0007486546],"domain_scores_gemma":[0.9983697,0.00047789974,0.00013978027,0.000650998,0.00022674644,0.00013489752],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074074406,0.00030707652,0.00046274083,0.0005450668,0.00011423979,0.00013132862,0.00096059655,0.00017908815,0.00014912555],"category_scores_gemma":[0.0008757324,0.00032296003,0.00013099263,0.0018989161,0.00019478127,0.0003611309,0.00026179734,0.0005549738,0.00005069255],"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":[0.000038712933,0.000085955246,0.00032359496,0.000022522665,0.000013332588,0.000098004755,0.0068125525,0.83334583,0.0065737306,0.11676473,0.0000063364614,0.035914686],"study_design_scores_gemma":[0.0001554912,0.00022840472,0.0000757289,0.00013222086,0.0000041434664,0.000015143939,0.0010162907,0.9247836,0.06667003,0.0057767914,0.0007781904,0.00036398173],"about_ca_topic_score_codex":0.0011149034,"about_ca_topic_score_gemma":0.0012899691,"teacher_disagreement_score":0.9775154,"about_ca_system_score_codex":0.00036058045,"about_ca_system_score_gemma":0.00037812046,"threshold_uncertainty_score":0.9999223},"labels":[],"label_agreement":null},{"id":"W9932698","doi":"10.48550/arxiv.1205.2619","title":"Regret-based Reward Elicitation for Markov Decision Processes","year":2012,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":61,"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":"Regret; Markov decision process; Computer science; Preference elicitation; Function (biology); Minimax; Preference; Markov process; Markov chain; Mathematical optimization; Machine learning; Mathematics; Economics; Microeconomics","score_opus":0.06076316977610513,"score_gpt":0.3316740532104062,"score_spread":0.2709108834343011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9932698","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":[0.006224458,0.000100567726,0.99106616,0.00062897167,0.0008009584,0.0005801766,0.0000021409483,0.00014265958,0.00045393853],"genre_scores_gemma":[0.83595026,0.000015355714,0.16332868,0.00036667445,0.00014285771,0.00010020419,0.000012747715,0.000015301133,0.00006795394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977805,0.00007609058,0.00065835344,0.00039691385,0.00045059447,0.00063751626],"domain_scores_gemma":[0.9971154,0.0016669366,0.00020772919,0.00052001246,0.00036072376,0.00012924668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001253235,0.00020912317,0.00021047328,0.0002943731,0.00016907648,0.00018532133,0.000864263,0.00011956389,0.000029275177],"category_scores_gemma":[0.00340313,0.00019806823,0.00007388119,0.0010691283,0.00008641126,0.00068490417,0.00009730591,0.00018552711,0.0001174913],"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":[0.000086741224,0.000088580404,0.00092815625,0.000062485975,0.0000042258166,0.0000011907373,0.0011791738,0.7760761,0.000173311,0.03872203,0.00017677435,0.18250127],"study_design_scores_gemma":[0.00006960314,0.00017097045,0.00019212207,0.0001483679,0.000005887343,0.0000016824342,0.0002495141,0.9661885,0.012210977,0.017191485,0.0032668232,0.0003040811],"about_ca_topic_score_codex":0.00006058887,"about_ca_topic_score_gemma":0.000084854975,"teacher_disagreement_score":0.8297258,"about_ca_system_score_codex":0.00019137963,"about_ca_system_score_gemma":0.000198879,"threshold_uncertainty_score":0.8076988},"labels":[],"label_agreement":null}]}