{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":418,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":418,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"7a7781369b2e","filters":{"topic":"Markov Chains and Monte Carlo Methods"}},"results":[{"id":"W1501586228","doi":"10.1111/j.1467-9868.2009.00736.x","title":"Particle Markov Chain Monte Carlo Methods","year":2010,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":2055,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Monte Carlo method; Hybrid Monte Carlo; Particle filter; Monte Carlo molecular modeling; Monte Carlo method in statistical physics; Computer science; Monte Carlo integration; Statistical physics; Markov chain mixing time; Quasi-Monte Carlo method; Markov chain; Mathematical optimization; Algorithm; Applied mathematics; Markov model; Mathematics; Markov property; Artificial intelligence; Statistics; Machine learning; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.08842884299117262,"gpt":0.4081792489599942,"spread":0.3197504059688216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01496063,0.0005075961,0.001415046,0.00003923061,0.0004591771,0.0001177848,0.0009709945,0.0004995647,0.0007291614],"category_scores_gemma":[0.05343314,0.000325087,0.0007546205,0.0003399412,0.001523269,0.0001465385,0.0004523123,0.002514654,9.254364e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001234717,"about_ca_system_score_gemma":0.0002308838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006686906,"about_ca_topic_score_gemma":0.00007030985,"domain_scores_codex":[0.9904186,0.005640093,0.001707255,0.0004741796,0.0008172016,0.000942634],"domain_scores_gemma":[0.9702234,0.02690596,0.0008898994,0.0007467233,0.0005694905,0.000664572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008832495,0.000427206,0.0008003085,0.000318794,0.0007256113,0.0001342489,0.001622979,0.00007360733,0.008513524,0.8137401,0.05705374,0.1157066],"study_design_scores_gemma":[0.003725202,0.001807338,0.006811652,0.0001528699,0.00213931,0.001081485,0.002886896,0.04460651,0.01096791,0.832821,0.09136394,0.001635892],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02017304,0.000154104,0.9720591,0.002929416,0.003239465,0.000357903,0.0002362348,0.0000552474,0.000795489],"genre_scores_gemma":[0.01862078,0.00003295184,0.9778603,0.0007578853,0.0007146169,0.00002151125,0.000001734163,0.0000805271,0.001909676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1140707,"threshold_uncertainty_score":0.9999201,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2920804790","doi":"10.1214/20-ba1221","title":"Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)","year":2020,"lang":"en","type":"article","venue":"Bayesian Analysis","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1511,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Convergence (economics); Monte Carlo method; Bayesian probability; Variance (accounting); Markov chain; TRACE (psycholinguistics); Rank (graph theory)","retraction":null,"screen_n_in":null,"score":{"opus":0.04196391283684291,"gpt":0.3385584988034436,"spread":0.2965945859666007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000408966,0.0001543594,0.0004668159,0.000123482,0.0001225224,0.00006214481,0.0001180764,0.00006755444,0.00006418292],"category_scores_gemma":[0.0003149287,0.00008973282,0.0001375793,0.0009011069,0.00005886146,0.0002397862,0.00003222485,0.00004342872,1.04706e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001203763,"about_ca_system_score_gemma":0.00004108965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001656728,"about_ca_topic_score_gemma":0.00007065314,"domain_scores_codex":[0.9988281,0.000133035,0.0003955567,0.0003192087,0.000168042,0.0001560948],"domain_scores_gemma":[0.9989339,0.0001214867,0.000284908,0.000259565,0.0002270849,0.0001730145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001520926,0.001264256,0.6127262,0.008385792,0.01119678,0.00003827273,0.0411183,0.008613798,0.02073371,0.1658643,0.00464024,0.1238974],"study_design_scores_gemma":[0.0007947127,0.0001466055,0.0004466433,0.00004397288,0.002323974,0.000001398021,0.001706129,0.989767,0.002182388,0.00129831,0.001014086,0.0002748281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005802514,0.00004094224,0.9932067,0.0004584368,0.0000189443,0.0002434809,0.00001593384,0.00004067802,0.0001723093],"genre_scores_gemma":[0.786557,0.00001879491,0.2128602,0.0002004675,0.00005570567,0.00002039538,0.00004967213,0.00002645149,0.0002113351],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9811531,"threshold_uncertainty_score":0.3659198,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2032916024","doi":"10.1111/j.1751-5823.2002.tb00178.x","title":"On Choosing and Bounding Probability Metrics","year":2002,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1204,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bounding overwatch; Metric (unit); Convergence (economics); Mathematics; Computer science; Mathematical optimization; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.2082485533330793,"gpt":0.4373832283630112,"spread":0.229134675029932,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008720054,0.0001093783,0.0002440206,0.0000473107,0.00005031106,0.00004653927,0.0001130187,0.00003032366,0.00081498],"category_scores_gemma":[0.01693407,0.00008644909,0.00004578209,0.0001071815,0.00005133657,0.00004627335,0.00005099758,0.0001390199,0.000002169935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009232694,"about_ca_system_score_gemma":0.000005236044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003681765,"about_ca_topic_score_gemma":0.000002003873,"domain_scores_codex":[0.9988458,0.0001302438,0.0003246771,0.0002209844,0.000345535,0.0001327868],"domain_scores_gemma":[0.9970168,0.002564576,0.0000839868,0.0001440377,0.00009340913,0.00009717266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002850164,0.00006820665,0.00006138285,0.0006714495,0.00001757409,0.000008735597,0.00001539514,7.122596e-8,0.000003775699,0.8025538,0.01300404,0.1835927],"study_design_scores_gemma":[0.0004568563,0.0001339679,0.0002239209,0.003874627,0.0001274509,0.00005418842,0.000006529427,0.007232994,0.00002219033,0.6889166,0.2985815,0.0003691592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005285985,0.0324172,0.7954406,0.006817639,0.001103778,0.001298179,0.0002164219,0.0001443559,0.1572759],"genre_scores_gemma":[0.1372835,0.05417063,0.7998891,0.005388624,0.0003179127,0.0001277556,0.00002840215,0.0000619566,0.002732147],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2855774,"threshold_uncertainty_score":0.9913467,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047978125","doi":"10.1198/jcgs.2009.06134","title":"Examples of Adaptive MCMC","year":2009,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1078,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Metropolis–Hastings algorithm; Gibbs sampling; Markov chain; Computer science; Algorithm; Logarithm; Dimension (graph theory); Multivariate statistics; Mathematics; Artificial intelligence; Machine learning; Bayesian probability","retraction":null,"screen_n_in":null,"score":{"opus":0.05855318414416023,"gpt":0.3456207258929976,"spread":0.2870675417488374,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004795981,0.0000830967,0.0002875466,0.0001058386,0.00003816824,0.00001144114,0.00006726487,0.00004265496,0.000008886655],"category_scores_gemma":[0.0004461674,0.00006225816,0.00007019271,0.0001098691,0.00009010966,0.00004261683,0.00001125109,0.0001447257,9.723732e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000070275,"about_ca_system_score_gemma":0.00004084712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001554516,"about_ca_topic_score_gemma":0.000001538852,"domain_scores_codex":[0.9989315,0.00009363394,0.0004973593,0.00006343063,0.0003276519,0.00008647561],"domain_scores_gemma":[0.9973207,0.001684264,0.0003808489,0.00004083948,0.0004754041,0.00009800933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001148359,0.0001365027,0.0002480379,0.00002931106,0.00005239222,0.00002214842,0.0001702353,0.0002223871,0.00006997374,0.9606035,0.002333751,0.03599695],"study_design_scores_gemma":[0.000472597,0.0006655096,0.0126106,0.00004586666,0.00004960059,0.00007654084,0.00006158901,0.003702119,0.00002926292,0.9817947,0.0004203719,0.00007125843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1041341,0.0001908342,0.8949464,0.0002847784,0.00006243341,0.00004422226,0.0000662048,0.000003548427,0.0002674117],"genre_scores_gemma":[0.4915061,0.00003933818,0.5083071,0.00007982179,0.00004916126,1.37263e-7,0.000001712046,0.00000298188,0.00001360942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.387372,"threshold_uncertainty_score":0.2538814,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1840847274","doi":"10.1201/9781003453420-2","title":"MCMC Using Hamiltonian Dynamics","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":542,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hamiltonian (control theory); Statistical physics; Hamiltonian mechanics; Computation; Discretization; Hybrid Monte Carlo; Jacobian matrix and determinant; Applied mathematics; Monte Carlo method; Computer science; Mathematics; Mathematical optimization; Markov chain Monte Carlo; Algorithm; Physics; Mathematical analysis; Phase space; Quantum mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.2636789587258027,"gpt":0.4074502954342012,"spread":0.1437713367083986,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008524538,0.0003995381,0.0006278246,0.0001435511,0.00007211501,0.00006186639,0.0004589281,0.0005793586,0.0002161887],"category_scores_gemma":[0.0002679751,0.0003507973,0.0003254894,0.00006731988,0.00006050962,0.00003294995,0.001164393,0.0006850756,3.602474e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002511485,"about_ca_system_score_gemma":0.0001545979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005402287,"about_ca_topic_score_gemma":0.0007851223,"domain_scores_codex":[0.9982455,0.0001728567,0.0004792841,0.0005197444,0.0002243198,0.0003582776],"domain_scores_gemma":[0.9980488,0.0001714518,0.000285719,0.001212357,0.0001401929,0.0001414355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003464036,0.000287549,0.0004962913,0.002015176,0.0003906437,0.00005638558,0.001719892,0.00005247544,0.0003221591,0.9707477,0.004185281,0.01969178],"study_design_scores_gemma":[0.0005479975,0.0000579459,0.00004602268,0.000769838,0.0005704172,0.00005739864,0.000689791,0.1595727,0.001146468,0.8288962,0.005709666,0.001935593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06246638,0.0001048247,0.5968617,0.00005952258,0.001581555,0.0005173602,0.00004678367,0.0002916398,0.3380702],"genre_scores_gemma":[0.02536167,0.00004794643,0.9572919,0.0001339902,0.0002721096,0.00002439935,0.00002440427,0.0001196682,0.01672389],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3604302,"threshold_uncertainty_score":0.9998944,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2006582998","doi":"10.1239/jap/1183667414","title":"Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":382,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ergodicity; Mathematics; Counterexample; Markov chain; Markov chain Monte Carlo; Convergence (economics); Coupling (piping); Markov chain mixing time; Monte Carlo method; Algorithm; Statistical physics; Applied mathematics; Markov model; Discrete mathematics; Markov property; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.05514559893675608,"gpt":0.3196763472424737,"spread":0.2645307483057177,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0096956,0.0002104738,0.0007314171,0.0001235561,0.00006741998,0.00001407067,0.0001941607,0.0001643535,0.000008465266],"category_scores_gemma":[0.0005708077,0.0001673264,0.0001816836,0.000181996,0.0002131099,0.00007603334,0.00009973631,0.0004536721,1.582509e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001444727,"about_ca_system_score_gemma":0.00009488165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001743531,"about_ca_topic_score_gemma":0.00004185286,"domain_scores_codex":[0.9978126,0.00006512965,0.00113385,0.0002303157,0.0004645906,0.0002935342],"domain_scores_gemma":[0.9969593,0.001139551,0.0009469415,0.0003063633,0.0004430634,0.0002048039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.02111031,0.005701291,0.02693834,0.006369534,0.002405035,0.0003402429,0.02970261,0.003384313,0.04205164,0.3004178,0.002011342,0.5595675],"study_design_scores_gemma":[0.01660794,0.004441063,0.02210148,0.001365349,0.001580257,0.0005025302,0.01632733,0.06790333,0.1021703,0.7610521,0.003148703,0.002799612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315118,0.0002993378,0.06401803,0.00005023042,0.0001700438,0.0004560537,0.000009083119,0.00001567784,0.003469793],"genre_scores_gemma":[0.7003861,0.0000285093,0.2994085,0.00001834607,0.0001174975,0.000003058189,1.338769e-7,0.00001559639,0.00002223624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5567679,"threshold_uncertainty_score":0.6823374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2090251478","doi":"10.1214/aoap/1019487508","title":"Analysis of a nonreversible Markov chain sampler","year":2000,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":283,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Markov chain; Mathematics; Markov chain mixing time; Balance equation; Variable-order Markov model; Sampling (signal processing); Markov model; Simple (philosophy); Probabilistic logic; Markov renewal process; Convergence (economics); Markov property; Markov chain Monte Carlo; Continuous-time Markov chain; Applied mathematics; Chain (unit); Examples of Markov chains; Additive Markov chain; Markov process; Statistics; Computer science; Bayesian probability","retraction":null,"screen_n_in":null,"score":{"opus":0.1697908186442776,"gpt":0.3809267444006278,"spread":0.2111359257563503,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004534451,0.0001705547,0.0007051593,0.0001113823,0.00006010489,0.000007581677,0.0004142606,0.00009063868,0.001181763],"category_scores_gemma":[0.0002230626,0.0001162489,0.0004226105,0.0009627095,0.0002446405,0.00003124723,0.00008037588,0.000130068,3.657251e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001245242,"about_ca_system_score_gemma":0.0000386503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008985603,"about_ca_topic_score_gemma":0.00007968281,"domain_scores_codex":[0.9982943,0.0002292823,0.0006075251,0.0002874178,0.0003167085,0.0002647762],"domain_scores_gemma":[0.9973277,0.0009897857,0.0002627802,0.001184103,0.0001693425,0.00006621854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.006226146,0.003906005,0.004249484,0.00266206,0.01004771,0.000002465582,0.01886549,0.003154367,0.009085254,0.4430929,0.01502049,0.4836877],"study_design_scores_gemma":[0.001338473,0.0003108973,0.0096839,0.00007946454,0.003297798,0.000001467015,0.0009354827,0.00552139,0.05588131,0.9043581,0.01772046,0.0008712583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9230034,0.00005461431,0.00133352,0.0005222122,0.00001629518,0.0006303749,0.00007451283,0.00003146568,0.07433361],"genre_scores_gemma":[0.9678913,0.00006996184,0.03096674,0.0002508252,0.00002856765,0.00005006465,0.000009685069,0.00001651147,0.000716283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4828164,"threshold_uncertainty_score":0.9997313,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2941443959","doi":"10.1103/physrevd.100.034515","title":"Flow-based generative models for Markov chain Monte Carlo in lattice field theory","year":2019,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":227,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Argonne National Laboratory; Nuclear Physics; Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; National Science Foundation; Government of Canada; U.S. Department of Energy; Office of Science","keywords":"Statistical physics; Markov chain Monte Carlo; Generative grammar; Monte Carlo method; Markov chain; Computer science; Mathematics; Physics; Artificial intelligence; Machine learning; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.04610510541183537,"gpt":0.4599356051944695,"spread":0.4138304997826341,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002868458,0.0007879835,0.00284522,0.0000847857,0.00009030286,0.0000398793,0.0007060875,0.0001131827,0.0000354364],"category_scores_gemma":[0.003965122,0.0006051505,0.001507724,0.0006347345,0.00008033565,0.0002737953,0.0001924764,0.0007398915,0.00001302059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000153765,"about_ca_system_score_gemma":0.0001496321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001675639,"about_ca_topic_score_gemma":0.00001675557,"domain_scores_codex":[0.9947403,0.001530593,0.001081472,0.001085409,0.0007009792,0.0008612691],"domain_scores_gemma":[0.9902464,0.007159485,0.000479628,0.001457758,0.0003297846,0.000326915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004221348,0.002973595,0.0002100305,0.06832994,0.000323671,0.00002784768,0.0006636026,0.0007161606,0.005395006,0.7024918,0.02632268,0.1921235],"study_design_scores_gemma":[0.001706311,0.0006920272,0.00001693025,0.02279982,0.0009211659,0.000002783421,0.00003380241,0.5445462,0.00407803,0.4019905,0.02181443,0.001397942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6367866,0.1830348,0.08866011,0.01477526,0.001260663,0.03165331,0.000493471,0.0005972946,0.04273856],"genre_scores_gemma":[0.6326328,0.1291256,0.1506078,0.06966063,0.00377906,0.009579844,0.0001768322,0.0008136905,0.003623829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5438301,"threshold_uncertainty_score":0.99964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963561977","doi":"","title":"The bouncy particle sampler: A non-reversible rejection free Markov chain Monte Carlo method","year":2017,"lang":"en","type":"article","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":194,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Air Force Office of Scientific Research; Engineering and Physical Sciences Research Council","keywords":"Markov chain Monte Carlo; Monte Carlo method; Markov chain; Statistical physics; Mathematics; Applied mathematics; Markov chain mixing time; Particle filter; Computer science; Physics; Balance equation; Statistics; Markov model; Kalman filter","retraction":null,"screen_n_in":null,"score":{"opus":0.07136927114476796,"gpt":0.3376477929546848,"spread":0.2662785218099168,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005535887,0.0003477391,0.0006183556,0.0004474599,0.00630513,0.0001689827,0.003819471,0.0002150018,0.00008793771],"category_scores_gemma":[0.001983387,0.0003596045,0.0005201143,0.0005587132,0.001585827,0.0008707481,0.003334161,0.001045649,0.000001065849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004294062,"about_ca_system_score_gemma":0.0004174629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006305924,"about_ca_topic_score_gemma":0.01849133,"domain_scores_codex":[0.9950403,0.001550409,0.0002716307,0.0007802349,0.001136508,0.001220918],"domain_scores_gemma":[0.9928557,0.002600328,0.0005047634,0.002771981,0.0007505782,0.0005166287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01390646,0.00166948,0.03072111,0.001331913,0.002608127,0.001630322,0.02813465,0.0001431155,0.01231047,0.343198,0.2126456,0.3517008],"study_design_scores_gemma":[0.008488295,0.001436871,0.01842872,0.0003754923,0.0003843884,0.0000398357,0.06894254,0.03901647,0.0009367921,0.04868763,0.8120781,0.001184864],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5497253,0.0001799037,0.2627987,0.008084028,0.0005754291,0.002680858,0.0007869357,0.0002425898,0.1749263],"genre_scores_gemma":[0.4387246,0.006254922,0.4383249,0.00005513817,0.000313452,0.000003834391,0.00003641057,0.0001422954,0.1161445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5994325,"threshold_uncertainty_score":0.9998856,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1550160156","doi":"10.1090/fim/016","title":"Lectures on Monte Carlo Methods","year":2001,"lang":"en","type":"book","venue":"American Mathematical Society eBooks","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Monte Carlo method; Markov chain Monte Carlo; Variance reduction; Statistical physics; Monte Carlo method in statistical physics; Computer science; Markov chain; Variance (accounting); Hybrid Monte Carlo; Ising model; Mathematics; Statistics; Physics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.06093238297592173,"gpt":0.3924025198980453,"spread":0.3314701369221235,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002194629,0.001299893,0.002691009,0.0001180996,0.0002844093,0.0001273582,0.0008812441,0.0007672674,0.0002652165],"category_scores_gemma":[0.00112604,0.001047184,0.002365084,0.0001395159,0.001280938,0.00002987065,0.0003444558,0.001839638,0.000008749505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006489219,"about_ca_system_score_gemma":0.0003770591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002406677,"about_ca_topic_score_gemma":0.000008257468,"domain_scores_codex":[0.9948046,0.0006551581,0.001224441,0.00115577,0.001066719,0.001093331],"domain_scores_gemma":[0.9901524,0.006282186,0.001026245,0.001817762,0.0002032777,0.000518168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006130715,0.0002441577,9.492198e-7,0.00121335,0.001317147,0.00006246277,0.004018649,0.00000326901,0.0001120466,0.2947224,0.5130487,0.1851955],"study_design_scores_gemma":[0.0003934343,0.0004244497,2.235315e-7,0.0005891319,0.000577778,0.00004990229,0.0005279218,0.0007829231,0.0002311781,0.4866451,0.5085753,0.001202633],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000108099,0.0001282487,0.1216955,0.0002318352,0.0002195787,0.001048636,0.00004601989,0.0005065919,0.8760155],"genre_scores_gemma":[0.00003951782,0.0000554771,0.4092728,0.001739247,0.0005694642,0.0001502797,0.00000670899,0.0002946513,0.5878719],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2881436,"threshold_uncertainty_score":0.9999753,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2075954168","doi":"10.1007/s11009-006-8550-0","title":"An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift","year":2006,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":160,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Metropolis–Hastings algorithm; Mathematics; Convergence (economics); Algorithm; Random walk; Langevin dynamics; Mathematical optimization; Markov chain Monte Carlo; Monte Carlo method; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1348409524048685,"gpt":0.3571457967899159,"spread":0.2223048443850474,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007028067,0.000214012,0.0004781443,0.00007019557,0.00022793,0.00001996985,0.000195007,0.0001856055,0.000004454439],"category_scores_gemma":[0.0003469664,0.0001374683,0.0000539592,0.0002572062,0.0002690874,0.00002491427,0.00007592421,0.0002832462,3.709938e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006309908,"about_ca_system_score_gemma":0.00003760016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003466734,"about_ca_topic_score_gemma":0.0004447274,"domain_scores_codex":[0.9973277,0.001377742,0.0003476886,0.0004970298,0.0001133271,0.0003364383],"domain_scores_gemma":[0.9916878,0.007609726,0.0001690531,0.0004003224,0.00008642667,0.00004660841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003528342,0.0008774785,0.006549399,0.0004429031,0.0001917826,0.000007826222,0.002739098,0.001669511,0.002141728,0.4646963,0.0004817501,0.5166739],"study_design_scores_gemma":[0.006771467,0.001599144,0.02441267,0.00009232078,0.0004536789,0.00004467186,0.004199441,0.3534873,0.006026711,0.5994809,0.002434669,0.0009970411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2262759,0.00009459973,0.7714603,0.00009214371,0.00006831351,0.001058813,0.00001170786,0.00008182889,0.0008564175],"genre_scores_gemma":[0.2516826,0.000001823089,0.748036,0.00006434342,0.00008405804,0.00008102618,0.00001111294,0.00001657283,0.00002254136],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5156769,"threshold_uncertainty_score":0.5605794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146960149","doi":"10.1007/s11222-010-9192-1","title":"Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo","year":2010,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":139,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Parallel tempering; Metropolis–Hastings algorithm; Markov chain Monte Carlo; Monte Carlo method; Scaling; Markov chain; Random walk; Statistical physics; Mathematics; Mathematical optimization; Work (physics); Hybrid Monte Carlo; Applied mathematics; Computer science; Algorithm; Statistics; Physics; Thermodynamics","retraction":null,"screen_n_in":null,"score":{"opus":0.02539699512440044,"gpt":0.334028828772443,"spread":0.3086318336480425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179132,0.0001917432,0.000427356,0.00009333686,0.0001389991,0.0000494276,0.0001493924,0.00009260619,0.00001804696],"category_scores_gemma":[0.0009621762,0.0001754779,0.0000637878,0.000118905,0.0001230866,0.00002707883,0.0001774891,0.0003032557,3.411957e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001440028,"about_ca_system_score_gemma":0.00004848362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002956309,"about_ca_topic_score_gemma":0.00007099527,"domain_scores_codex":[0.9986593,0.00007516014,0.0004616062,0.0002548749,0.0002385191,0.0003106122],"domain_scores_gemma":[0.9984688,0.0006978585,0.0002447778,0.0002614681,0.0002007814,0.0001263051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007270997,0.0001600737,0.001091724,0.0008430083,0.0001881901,0.00005094241,0.003793646,0.0001928948,0.01207285,0.6370547,0.001334443,0.3431448],"study_design_scores_gemma":[0.0008161325,0.000116028,0.0005299135,0.00009578754,0.0001079794,0.00002744705,0.00101084,0.9841341,0.001648177,0.01061511,0.0005098127,0.0003886363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5275712,0.00006421452,0.4707214,0.00002592871,0.0003276041,0.0001123399,0.00007413271,0.00002950737,0.001073734],"genre_scores_gemma":[0.511427,0.000009157909,0.488328,0.00001887835,0.000113981,0.000001365889,0.000002550736,0.00001928781,0.00007983926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9839413,"threshold_uncertainty_score":0.7155783,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2162340617","doi":"10.1198/jasa.2009.tm08393","title":"Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC","year":2009,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":131,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Social Sciences and Humanities Research Council","funders":"","keywords":"Markov chain Monte Carlo; Chain (unit); Computer science; Mathematics; Artificial intelligence; Bayesian probability; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03818152078787884,"gpt":0.3317143763202894,"spread":0.2935328555324106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000972219,0.0001210079,0.0004185662,0.00004079277,0.00009031664,0.00003774853,0.0001445107,0.00004751845,0.00001330773],"category_scores_gemma":[0.002960633,0.00007983703,0.0001182878,0.0001303391,0.00008225912,0.00007202941,0.00003057743,0.000346842,1.543838e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002478432,"about_ca_system_score_gemma":0.00005476316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006059076,"about_ca_topic_score_gemma":0.00002121952,"domain_scores_codex":[0.9981598,0.0005807544,0.000428477,0.0001182688,0.000527737,0.0001849581],"domain_scores_gemma":[0.9952956,0.002888345,0.001382255,0.0001244392,0.0002023339,0.0001069831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001779456,0.0006830039,0.02029571,0.00001808587,0.0009292496,0.00007478034,0.002830081,0.00008208895,0.002599998,0.4424775,0.1625366,0.3656934],"study_design_scores_gemma":[0.001530469,0.001313589,0.2156291,0.0001001364,0.0004169469,0.00003858963,0.001279138,0.004032787,0.00006738515,0.7686715,0.006575422,0.0003448614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4615188,0.0002194744,0.5196953,0.01572131,0.0003250036,0.0002291373,0.0001357589,0.00002575143,0.002129487],"genre_scores_gemma":[0.7179043,0.0001210337,0.2791286,0.001772318,0.0004079688,0.000001399425,0.000002388877,0.00001502741,0.0006469241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3653485,"threshold_uncertainty_score":0.3544368,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084668838","doi":"10.1016/j.spa.2007.12.005","title":"Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234","year":2008,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":105,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Algorithm; Metropolis–Hastings algorithm; Applied mathematics; Mathematical optimization; Statistics; Monte Carlo method; Markov chain Monte Carlo","retraction":null,"screen_n_in":null,"score":{"opus":0.04884437887349389,"gpt":0.3535913551804132,"spread":0.3047469763069193,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002189576,0.0002156498,0.0002986227,0.00007471542,0.0005259431,0.00003867306,0.0002070949,0.00007277232,0.000009287648],"category_scores_gemma":[0.0004371031,0.000170787,0.00006874477,0.0002966353,0.0001663659,0.0001103319,0.00007061732,0.00009719867,1.647079e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002506233,"about_ca_system_score_gemma":0.0001108313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008982441,"about_ca_topic_score_gemma":0.00001222342,"domain_scores_codex":[0.9989768,0.00001567834,0.0002653172,0.000357774,0.00009706456,0.0002873521],"domain_scores_gemma":[0.9981341,0.001014906,0.0001426164,0.0002908476,0.0003010305,0.0001164995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000170568,0.0009072729,0.0000369349,0.003248736,0.0004902949,0.000002460239,0.01089925,0.0001867306,0.004222542,0.8550706,0.005033644,0.119731],"study_design_scores_gemma":[0.00348005,0.0005780368,0.00002955618,0.0002637555,0.0004276586,0.0002841912,0.01457145,0.05311274,0.01532478,0.885357,0.02432676,0.002244073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004478896,0.00156576,0.9914336,0.0001957206,0.00003690408,0.0009957136,0.0001428027,0.000106807,0.001043825],"genre_scores_gemma":[0.6081337,0.0001047306,0.386973,0.000134904,0.00039746,0.003291421,0.00003359541,0.00006409174,0.0008671326],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6044605,"threshold_uncertainty_score":0.696449,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2048759201","doi":"10.1007/s00440-007-0131-9","title":"On the hardness of sampling independent sets beyond the tree threshold","year":2008,"lang":"en","type":"article","venue":"Probability Theory and Related Fields","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":99,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Office of Naval Research; Microsoft Research; National Science Foundation","keywords":"Mathematics; Gibbs measure; Markov chain; Gibbs sampling; Combinatorics; Markov chain Monte Carlo; Discrete mathematics; Bipartite graph; Random graph; Monte Carlo method; Graph; Statistics; Bayesian probability","retraction":null,"screen_n_in":null,"score":{"opus":0.07685186113986163,"gpt":0.3136031688352385,"spread":0.2367513076953768,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004599884,0.0001554531,0.0002438163,0.00002708514,0.000306595,0.00001278892,0.0002493789,0.0002862027,0.00009657355],"category_scores_gemma":[0.001393634,0.00007393066,0.0001348173,0.0001199221,0.0003916896,0.00003658416,0.0001125425,0.0006675489,2.00234e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001430553,"about_ca_system_score_gemma":0.00002836166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004673534,"about_ca_topic_score_gemma":0.00001126906,"domain_scores_codex":[0.998338,0.000683639,0.0003581798,0.0002354626,0.0002038244,0.0001808691],"domain_scores_gemma":[0.9953994,0.003740309,0.0001343165,0.0006148043,0.00006601,0.00004517134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001864818,0.0001330504,0.000288966,0.00007653762,0.00008639112,0.000003837709,0.005275617,0.00002724357,0.00008992921,0.990563,0.0003899742,0.002878924],"study_design_scores_gemma":[0.0002761912,0.00009613181,0.0002822351,0.00004656045,0.00004793728,0.00003438205,0.0004210655,0.0001790475,0.001038485,0.9973421,0.0001316569,0.0001042694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598997,0.0002976738,0.003024221,0.001596466,0.0002765568,0.0005589898,0.000005720378,0.00003758899,0.03430304],"genre_scores_gemma":[0.99765,0.00009219455,0.0006582093,0.000296913,0.00001949704,0.00002379766,0.000001196912,0.00001261785,0.001245632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0377502,"threshold_uncertainty_score":0.3014805,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153811005","doi":"10.1111/j.1467-9868.2005.00500.x","title":"Scaling Limits for the Transient Phase of Local Metropolis–Hastings Algorithms","year":2005,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":94,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metropolis–Hastings algorithm; Convergence (economics); Algorithm; Random walk; Transient (computer programming); Scaling; Path (computing); Trajectory; Langevin dynamics; Computer science; Statistical physics; Mathematics; Physics; Monte Carlo method; Statistics; Markov chain Monte Carlo","retraction":null,"screen_n_in":null,"score":{"opus":0.1757608270161161,"gpt":0.4326136336505182,"spread":0.2568528066344021,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008196622,0.0003882326,0.001244471,0.00004067961,0.000436638,0.00004967374,0.0007593518,0.000277663,0.0001588979],"category_scores_gemma":[0.02325959,0.0002128547,0.0008497993,0.0002538555,0.001818737,0.00009725053,0.0001357293,0.0009470602,1.718226e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001945535,"about_ca_system_score_gemma":0.00018661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003653993,"about_ca_topic_score_gemma":0.00002169566,"domain_scores_codex":[0.9947593,0.001654558,0.001779363,0.0003252066,0.0007922963,0.0006892796],"domain_scores_gemma":[0.950746,0.04694314,0.0009349091,0.000384781,0.0006798537,0.0003113097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00201778,0.0008286929,0.00001577328,0.0005860336,0.001142953,0.00001684267,0.002174525,0.001513367,0.001095785,0.5367935,0.04714712,0.4066676],"study_design_scores_gemma":[0.01467094,0.006525739,0.0007752329,0.0005512995,0.007473829,0.0006202105,0.01489978,0.4187815,0.01897976,0.3809687,0.1341242,0.001628777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00213176,0.000455705,0.991588,0.003437447,0.00099211,0.0005061511,0.0007355589,0.00002005182,0.0001331944],"genre_scores_gemma":[0.03064375,0.00006398525,0.9675314,0.000598531,0.0007957929,0.00002317411,0.000004357297,0.00005692779,0.0002821113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4172682,"threshold_uncertainty_score":0.9849679,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2168173277","doi":"10.1002/1098-2418(200010/12)17:3/4<290::aid-rsa6>3.0.co;2-q","title":"Extension of Fill's perfect rejection sampling algorithm to general chains","year":2000,"lang":"en","type":"article","venue":"Random Structures and Algorithms","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Western University","funders":"","keywords":"Extension (predicate logic); Randomness; Bounding overwatch; Algorithm; Sampling (signal processing); Computer science; Simple (philosophy); Connection (principal bundle); Rejection sampling; Mathematics; Theoretical computer science; Artificial intelligence; Statistics; Programming language; Hybrid Monte Carlo; Markov chain Monte Carlo","retraction":null,"screen_n_in":null,"score":{"opus":0.04046938814774089,"gpt":0.3395489419876489,"spread":0.299079553839908,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006385744,0.0002536378,0.0005690079,0.0001219987,0.0001796251,0.00003852007,0.0001052134,0.0001327128,0.000176127],"category_scores_gemma":[0.0001787925,0.0001951812,0.000162063,0.000185082,0.00005350392,0.00006152897,0.00004764609,0.0001646012,1.141227e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002375655,"about_ca_system_score_gemma":0.00001753203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001485929,"about_ca_topic_score_gemma":0.00001220187,"domain_scores_codex":[0.9984624,0.0001616938,0.0004049907,0.0004037326,0.000262874,0.0003043303],"domain_scores_gemma":[0.9990152,0.0003046682,0.0001015598,0.0003212453,0.00009531929,0.0001620195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002514774,0.00002692313,0.00001459654,0.00007082807,0.00005062634,0.000006580431,0.0006753643,0.0001640949,0.005434833,0.001516063,0.0004519765,0.9913366],"study_design_scores_gemma":[0.06237288,0.004463271,0.01065222,0.001647665,0.00135971,0.001738772,0.002552665,0.5120035,0.1159827,0.140608,0.1405776,0.00604104],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8030767,0.0006661806,0.193668,0.0001281949,0.0004751843,0.0006532623,0.00007440277,0.00008991308,0.001168094],"genre_scores_gemma":[0.1106195,0.0007827994,0.8841713,0.0002612307,0.001050102,0.0000350685,0.00002146765,0.00006983027,0.002988707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9852956,"threshold_uncertainty_score":0.7959259,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3099541808","doi":"10.6084/m9.figshare.1581629.v2","title":"Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":81,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Markov chain Monte Carlo; Mathematics; Applied mathematics; Mathematical optimization; Inference; Ergodicity; Approximate inference; Markov chain; Convergence (economics); Monte Carlo method; Algorithm; Computer science; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1740633116752489,"gpt":0.3590485212035675,"spread":0.1849852095283185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001830427,0.0001832057,0.0002906591,0.0000634993,0.0001022687,0.00007404214,0.000201172,0.0001270263,0.006973931],"category_scores_gemma":[0.002337906,0.0001693558,0.0001590536,0.0001023798,0.000008120125,0.0002046309,0.0001080863,0.0001616637,0.0000174443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006186606,"about_ca_system_score_gemma":0.00009239835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001360319,"about_ca_topic_score_gemma":0.00000528512,"domain_scores_codex":[0.9987946,0.00006389606,0.000344935,0.0002995617,0.0002286558,0.0002683771],"domain_scores_gemma":[0.9964921,0.001916941,0.0001577949,0.0002668175,0.001071294,0.00009508424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001668601,0.0004344613,0.00002753509,0.005357407,0.0004405706,0.00001733562,0.004329496,0.04638538,0.001101263,0.2618597,0.6088325,0.07104751],"study_design_scores_gemma":[0.0005640757,0.00008385069,0.00001749566,0.0006421218,0.00001856552,0.0000110087,0.0003333581,0.9559993,0.0002831377,0.03857492,0.003209675,0.0002624716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001427019,0.00003845691,0.9441794,0.0002863663,0.0001358855,0.001877269,0.03643312,0.0001978872,0.01542457],"genre_scores_gemma":[0.3588929,5.800932e-7,0.5861325,0.001778921,0.0003283284,0.0009721979,0.04986035,0.0001348753,0.001899363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.909614,"threshold_uncertainty_score":0.9939339,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1737070927","doi":"10.1080/01621459.2015.1096787","title":"Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations","year":2015,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":80,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Office of Science; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Markov chain Monte Carlo; Ergodicity; Importance sampling; Inference; Monte Carlo method; Convergence (economics); Gaussian process; Ode; Rejection sampling","retraction":null,"screen_n_in":null,"score":{"opus":0.1125788164123556,"gpt":0.3749705992627148,"spread":0.2623917828503592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002401521,0.0001454401,0.0005060807,0.00006943247,0.0001271065,0.00007191311,0.0002357574,0.00005610439,0.000003514634],"category_scores_gemma":[0.01592702,0.0001016179,0.0001914818,0.0002179911,0.0001164067,0.000179654,0.00006245836,0.0003026995,2.013756e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007070104,"about_ca_system_score_gemma":0.0002714664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001546987,"about_ca_topic_score_gemma":0.00001322315,"domain_scores_codex":[0.9975649,0.0004971581,0.0007920476,0.0001325637,0.0007643077,0.0002490184],"domain_scores_gemma":[0.989211,0.005487005,0.001857485,0.0001339662,0.003130988,0.0001795286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009181182,0.0007809718,0.002658448,0.0001520666,0.001244278,0.0000165375,0.004768325,0.0344972,0.0007414584,0.7197587,0.1111138,0.1233501],"study_design_scores_gemma":[0.0009971628,0.0004489685,0.001087293,0.00004815039,0.0002418923,0.00003533228,0.001645801,0.5106302,0.0000706631,0.4843037,0.0003137248,0.0001771327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0188743,0.000009053326,0.9771898,0.002781586,0.00030034,0.00026663,0.00007164737,0.00001540048,0.0004911804],"genre_scores_gemma":[0.4481738,0.000001741704,0.5506438,0.0007713342,0.000228495,0.000009949819,0.000006715776,0.00002132112,0.0001428427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.476133,"threshold_uncertainty_score":0.9923623,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2128598176","doi":"10.1111/1467-9469.00250","title":"Markov Chains and De‐initializing Processes","year":2001,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain; Initialization; Markov chain Monte Carlo; Markov chain mixing time; Mathematics; Convergence (economics); Variable-order Markov model; Examples of Markov chains; Markov process; Additive Markov chain; Markov model; Markov property; Applied mathematics; Markov kernel; Monte Carlo method; Mathematical optimization; Computer science; Statistics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.06189568995941064,"gpt":0.3586947562524259,"spread":0.2967990662930153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00100742,0.0001450021,0.0003283998,0.0001378005,0.0001067161,0.00006683666,0.0001311409,0.00006097089,0.00004420651],"category_scores_gemma":[0.002023968,0.0001226534,0.00004161417,0.0001699689,0.00009260129,0.0001029635,0.00003059825,0.0002156302,4.802466e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000618077,"about_ca_system_score_gemma":0.0001395267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000592326,"about_ca_topic_score_gemma":0.00002222142,"domain_scores_codex":[0.9988048,0.0001230731,0.0004563262,0.0001030484,0.0002433542,0.0002693839],"domain_scores_gemma":[0.9981678,0.0007208076,0.0004011133,0.0001104346,0.0003715649,0.000228314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001194704,0.0007110594,0.1136879,0.004285415,0.0006609977,0.007633507,0.0168546,0.00001271083,0.001879458,0.3226856,0.06967953,0.4607146],"study_design_scores_gemma":[0.01106428,0.004312531,0.01266537,0.006076185,0.001754614,0.03122428,0.01488369,0.00304676,0.002771626,0.8404393,0.06911275,0.002648679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1347057,0.0009706143,0.8589216,0.0002621738,0.0003072325,0.0001224309,0.00008899599,0.00001834399,0.004602876],"genre_scores_gemma":[0.458085,0.002055462,0.5383376,0.000133371,0.0004002601,0.000002282491,0.000002178168,0.00003725172,0.0009465914],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5177537,"threshold_uncertainty_score":0.5001659,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979619742","doi":"10.1007/s11222-012-9373-1","title":"Split Hamiltonian Monte Carlo","year":2013,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":69,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Monte Carlo method; Hamiltonian (control theory); Gaussian; Mathematics; Statistical physics; Applied mathematics; Quadratic equation; Hybrid Monte Carlo; Hamiltonian mechanics; Computation; Mathematical optimization; Algorithm; Physics; Markov chain Monte Carlo; Quantum mechanics; Statistics; Phase space; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.03487638221484857,"gpt":0.3229034758441773,"spread":0.2880270936293287,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003017674,0.000133814,0.000211358,0.00003554089,0.0001457425,0.0001028537,0.00008269768,0.00004440666,0.00003065814],"category_scores_gemma":[0.0002351416,0.0001189151,0.00002721447,0.0000552907,0.00004591866,0.00003228493,0.0001085198,0.0001210098,6.123637e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001510126,"about_ca_system_score_gemma":0.00001508521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002197395,"about_ca_topic_score_gemma":0.00003311995,"domain_scores_codex":[0.9991164,0.0000621013,0.0002444505,0.0001987717,0.0001240314,0.0002542973],"domain_scores_gemma":[0.998997,0.0005080716,0.00009028182,0.0001856251,0.000106205,0.0001127831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004153858,0.00004646242,0.001509895,0.0002576193,0.00005505088,0.00002454279,0.001989952,0.00001861237,0.0002844479,0.499785,0.02592351,0.4701007],"study_design_scores_gemma":[0.001271904,0.0002429206,0.01008053,0.0002479223,0.0001147207,0.00006636616,0.001858957,0.6961021,0.0001559181,0.2702886,0.01849522,0.001074748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4771444,0.0001854237,0.5127135,0.0001383811,0.0002853058,0.0002967179,0.00004620867,0.00008685474,0.009103265],"genre_scores_gemma":[0.4845373,0.00001224948,0.5141945,0.0001135273,0.00009143423,0.000004161339,0.000001697075,0.00001835127,0.001026763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6960835,"threshold_uncertainty_score":0.4849218,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2977245197","doi":"10.1111/rssb.12464","title":"Non-Reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Parallel tempering; Markov chain Monte Carlo; Computer science; Markov chain; Scalability; Prior probability; Scaling; Piecewise; Algorithm; Range (aeronautics); Applied mathematics; Schedule; Simulated annealing; Distribution (mathematics); Mathematical optimization; Statistical physics; Mathematics; Bayesian probability; Hybrid Monte Carlo; Artificial intelligence; Physics; Materials science; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.09901716239151234,"gpt":0.3612658349096236,"spread":0.2622486725181112,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004881768,0.000535173,0.001612367,0.00004357626,0.0005200792,0.0001487882,0.0007917576,0.0004724543,0.000826855],"category_scores_gemma":[0.02060581,0.0003730808,0.0008145639,0.0004350192,0.0009809408,0.0001736844,0.0006133054,0.001561045,0.000002696098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000262408,"about_ca_system_score_gemma":0.0005588543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004779077,"about_ca_topic_score_gemma":0.00002475595,"domain_scores_codex":[0.9936337,0.002238241,0.001622101,0.000562757,0.0009697048,0.0009734456],"domain_scores_gemma":[0.9856582,0.01145046,0.0007642623,0.0006939822,0.0008189589,0.0006141245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001012051,0.0006660595,0.001089074,0.0008924954,0.001254557,0.0006471642,0.001043407,0.0004633317,0.003128672,0.7391499,0.2464904,0.004162912],"study_design_scores_gemma":[0.00681659,0.00171338,0.005371195,0.0005801109,0.002032059,0.001459339,0.004762959,0.02218142,0.004326745,0.8237901,0.1250003,0.001965758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006662536,0.0003407865,0.9855716,0.003539912,0.001443291,0.0002927312,0.0002370216,0.00004404309,0.001868133],"genre_scores_gemma":[0.004239119,0.0001377245,0.9878033,0.0009849616,0.0004846051,0.00001966799,0.00001114635,0.000077663,0.006241818],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1214901,"threshold_uncertainty_score":0.9998721,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1968782267","doi":"10.1002/cjs.5550360401","title":"Optimal scaling of Metropolis algorithms: Heading toward general target distributions","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"University of Toronto; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Heading (navigation); Simple (philosophy); Scaling; Independent and identically distributed random variables; Gaussian; Algorithm; Distribution (mathematics); Asymptotically optimal algorithm; Computer science; Mathematics; Mathematical optimization; Statistical physics; Applied mathematics; Random variable; Statistics; Mathematical analysis; Geometry; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.09391328041418588,"gpt":0.3312765790653092,"spread":0.2373632986511233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006038198,0.0001487811,0.0004590616,0.000271686,0.000192498,0.0000222254,0.0002127039,0.00007878867,0.00009901622],"category_scores_gemma":[0.001676544,0.000141674,0.0001342456,0.0002083342,0.0002196784,0.00008128808,0.00001465643,0.0002696603,9.996607e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002862773,"about_ca_system_score_gemma":0.001186232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002112891,"about_ca_topic_score_gemma":0.0008939753,"domain_scores_codex":[0.9984569,0.0001119887,0.0007082264,0.0001015557,0.0002495016,0.0003718499],"domain_scores_gemma":[0.9977834,0.0003274938,0.0004195821,0.0001554375,0.0006244195,0.0006897292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009144593,0.0002468949,0.01254412,0.0006869725,0.001067754,0.005506177,0.01287535,0.002974241,0.001363246,0.6157585,0.3285169,0.01836832],"study_design_scores_gemma":[0.01709078,0.005611602,0.01319519,0.003036597,0.004004675,0.02279791,0.02481069,0.1971476,0.06628799,0.2776954,0.3607969,0.007524664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08505029,0.0003932927,0.9112486,0.0001256659,0.0004733017,0.00006776381,0.002193364,0.000004745452,0.0004429825],"genre_scores_gemma":[0.2180873,0.00005737363,0.7813322,0.00002980999,0.0002619315,9.075584e-7,0.00002028874,0.00002151926,0.0001886929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3380632,"threshold_uncertainty_score":0.5777298,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2071768380","doi":"10.1214/009117906000000034","title":"Subtree prune and regraft: A reversible real tree-valued Markov process","year":2006,"lang":"en","type":"article","venue":"The Annals of Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":57,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Division of Mathematical Sciences; Pacific Institute for the Mathematical Sciences; National Science Foundation","keywords":"Markov chain; Dirichlet distribution; Markov process; Markov property; Hierarchical Dirichlet process; Dirichlet process; Variable-order Markov model; Path (computing); Brownian motion","retraction":null,"screen_n_in":null,"score":{"opus":0.1430059525250482,"gpt":0.3894167694051828,"spread":0.2464108168801347,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004098632,0.0001984458,0.0004274118,0.00004590469,0.0001153255,0.00002235707,0.0002944754,0.0001032101,0.00001863796],"category_scores_gemma":[0.0008924244,0.0001301286,0.000144401,0.0002511546,0.000324481,0.0001033039,0.0001147107,0.000160411,8.463953e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001701422,"about_ca_system_score_gemma":0.00006560935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004758287,"about_ca_topic_score_gemma":0.000715408,"domain_scores_codex":[0.9981003,0.0004447716,0.0004941185,0.000339641,0.0003062688,0.0003149244],"domain_scores_gemma":[0.998043,0.0004887569,0.0002792607,0.000791973,0.0003279015,0.00006913058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004215366,0.00477674,0.07107893,0.01361226,0.0005384568,0.00004613318,0.009913674,0.00003537403,0.01203205,0.6917757,0.1039064,0.0880689],"study_design_scores_gemma":[0.0007317213,0.0002341751,0.009031096,0.0001434937,0.00009703364,0.0000153337,0.0002900125,0.0005879311,0.01449256,0.973026,0.001038466,0.0003121895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9660348,0.0002676539,0.0005489076,0.002331534,0.00004222653,0.000808212,0.00002254991,0.00007028632,0.02987386],"genre_scores_gemma":[0.9624584,0.00009485325,0.03382422,0.0001039846,0.0001269941,0.00006578411,0.000005548571,0.00003026096,0.003290007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2812502,"threshold_uncertainty_score":0.5306491,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W84569508","doi":"","title":"Adaptive MCMC with Bayesian Optimization","year":2012,"lang":"en","type":"article","venue":"","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"D-Wave Systems (Canada); University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Probabilistic logic; Markov chain; Mathematical optimization; Bayesian probability; Adaptive sampling; Bayesian optimization; Sampling (signal processing); Graphical model; Markov process; Machine learning; Artificial intelligence; Mathematics; Monte Carlo method; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.06814830895941162,"gpt":0.3270986019886887,"spread":0.2589502930292771,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003385393,0.00009079929,0.0001173372,0.00003322838,0.00004019231,0.00001046223,0.00005023524,0.00004389976,0.0002287574],"category_scores_gemma":[0.00006868009,0.00005989225,0.00002777889,0.00008891842,0.0000189658,0.0001329872,0.00001974819,0.00005380293,1.946582e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002179513,"about_ca_system_score_gemma":0.00001170643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008751431,"about_ca_topic_score_gemma":0.00001239476,"domain_scores_codex":[0.9994364,0.00006398986,0.0001006323,0.00008817937,0.0001141377,0.0001967031],"domain_scores_gemma":[0.9994959,0.0001321382,0.00004596551,0.0001783896,0.00005013281,0.00009747387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001786375,0.0004684844,0.003870575,0.00008883839,0.0001495813,0.00000598729,0.003660809,0.00145059,0.0001269444,0.9554961,0.01592321,0.01858027],"study_design_scores_gemma":[0.01003639,0.002753207,0.0009413901,0.0005649145,0.001172803,0.0003914436,0.027512,0.8164188,0.02824532,0.02858759,0.07792499,0.00545112],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001271784,0.0000249984,0.8228217,0.00006473256,0.00005891644,0.0001250009,9.347251e-7,0.00007118766,0.1755607],"genre_scores_gemma":[0.2262989,0.00000333379,0.7685828,0.0001005781,0.0001023905,0.00001279679,0.000001456488,0.00001805203,0.004879648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9269085,"threshold_uncertainty_score":0.2504733,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1624558029","doi":"","title":"Adaptive Hamiltonian and Riemann Manifold Monte Carlo","year":2013,"lang":"en","type":"article","venue":"","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Monte Carlo method; Hybrid Monte Carlo; Computer science; Hamiltonian (control theory); Statistical physics; Mathematics; Applied mathematics; Physics; Topology (electrical circuits); Mathematical optimization; Markov chain Monte Carlo; Combinatorics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.05936358466552672,"gpt":0.3039147555494982,"spread":0.2445511708839714,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000286322,0.000169907,0.0002456692,0.00005091858,0.00007317963,0.00005791852,0.0001035326,0.00008709064,0.0001957096],"category_scores_gemma":[0.0001200141,0.0001292309,0.00006399252,0.00006381476,0.00003840715,0.0001374461,0.0001020408,0.0001145828,0.000001980428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002245971,"about_ca_system_score_gemma":0.00001227308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006169167,"about_ca_topic_score_gemma":0.0003376897,"domain_scores_codex":[0.9990697,0.00007849919,0.0002084331,0.0002465591,0.0001418196,0.0002549963],"domain_scores_gemma":[0.9991395,0.0002375596,0.00005899739,0.0003188575,0.00009509323,0.000149945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004318311,0.0002125548,0.002082627,0.0002457747,0.0002582957,0.000055047,0.00441274,0.000002982268,0.002199043,0.7682014,0.1615632,0.06072323],"study_design_scores_gemma":[0.01093125,0.002818384,0.02889411,0.0007905163,0.0008847754,0.000526396,0.04339266,0.1448307,0.01395697,0.5362493,0.2095821,0.00714275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6688017,0.0003872584,0.01695137,0.001015765,0.0002421505,0.001007127,0.000009236602,0.0002788751,0.3113066],"genre_scores_gemma":[0.7977915,0.00003636939,0.1574453,0.0004092502,0.00008792018,0.00007185927,4.047781e-7,0.0000337108,0.04412362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2671829,"threshold_uncertainty_score":0.5269882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2038604769","doi":"10.1016/j.spa.2011.11.004","title":"Scaling analysis of multiple-try MCMC methods","year":2011,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":53,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Markov chain Monte Carlo; Metropolis–Hastings algorithm; Implementation; Markov chain; Scaling; Set (abstract data type); Mathematics; Algorithm; Mathematical optimization; Theoretical computer science; Computer science; Monte Carlo method; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1025433624569132,"gpt":0.381014057591398,"spread":0.2784706951344847,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005759127,0.0001573818,0.0004137006,0.0002047564,0.0001181515,0.00001128672,0.0001825804,0.00007164665,0.00002040124],"category_scores_gemma":[0.0007251719,0.0001194459,0.0001095553,0.0009756552,0.0001172816,0.00004556955,0.00007801045,0.00008622133,5.138222e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008507101,"about_ca_system_score_gemma":0.00004519535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003258733,"about_ca_topic_score_gemma":0.00003795309,"domain_scores_codex":[0.9990848,0.00005451992,0.0003438088,0.0002734036,0.00007647004,0.0001669745],"domain_scores_gemma":[0.9974392,0.00163303,0.000214083,0.0003813744,0.0002414945,0.00009082012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001112142,0.0009993355,0.000382458,0.00225008,0.003540513,4.843469e-7,0.02426014,0.00006539631,0.01030958,0.7113088,0.00004266228,0.2467293],"study_design_scores_gemma":[0.001378127,0.0001984731,0.0005111087,0.0002796778,0.007400875,0.00001813576,0.01846801,0.08531762,0.03009684,0.8537366,0.001159292,0.001435277],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006006008,0.0005690854,0.9903812,0.00001455586,0.00001693376,0.0003566729,0.00005072129,0.00005801102,0.00254681],"genre_scores_gemma":[0.6958604,0.00001237068,0.3037392,0.0000175553,0.00002404593,0.0002722372,0.000007982667,0.00001552552,0.00005062165],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6898544,"threshold_uncertainty_score":0.4870862,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047000577","doi":"10.1239/jap/1044476831","title":"On the geometric ergodicity of hybrid samplers","year":2003,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Ergodicity; Random walk; Metropolis–Hastings algorithm; Statistical physics; Applied mathematics; Statistics; Markov chain Monte Carlo; Monte Carlo method","retraction":null,"screen_n_in":null,"score":{"opus":0.08652925242123094,"gpt":0.3120676138887566,"spread":0.2255383614675257,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007310641,0.000148402,0.0004697998,0.0001392937,0.00006736736,0.00001381974,0.0002812478,0.0000488073,0.0001201214],"category_scores_gemma":[0.005519801,0.00008583424,0.000253926,0.0003982911,0.0001353752,0.0000319205,0.00003062712,0.0004004145,1.234836e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080936,"about_ca_system_score_gemma":0.0001189602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002209025,"about_ca_topic_score_gemma":0.000002080244,"domain_scores_codex":[0.9980879,0.0002937974,0.0007791925,0.0001462047,0.000496196,0.0001967403],"domain_scores_gemma":[0.9951238,0.003267838,0.0007689262,0.0004810767,0.000262848,0.00009552495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00046144,0.0008300972,0.0005263409,0.0002771428,0.0001484188,0.00000557494,0.0003467963,0.0002068527,0.001365402,0.9829672,0.006731113,0.006133647],"study_design_scores_gemma":[0.0006719281,0.0002669926,0.0001951917,0.00003621119,0.00007618643,0.0000314774,0.0001916437,0.00002868314,0.04789209,0.9468125,0.00366229,0.0001347928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.950513,0.00004107539,0.01990299,0.0001576365,0.0001610461,0.0003818391,0.000006401204,0.000008298817,0.02882773],"genre_scores_gemma":[0.941413,0.00001216702,0.05836876,0.0001195146,0.00003955119,0.000006740823,1.543055e-7,0.0000124569,0.00002759211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04652669,"threshold_uncertainty_score":0.6608116,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1488446430","doi":"10.1002/rsa.20539","title":"The mixing time of the giant component of a random graph","year":2014,"lang":"en","type":"article","venue":"Random Structures and Algorithms","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Expander graph; Mixing (physics); Random graph; Random walk; Mathematics; Combinatorics; Constant (computer programming); Vertex (graph theory); Component (thermodynamics); Exponential function; Discrete mathematics; Graph; Statistical physics; Computer science; Physics; Statistics; Mathematical analysis; Quantum mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.01511420349440316,"gpt":0.2716785362266982,"spread":0.256564332732295,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001427528,0.0001618978,0.0005154129,0.0000327968,0.0002189628,0.00001868114,0.0002488742,0.00006473633,0.000008567969],"category_scores_gemma":[0.0004881337,0.00007276821,0.00025071,0.00009901488,0.0002624236,0.00002016982,0.0001079374,0.0001336291,1.261656e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005250579,"about_ca_system_score_gemma":0.00001322181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003816939,"about_ca_topic_score_gemma":0.000009198062,"domain_scores_codex":[0.998519,0.0004387996,0.0004274074,0.0001555598,0.0002706049,0.0001885981],"domain_scores_gemma":[0.9970578,0.002107231,0.0003104152,0.0003960452,0.00007836831,0.00005011749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005534273,0.0002303415,0.0006809513,0.001574323,0.001523297,0.000006426782,0.009032185,0.0002318621,0.1401388,0.2512047,0.004971961,0.5848708],"study_design_scores_gemma":[0.08240771,0.0004996967,0.005973731,0.0008371544,0.001052658,0.000168462,0.001575504,0.1091354,0.1160911,0.659934,0.02109858,0.001226088],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9009304,0.002879868,0.08960737,0.0007617342,0.001004834,0.001493723,0.00006687341,0.00004640287,0.003208774],"genre_scores_gemma":[0.9732144,0.0002353489,0.02582978,0.00006859146,0.0001877655,0.0000161355,0.000002022892,0.00002863347,0.0004173581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5836447,"threshold_uncertainty_score":0.2967401,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1497401739","doi":"10.1137/130930339","title":"An Iterative Minimization Formulation for Saddle Point Search","year":2015,"lang":"en","type":"article","venue":"SIAM Journal on Numerical Analysis","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Saddle point; Minification; Iterative method; Rate of convergence; Applied mathematics; Mathematical optimization; Quadratic equation; Local convergence; Manifold (fluid mechanics); Saddle; Computer science; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.1243300050969161,"gpt":0.4196921434342967,"spread":0.2953621383373806,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001569104,0.0001589996,0.0004422548,0.000390204,0.0001726864,0.0001399521,0.0001505732,0.00008235534,0.00008142283],"category_scores_gemma":[0.0007122639,0.0001168877,0.0004033669,0.0007140146,0.00001341235,0.0002692523,0.00001741745,0.0002025062,5.537188e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000166896,"about_ca_system_score_gemma":0.0000598754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007072393,"about_ca_topic_score_gemma":0.000006984639,"domain_scores_codex":[0.9981331,0.0004397617,0.0004712736,0.0002259134,0.000474171,0.0002558272],"domain_scores_gemma":[0.9980506,0.0004421461,0.0002375396,0.0002417995,0.0006368953,0.0003910227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007615874,0.007621977,0.02037767,0.0003052817,0.01384401,0.0001739674,0.04309518,0.290278,0.003683359,0.2803775,0.04012042,0.2925068],"study_design_scores_gemma":[0.001538913,0.001660905,0.0002443952,0.00002706968,0.001167629,0.00002196096,0.001550841,0.9607678,0.001094247,0.02803364,0.00353589,0.0003566942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06110302,0.00002937596,0.9370347,0.0006564494,0.00009579492,0.0001780758,0.00001299553,0.00002766413,0.0008619382],"genre_scores_gemma":[0.7922572,0.000008454909,0.2065701,0.0002482927,0.0003368855,0.00001362405,0.00003971086,0.0000226447,0.0005030087],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7311542,"threshold_uncertainty_score":0.4766543,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042221696","doi":"10.1007/s11222-008-9051-5","title":"Metropolis–Hastings algorithms with adaptive proposals","year":2008,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Marsden Fund; Royal Society","keywords":"Metropolis–Hastings algorithm; Markov chain Monte Carlo; Rejection sampling; Algorithm; Markov chain; Gibbs sampling; Computer science; Convergence (economics); Sampling (signal processing); Mathematics; Univariate; Monte Carlo method; Mathematical optimization; Artificial intelligence; Hybrid Monte Carlo; Bayesian probability; Machine learning; Statistics; Multivariate statistics; Filter (signal processing)","retraction":null,"screen_n_in":null,"score":{"opus":0.07367654801908341,"gpt":0.3306847149384586,"spread":0.2570081669193752,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448445,0.0001710819,0.000287419,0.00005426895,0.0003066283,0.00003020035,0.00007594837,0.0000395621,0.000006004313],"category_scores_gemma":[0.0002455052,0.0001309395,0.00002189127,0.0001203865,0.0001596192,0.00003158637,0.00008420494,0.000152766,9.479433e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002304364,"about_ca_system_score_gemma":0.00005128267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007264117,"about_ca_topic_score_gemma":0.00001784122,"domain_scores_codex":[0.9989434,0.0000781176,0.0002364416,0.0002513297,0.0002135875,0.0002770579],"domain_scores_gemma":[0.9987751,0.0006583171,0.0001445828,0.0001423931,0.0001719211,0.0001077174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009672727,0.0001747261,0.00248734,0.0002757079,0.0002114663,0.0004895938,0.005340903,0.00003275068,0.0001818514,0.8032398,0.007786617,0.1796825],"study_design_scores_gemma":[0.005219639,0.003377576,0.002161072,0.0007770792,0.0004409889,0.002098416,0.007246378,0.8164572,0.002040048,0.1520148,0.005505188,0.002661697],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06476201,0.00008272879,0.9310781,0.00002204091,0.00007608564,0.0001740084,0.00004925569,0.00006411764,0.003691654],"genre_scores_gemma":[0.2773771,0.00001322241,0.7220798,0.00004680071,0.0001027378,0.00000249508,0.00000346415,0.0000234414,0.0003508755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8164244,"threshold_uncertainty_score":0.5339559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078014848","doi":"10.1214/11-aap806","title":"Adaptive Gibbs samplers and related MCMC methods","year":2013,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; University of Toronto","keywords":"Gibbs sampling; Convergence (economics); Markov chain Monte Carlo; Selection (genetic algorithm); Set (abstract data type); Markov chain","retraction":null,"screen_n_in":null,"score":{"opus":0.2503156941519951,"gpt":0.4202935983879869,"spread":0.1699779042359917,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053728,0.0002063769,0.000464125,0.00003893613,0.0001068714,0.00002228724,0.0002747649,0.0001315722,0.0001396773],"category_scores_gemma":[0.0007575082,0.0001314605,0.0001261073,0.0002018931,0.0004346712,0.00006950264,0.0002012379,0.0002499632,6.070173e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001318682,"about_ca_system_score_gemma":0.00003475726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009799259,"about_ca_topic_score_gemma":0.000008029573,"domain_scores_codex":[0.9981203,0.000529027,0.0005236425,0.0003370138,0.0001888306,0.0003011972],"domain_scores_gemma":[0.9964309,0.002155407,0.0002739984,0.0008019806,0.0002286484,0.0001090779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002804892,0.0002316656,0.00006339401,0.0003321853,0.0002654015,3.488185e-7,0.005631213,0.00002196117,0.006421743,0.7190751,0.004788549,0.262888],"study_design_scores_gemma":[0.0002288059,0.00007756406,0.0005637922,0.00001593797,0.0000383561,0.00000204661,0.0005993066,0.000578624,0.01128972,0.9854057,0.001039979,0.0001601279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.857923,0.000348769,0.02071718,0.003327734,0.0001198269,0.003216836,0.0000214052,0.0001587704,0.1141665],"genre_scores_gemma":[0.534506,0.00005134683,0.4646283,0.0002823817,0.00002772385,0.0002024636,0.000001859222,0.00002751153,0.0002724075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4439111,"threshold_uncertainty_score":0.5360804,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2913888525","doi":"10.1017/s0963548306007541","title":"Rayleigh Matroids","year":2006,"lang":"en","type":"article","venue":"Combinatorics Probability Computing","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Matroid; Graphic matroid; Combinatorics; Binary number; Matroid partitioning; Mathematics; Class (philosophy); Property (philosophy); Discrete mathematics; Computer science; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.05178645719083579,"gpt":0.3178471076443546,"spread":0.2660606504535188,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002286486,0.0002721234,0.0004399732,0.00008252117,0.0002654343,0.00007712124,0.0003688894,0.0001476331,0.00001341289],"category_scores_gemma":[0.0008148932,0.0002633301,0.0001968953,0.0004344527,0.00009232426,0.00007933746,0.000293719,0.0003201345,6.74126e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002476793,"about_ca_system_score_gemma":0.00005938292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004997266,"about_ca_topic_score_gemma":0.000008522973,"domain_scores_codex":[0.9976681,0.0003097416,0.0006489556,0.0004979342,0.0003639922,0.0005112864],"domain_scores_gemma":[0.9977181,0.0009601904,0.0002239119,0.0007314576,0.0002649571,0.0001013829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007630762,0.0003902621,0.008946351,0.0002516265,0.00001218783,0.00000773325,0.0001146331,0.00003905822,0.0003010131,0.9845838,0.002748668,0.002597024],"study_design_scores_gemma":[0.0006021671,0.00006056575,0.0007875935,0.00004053115,0.00002423298,0.00001288898,0.00003116061,0.003931907,0.001421111,0.9892208,0.003536867,0.0003301636],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8720347,0.0001736085,0.08219534,0.0001934767,0.0009823213,0.0005627826,0.000002811812,0.0005335126,0.04332144],"genre_scores_gemma":[0.907667,0.000001420495,0.09135647,0.0000363452,0.0002192999,0.000009312711,0.000005256998,0.00003989824,0.0006649599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04265648,"threshold_uncertainty_score":0.9999819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3098551378","doi":"","title":"Invariant Gaussian processes and independent sets on regular graphs of large Girth","year":2015,"lang":"en","type":"article","venue":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Combinatorics; Mathematics education","retraction":null,"screen_n_in":null,"score":{"opus":0.05510388448335679,"gpt":0.3059500311985111,"spread":0.2508461467151543,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002167992,0.0004096848,0.0009149522,0.0003437511,0.0003465356,0.00005426108,0.003331748,0.0005182388,0.00001303944],"category_scores_gemma":[0.0005153494,0.0002455173,0.0003593179,0.001429676,0.002057835,0.00199519,0.001728486,0.0008606473,7.897757e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001380098,"about_ca_system_score_gemma":0.0004787652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007406766,"about_ca_topic_score_gemma":2.836547e-7,"domain_scores_codex":[0.9948677,0.001103595,0.00140911,0.0006356263,0.001501204,0.0004827644],"domain_scores_gemma":[0.9962314,0.0007355843,0.00215718,0.0005606744,0.00003622259,0.0002789566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008942742,0.00102542,0.06253713,0.004372934,0.0004829889,0.000005736726,0.006352838,0.0001523697,0.05412453,0.8381509,0.03086876,0.001032074],"study_design_scores_gemma":[0.001292104,0.0003638986,0.01595283,0.001955194,0.0001948951,0.0000829707,0.00109199,0.0004165947,0.7857735,0.1886771,0.00375955,0.0004393918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625721,0.00249394,0.00003484483,0.01494769,0.0004164002,0.001191235,0.0001227651,0.00009221494,0.01812882],"genre_scores_gemma":[0.9859414,0.0001817985,0.009960583,0.0007656931,0.000116175,0.00001292785,0.00000115945,0.00005877362,0.002961503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7316489,"threshold_uncertainty_score":0.9999997,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1992601594","doi":"10.1080/10618600.2015.1005213","title":"Tweedie’s Compound Poisson Model With Grouped Elastic Net","year":2015,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Poisson distribution; Mathematics; Library science; Statistics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.06901214203725027,"gpt":0.3349939212535888,"spread":0.2659817792163386,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006524216,0.00014745,0.0003545123,0.0001195915,0.00006786632,0.00005363957,0.00009405854,0.00005965171,0.00000276421],"category_scores_gemma":[0.000414783,0.0001011414,0.00004860521,0.0001355119,0.000142674,0.00008820045,0.00002926439,0.0002653561,4.274148e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002605462,"about_ca_system_score_gemma":0.0001320757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004277567,"about_ca_topic_score_gemma":0.00001082519,"domain_scores_codex":[0.9985515,0.0001010615,0.0004798087,0.0001113815,0.0006002365,0.0001560388],"domain_scores_gemma":[0.9972541,0.001260692,0.0003339904,0.00006035411,0.0007599574,0.0003308557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005624917,0.0002687446,0.001285729,0.00009880475,0.0001739933,0.0001653086,0.0005456446,0.03110663,0.0000155141,0.9496614,0.0130795,0.00303621],"study_design_scores_gemma":[0.001428666,0.0005905012,0.00110207,0.00004547656,0.0001005824,0.0003406387,0.0000810373,0.2705889,0.000001364105,0.7250999,0.0004925016,0.0001283574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1332457,0.00009385864,0.8657525,0.0004355189,0.00009879976,0.00006307507,0.0000574637,0.000008866391,0.0002442054],"genre_scores_gemma":[0.4588026,0.0000116479,0.5408974,0.0001218692,0.00009465301,8.228598e-7,0.00000770612,0.00001173743,0.00005157216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3255568,"threshold_uncertainty_score":0.4124426,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2113328396","doi":"10.1007/s11222-011-9301-9","title":"Interacting multiple try algorithms with different proposal distributions","year":2011,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Exploit; Extension (predicate logic); Monte Carlo method; Markov chain; Class (philosophy); Population; Selection (genetic algorithm)","retraction":null,"screen_n_in":null,"score":{"opus":0.06591323379604168,"gpt":0.3268388600126755,"spread":0.2609256262166338,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002136522,0.0001466848,0.000201966,0.00002840552,0.0002076408,0.00004287645,0.00006855079,0.00003287741,0.0000103806],"category_scores_gemma":[0.0003117958,0.0001066059,0.00002201801,0.00005034356,0.00005889548,0.00002870179,0.0001005483,0.0001687171,4.502336e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002055752,"about_ca_system_score_gemma":0.0000209328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004643248,"about_ca_topic_score_gemma":0.00006332197,"domain_scores_codex":[0.9991664,0.00007095529,0.0002267119,0.000200857,0.000109211,0.0002258743],"domain_scores_gemma":[0.9988694,0.0006845937,0.0001324654,0.0001365547,0.00008792902,0.00008911201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000117333,0.0005099805,0.0224814,0.0004755434,0.0002020041,0.0001290095,0.00939573,0.000003593798,0.0003214742,0.6204279,0.001097676,0.3448384],"study_design_scores_gemma":[0.004599623,0.001531918,0.01854224,0.001225728,0.0004931556,0.0003560335,0.009532029,0.8202724,0.005023808,0.1352389,0.001207897,0.001976279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1837515,0.00001611073,0.8147808,0.00001172989,0.000138319,0.0001344196,0.00009650215,0.00004604902,0.001024614],"genre_scores_gemma":[0.5293062,0.000002241859,0.4705598,0.000006786047,0.00004510851,0.000002994026,0.00001295022,0.00001193782,0.00005204732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8202688,"threshold_uncertainty_score":0.4347263,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160639001","doi":"10.1214/ecp.v12-1336","title":"On Variance Conditions for Markov Chain CLTs","year":2007,"lang":"en","type":"article","venue":"Electronic Communications in Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Markov chain; Variance (accounting); Limit (mathematics); Chain (unit); Applied mathematics; Econometrics; Statistics; Mathematical analysis; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.07906550605768099,"gpt":0.4114142508277198,"spread":0.3323487447700388,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007205604,0.0001725113,0.0002720759,0.0001267721,0.0002660348,0.00001848475,0.000834872,0.0001286065,0.00002512793],"category_scores_gemma":[0.003297673,0.0001764967,0.0001276187,0.0004011761,0.0001829251,0.00006759358,0.0001619776,0.0005291136,3.115207e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007379062,"about_ca_system_score_gemma":0.0002002592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002525746,"about_ca_topic_score_gemma":0.003817907,"domain_scores_codex":[0.9979278,0.000428443,0.0005782151,0.0003357515,0.0001413919,0.0005883746],"domain_scores_gemma":[0.9910162,0.005906191,0.0001562359,0.002705899,0.000149449,0.00006604371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008566533,0.000663875,0.0001955802,0.00005253642,0.00001836676,2.208159e-7,0.0002597657,0.000006276006,0.0001976369,0.9876906,0.0007021505,0.01012737],"study_design_scores_gemma":[0.0007764936,0.0001722911,0.0005983278,0.00004843747,0.00001816366,0.000003341345,0.00007332058,0.001899939,0.0003355492,0.979284,0.01658639,0.0002036807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3690447,0.001259098,0.5182913,0.01002025,0.0003700434,0.008390993,0.0001486039,0.0005088648,0.09196614],"genre_scores_gemma":[0.858215,0.00007079901,0.140077,0.0001970351,0.000029201,0.0006828893,0.00004382402,0.00002591294,0.00065836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4891703,"threshold_uncertainty_score":0.7197328,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152650468","doi":"10.1109/9.898698","title":"A probabilistic analysis of bias optimality in unichain Markov decision processes","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov decision process; Probabilistic logic; Markov process; Mathematical optimization; State space; Markov chain; Partially observable Markov decision process; Decision theory; Computer science; Mathematics; Markov model; Optimal decision; Value (mathematics); Decision problem; Markov kernel; Variable-order Markov model; Artificial intelligence; Algorithm; Statistics; Decision tree","retraction":null,"screen_n_in":null,"score":{"opus":0.05701441502884771,"gpt":0.3422949579532089,"spread":0.2852805429243612,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002072195,0.000269167,0.0009976138,0.0009213016,0.0000727288,0.00002894587,0.0002460681,0.0001419744,0.0002420119],"category_scores_gemma":[0.001148838,0.0002277557,0.0003580917,0.002553976,0.00007966326,0.00009612627,0.000001747209,0.0001730497,3.607507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393183,"about_ca_system_score_gemma":0.0001451924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046624,"about_ca_topic_score_gemma":0.002550061,"domain_scores_codex":[0.9973397,0.0004829221,0.001033707,0.0003747204,0.0004501517,0.0003188084],"domain_scores_gemma":[0.9929972,0.00568187,0.0003016813,0.0006782504,0.0002326651,0.0001083177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002357942,0.009534644,0.002581017,0.003675093,0.005877964,0.0001487247,0.005657289,0.2240851,0.002537241,0.002632489,0.000172546,0.7407399],"study_design_scores_gemma":[0.002921257,0.0001790598,0.001046157,0.0003711046,0.002270466,0.00001117329,0.0002976526,0.9888111,0.0005917626,0.003103806,0.00005065449,0.0003457732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4043755,0.00003080172,0.594332,0.00008875004,0.00007556143,0.0005893231,0.00004090845,0.00008783259,0.0003792848],"genre_scores_gemma":[0.9659389,0.00003679561,0.03351171,0.00005386085,0.000009190793,0.0002086243,0.000001657153,0.0000262107,0.0002131026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.764726,"threshold_uncertainty_score":0.9287608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2496191157","doi":"10.1017/cbo9780511791277.013","title":"Markov chain Monte Carlo","year":2005,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Monte Carlo method; Computer science; Hybrid Monte Carlo; Monte Carlo molecular modeling; Monte Carlo method in statistical physics; Statistical physics; Metropolis–Hastings algorithm; Bayesian probability; Monte Carlo integration; Applied mathematics; Quasi-Monte Carlo method; Mathematical optimization; Computation; Approximate Bayesian computation; Algorithm; Mathematics; Artificial intelligence; Physics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.04933256967473269,"gpt":0.2564852831290715,"spread":0.2071527134543389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000468363,0.0007450209,0.000954743,0.0002961602,0.0002207469,0.00005279924,0.000778016,0.0007978097,0.00001700045],"category_scores_gemma":[0.00006918633,0.000852042,0.0006254701,0.00001129673,0.0002415483,0.00008418164,0.000611699,0.00087345,0.000001082377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004349814,"about_ca_system_score_gemma":0.0001330781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001127451,"about_ca_topic_score_gemma":0.00002375854,"domain_scores_codex":[0.9976299,0.0001372191,0.0004056466,0.0008084541,0.0004772761,0.0005415363],"domain_scores_gemma":[0.9973392,0.0003452637,0.0004503065,0.001259295,0.0002531338,0.0003528379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001074578,0.00001733517,5.486254e-7,0.0002165611,0.0002446102,0.0004435681,0.00009311671,0.000001156499,0.00002271977,0.8428298,0.1467118,0.0093113],"study_design_scores_gemma":[0.0008045328,0.00005556971,8.752609e-7,0.0002924152,0.0004546785,0.00003364526,0.0000592464,0.0002183241,0.0001276463,0.00004911491,0.9970552,0.0008487952],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001880895,0.0003431411,0.001432786,0.00004080585,0.0004125309,0.0007682609,0.0004279045,0.0003264793,0.99606],"genre_scores_gemma":[0.0002324503,0.0002472093,0.007110373,0.0001187819,0.0005960228,0.000002270175,0.00001836071,0.0001655446,0.991509],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8503433,"threshold_uncertainty_score":0.999393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4238723945","doi":"10.1017/s0021900200117954","title":"Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ergodicity; Mathematics; Counterexample; Markov chain Monte Carlo; Markov chain; Statistical physics; Coupling (piping); Markov chain mixing time; Convergence (economics); Monte Carlo method; Algorithm; Applied mathematics; Markov model; Variable-order Markov model; Discrete mathematics; Statistics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.05514559893675608,"gpt":0.3196763472424737,"spread":0.2645307483057177,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0096956,0.0002104738,0.0007314171,0.0001235561,0.00006741998,0.00001407067,0.0001941607,0.0001643535,0.000008465266],"category_scores_gemma":[0.0005708077,0.0001673264,0.0001816836,0.000181996,0.0002131099,0.00007603334,0.00009973631,0.0004536721,1.582509e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001444727,"about_ca_system_score_gemma":0.00009488165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001743531,"about_ca_topic_score_gemma":0.00004185286,"domain_scores_codex":[0.9978126,0.00006512965,0.00113385,0.0002303157,0.0004645906,0.0002935342],"domain_scores_gemma":[0.9969593,0.001139551,0.0009469415,0.0003063633,0.0004430634,0.0002048039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.02111031,0.005701291,0.02693834,0.006369534,0.002405035,0.0003402429,0.02970261,0.003384313,0.04205164,0.3004178,0.002011342,0.5595675],"study_design_scores_gemma":[0.01660794,0.004441063,0.02210148,0.001365349,0.001580257,0.0005025302,0.01632733,0.06790333,0.1021703,0.7610521,0.003148703,0.002799612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315118,0.0002993378,0.06401803,0.00005023042,0.0001700438,0.0004560537,0.000009083119,0.00001567784,0.003469793],"genre_scores_gemma":[0.7003861,0.0000285093,0.2994085,0.00001834607,0.0001174975,0.000003058189,1.338769e-7,0.00001559639,0.00002223624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5567679,"threshold_uncertainty_score":0.6823374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2088606685","doi":"10.1007/s00362-006-0032-5","title":"On the natural restrictions in the singular Gauss–Markov model","year":2006,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Natural (archaeology); Gauss; Markov chain; Markov model; Econometrics; Mathematical economics; Mathematics; Applied mathematics; Computer science; Statistical physics; Statistics; Geography; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.0362272021142136,"gpt":0.32963675568475,"spread":0.2934095535705364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007503752,0.0001363093,0.0001427595,0.00003954997,0.000178415,0.00004501737,0.0001999939,0.00005503503,0.00003747204],"category_scores_gemma":[0.001870746,0.00006951211,0.0000611418,0.0001789715,0.0001311668,0.00001934026,0.00002421347,0.0003808665,5.507811e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005207661,"about_ca_system_score_gemma":0.00003277205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000128689,"about_ca_topic_score_gemma":0.0003269468,"domain_scores_codex":[0.9986567,0.0003411139,0.0002345844,0.0001884705,0.0003135298,0.0002656005],"domain_scores_gemma":[0.9946595,0.004898612,0.00004216472,0.0003417686,0.0000265037,0.00003146759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000199423,0.00007145713,0.00001711854,0.000009586121,0.00000580765,0.00001953912,0.000156708,0.0001024433,0.0002074089,0.968941,0.02868671,0.001762264],"study_design_scores_gemma":[0.0004903655,0.00008229948,0.001230715,0.00004162495,0.00006038617,0.00001169603,0.0007787783,0.07999537,0.00003038678,0.9121666,0.004832915,0.0002788616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1133362,0.0001472082,0.2015928,0.009912614,0.0005195088,0.001194477,0.0001786049,0.0001345852,0.672984],"genre_scores_gemma":[0.9376922,0.000005791816,0.05957335,0.00100349,0.00007662878,0.00004360643,0.0000114667,0.00001869229,0.001574793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.824356,"threshold_uncertainty_score":0.2834622,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089048182","doi":"10.1016/j.spa.2005.03.005","title":"Exponential forgetting and geometric ergodicity for optimal filtering in general state-space models","year":2005,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Markov chain; Ergodicity; State space; Continuous-time Markov chain; Context (archaeology); Sequence (biology); Ergodic theory; Applied mathematics; Additive Markov chain; Filter (signal processing); Markov process; Variable-order Markov model; Markov model; Pure mathematics; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.04613108094967818,"gpt":0.316571938793911,"spread":0.2704408578442328,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003407053,0.0001609758,0.0002296068,0.0001553007,0.0001710292,0.0000516754,0.00009533506,0.00005152583,0.000001490299],"category_scores_gemma":[0.0001852704,0.0001383672,0.00002994559,0.0002832397,0.00004969431,0.0001423013,0.00008647276,0.00008879189,2.06143e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002452386,"about_ca_system_score_gemma":0.00004202795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001533609,"about_ca_topic_score_gemma":0.00006262894,"domain_scores_codex":[0.9991045,0.00001318776,0.0002511238,0.0002995148,0.00007027603,0.0002613739],"domain_scores_gemma":[0.999037,0.0005327127,0.00009902913,0.0001492925,0.00009537867,0.00008661817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004034599,0.0009950344,0.0000474406,0.005428946,0.0002167605,0.000001234761,0.01618087,0.03478153,0.01639309,0.4549845,0.000378663,0.4701884],"study_design_scores_gemma":[0.001804443,0.0001331742,0.00001326669,0.000145951,0.00006470238,0.00002513166,0.001364907,0.7471355,0.004165272,0.2434247,0.001089128,0.0006338673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158136,0.0007464947,0.882283,0.0001632145,0.00001134588,0.0007637169,0.00005345707,0.00004107401,0.0001241864],"genre_scores_gemma":[0.8088012,0.00003307194,0.1899253,0.00002807102,0.0001213305,0.0009449052,0.000008302756,0.00002308274,0.0001146954],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7123539,"threshold_uncertainty_score":0.5642452,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2613020357","doi":"10.1080/00949655.2017.1326117","title":"Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Bayesian probability; Computation; Gaussian process; Gaussian; Applied mathematics; Statistics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.1758087556041645,"gpt":0.5287679271883436,"spread":0.3529591715841792,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001641205,0.00013657,0.0007301517,0.0003247056,0.0002337668,0.0001148442,0.00009995041,0.00007674447,0.000007325686],"category_scores_gemma":[0.003225015,0.0001138076,0.0001249894,0.0001942701,0.0001272758,0.0002297976,0.00002334018,0.000109664,1.239992e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003385386,"about_ca_system_score_gemma":0.00007844358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006507283,"about_ca_topic_score_gemma":0.00001868834,"domain_scores_codex":[0.9982806,0.0003255643,0.00088734,0.0001544158,0.0002337483,0.0001182619],"domain_scores_gemma":[0.9907664,0.006110916,0.001648155,0.0001055584,0.001257205,0.00011178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001296132,0.0004959725,0.002513663,0.001397932,0.002342582,0.000006256466,0.007154752,0.7204494,0.0003940335,0.08235345,0.0005308719,0.1810649],"study_design_scores_gemma":[0.001052389,0.0003189213,0.005986132,0.00005873515,0.001042838,0.000003166975,0.0003651136,0.9349261,0.00006152652,0.05598031,0.00009500106,0.0001097848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02274192,0.0000420293,0.9763511,0.0001020716,0.0001271342,0.0002708066,0.00006150461,0.000007551475,0.0002958398],"genre_scores_gemma":[0.5343547,0.000004169517,0.4655598,0.00001295546,0.00003659439,0.000001743667,0.00001670688,0.000004990515,0.000008314736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5116128,"threshold_uncertainty_score":0.4640938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108293203","doi":"10.1007/s10957-007-9297-7","title":"Measure-Valued Differentiation for Markov Chains","year":2007,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Markov chain; Mathematics; Measure (data warehouse); Markov kernel; Estimator; Kernel (algebra); Applied mathematics; Generality; Markov model; Discrete mathematics; Combinatorics; Mathematical optimization; Variable-order Markov model; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.03489857493065025,"gpt":0.3465355127841554,"spread":0.3116369378535052,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00427645,0.00009472885,0.0001885232,0.0001366354,0.0001637309,0.00002947542,0.00009548909,0.00007335388,0.00001678444],"category_scores_gemma":[0.0006128293,0.00007838343,0.00009821677,0.0001370111,0.0000385165,0.0001044083,0.0000135397,0.00008904676,1.900185e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002871722,"about_ca_system_score_gemma":0.00002515954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.236798e-7,"about_ca_topic_score_gemma":8.829205e-7,"domain_scores_codex":[0.9990471,0.0001259299,0.0004682988,0.00009859315,0.0001404662,0.0001195991],"domain_scores_gemma":[0.9978829,0.000997256,0.0004823787,0.0001347941,0.0004110576,0.00009162237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002688144,0.000143527,0.00004114245,0.00006043715,0.00006417853,3.522044e-7,0.0003006449,0.0006783868,0.0008971355,0.9655915,0.0002467556,0.03170709],"study_design_scores_gemma":[0.008425003,0.0007220632,0.0006180404,0.0003167078,0.001425388,0.0002520465,0.004481939,0.06185456,0.01339883,0.8868876,0.02047124,0.001146582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002677171,0.0001663959,0.9952717,0.0001220649,0.00008074902,0.000438683,0.000006705706,0.00001683537,0.001219727],"genre_scores_gemma":[0.2126675,0.0001176277,0.7851022,0.0001712758,0.0006118842,0.00006771187,0.00001408928,0.00003641294,0.001211324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2101695,"threshold_uncertainty_score":0.3196383,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2013770427","doi":"10.1007/s00440-006-0003-8","title":"Faster Mixing and Small Bottlenecks","year":2006,"lang":"en","type":"article","venue":"Probability Theory and Related Fields","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixing (physics); Markov chain; Mathematics; Mathematical finance; Upper and lower bounds; Statistical physics; Conductance; Markov chain mixing time; Markov process; Combinatorics; Chain (unit); Discrete mathematics; Markov property; Mathematical analysis; Markov model; Statistics; Physics; Quantum mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.02706464160399145,"gpt":0.2703303037262976,"spread":0.2432656621223062,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002210823,0.0001423953,0.0002124742,0.00003376685,0.0001071801,0.00003266878,0.0000615838,0.0003276683,0.00004880144],"category_scores_gemma":[0.0004047379,0.0001086549,0.00005806741,0.00005962509,0.0001462944,0.00004912942,0.00008344277,0.0003150441,1.227153e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009994742,"about_ca_system_score_gemma":0.000009066066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001508775,"about_ca_topic_score_gemma":0.0000283108,"domain_scores_codex":[0.9987778,0.000415569,0.0002788693,0.0002765341,0.00006037707,0.0001908654],"domain_scores_gemma":[0.9986136,0.0009896863,0.00006392821,0.0002405129,0.00003404503,0.00005824764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009850848,0.00009740377,0.001579763,0.0002954567,0.00003996185,0.000008497342,0.001134331,0.000004151061,0.000379965,0.9788238,0.0001579495,0.01738017],"study_design_scores_gemma":[0.0003676418,0.00005597303,0.0002569184,0.00005067214,0.00004925171,0.0000333538,0.0001271409,0.0001718192,0.0006347939,0.9972401,0.0008540978,0.0001581951],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9288391,0.000852093,0.01546142,0.0004217227,0.0001491038,0.0002874407,0.000002593333,0.00008624012,0.05390029],"genre_scores_gemma":[0.9771364,0.00005469545,0.01553621,0.0001109218,0.00005561003,0.00001239062,0.00000237141,0.00001692539,0.007074476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0482973,"threshold_uncertainty_score":0.443082,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2066357294","doi":"10.1214/07-aap486","title":"Variance bounding Markov chains","year":2008,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bounding overwatch; Variance (accounting); Markov chain; Property (philosophy); Markov property; Limit (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.2445090963513248,"gpt":0.3829196259814799,"spread":0.1384105296301551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003845323,0.0001927922,0.0004200691,0.00003574512,0.0002519865,0.00001164946,0.0004447005,0.00009366473,0.00004286916],"category_scores_gemma":[0.0005868451,0.0001317366,0.0001684084,0.0002454335,0.0003991894,0.00004821148,0.0001544056,0.0002193172,4.355485e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002098934,"about_ca_system_score_gemma":0.00007294416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000178408,"about_ca_topic_score_gemma":0.00001197375,"domain_scores_codex":[0.9983425,0.0001838234,0.000484879,0.0003129297,0.0003181491,0.0003577006],"domain_scores_gemma":[0.9976056,0.0008340555,0.0002626263,0.00105672,0.0001577268,0.00008328263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004707975,0.0004347732,0.0003296137,0.0004838498,0.0001064931,0.00000398459,0.0050829,0.0000260484,0.003587021,0.9690741,0.007728307,0.01267215],"study_design_scores_gemma":[0.0005058291,0.00009455533,0.001257423,0.0000465863,0.00003783874,0.00001690522,0.0002377315,0.0004066775,0.03227405,0.954403,0.01034654,0.0003728713],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8806045,0.0001007172,0.01114036,0.00151114,0.0001272524,0.0009691734,0.00001649256,0.0001076351,0.1054227],"genre_scores_gemma":[0.9448953,0.00008650291,0.05366246,0.000412234,0.0001282301,0.00008931317,0.000001794738,0.00002504038,0.0006991387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1047236,"threshold_uncertainty_score":0.5372061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114493774","doi":"10.1063/1.1649728","title":"Multicanonical basin hopping: A new global optimization method for complex systems","year":2004,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Maxima and minima; Global optimization; Minification; Monte Carlo method; Computation; Structural basin; Computer science; Multivariable calculus; Energy landscape; Energy minimization; Complex system; Mathematical optimization; Energy (signal processing); Algorithm; Mathematics; Physics; Engineering; Geology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1177199628182187,"gpt":0.4018856091586752,"spread":0.2841656463404564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001200354,0.0001593449,0.0004696285,0.00001270684,0.00004910554,0.00003530447,0.0003361671,0.00008819221,0.000003018475],"category_scores_gemma":[0.0008606063,0.0001033981,0.000262576,0.0001528621,0.00005718278,0.00008032909,0.00005366278,0.0002118775,5.658313e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002096766,"about_ca_system_score_gemma":0.0002101086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002297017,"about_ca_topic_score_gemma":4.768111e-7,"domain_scores_codex":[0.9986327,0.0001696136,0.0005556734,0.00009915079,0.0003207297,0.0002221009],"domain_scores_gemma":[0.9977155,0.001114394,0.000478396,0.0002332721,0.0002904381,0.0001680086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002064474,0.001330865,0.00001614865,0.0007213011,0.0009393877,0.00001366273,0.001943457,0.29241,0.2297785,0.4108625,0.03529975,0.02461986],"study_design_scores_gemma":[0.0127886,0.0006612147,0.000005174989,0.000742832,0.001659471,0.0006705516,0.0007326617,0.4005759,0.07553857,0.4974342,0.008323864,0.0008668883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007257969,0.00008100194,0.9905249,0.001181014,0.000182326,0.0002656485,0.00001099008,0.00001768213,0.0004784575],"genre_scores_gemma":[0.07870542,0.00001179318,0.9198783,0.0002260014,0.001113687,0.000002831987,0.000002575616,0.00002456768,0.00003478846],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.15424,"threshold_uncertainty_score":0.4216453,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012518296","doi":"10.1081/stm-200033117","title":"Convergence in the Wasserstein Metric for Markov Chain Monte Carlo Algorithms with Applications to Image Restoration","year":2004,"lang":"en","type":"article","venue":"Stochastic Models","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Markov chain; Wasserstein metric; Metric (unit); Markov chain Monte Carlo; Convergence (economics); Algorithm; Upper and lower bounds; Total variation; Applied mathematics; Image (mathematics); Bayesian probability; Mathematical optimization; Statistics; Computer science; Artificial intelligence; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.06900419749926845,"gpt":0.3476320010813868,"spread":0.2786278035821184,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001307292,0.0002181004,0.0002879642,0.0002469173,0.0001418274,0.00005088832,0.0003477832,0.00007892081,0.000002155426],"category_scores_gemma":[0.0003151873,0.0001554213,0.00007553218,0.0007881019,0.00004906955,0.0001555357,0.00004036872,0.000152587,3.284117e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001794897,"about_ca_system_score_gemma":0.0001110629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001615067,"about_ca_topic_score_gemma":0.000394167,"domain_scores_codex":[0.9984576,0.00009362139,0.0003568164,0.0003938393,0.0003510159,0.0003471166],"domain_scores_gemma":[0.9983774,0.000593565,0.0001245376,0.0005907678,0.000209502,0.0001042377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003457349,0.0005122178,0.000005399252,0.0003535298,0.00009027724,0.00001802714,0.01374765,0.3402211,0.001030556,0.6293268,0.001025352,0.01332334],"study_design_scores_gemma":[0.003766223,0.0008432344,0.00001917286,0.000236915,0.0002109259,0.00003066814,0.00541427,0.7545058,0.0003605595,0.2332287,0.0004851045,0.0008984635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007128126,0.0001053691,0.987938,0.0008389523,0.00007495052,0.003259677,0.00005172199,0.00004856947,0.0005546584],"genre_scores_gemma":[0.5163804,0.000003611819,0.4802695,0.0001743871,0.00008465804,0.002811277,0.000006958721,0.0000349971,0.0002342276],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5092523,"threshold_uncertainty_score":0.6337898,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2011918585","doi":"10.1016/s0304-4149(02)00096-0","title":"One-shot coupling for certain stochastic recursive sequences","year":2002,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Markov chain; Coupling (piping); Combinatorics; Chain (unit); Convergence (economics); Discrete mathematics; Statistics; Physics; Quantum mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.1437429349638152,"gpt":0.354352583947851,"spread":0.2106096489840358,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002943913,0.000232058,0.0003295846,0.00008405734,0.0004149046,0.00006590819,0.0002199008,0.00009524075,0.0000196367],"category_scores_gemma":[0.0007186647,0.0001997829,0.0000662325,0.0002951772,0.0001479325,0.00008527871,0.0000497099,0.000124873,4.224548e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000313548,"about_ca_system_score_gemma":0.00005227707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006969607,"about_ca_topic_score_gemma":0.00002977838,"domain_scores_codex":[0.9987933,0.00001347014,0.0003111771,0.0004264822,0.0001215665,0.0003339789],"domain_scores_gemma":[0.9971963,0.001834104,0.0001851433,0.0003109029,0.0003307502,0.0001428423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001008426,0.0006707822,0.00000189846,0.002552675,0.0002637936,4.45923e-7,0.007043246,0.0007188448,0.002009448,0.9452044,0.0008186469,0.04061501],"study_design_scores_gemma":[0.0008508706,0.0002990381,7.900563e-7,0.0003154871,0.0002148058,0.00002044555,0.003937765,0.06758711,0.0005356845,0.9243489,0.001221718,0.0006673807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001498086,0.001644126,0.9935439,0.0005369414,0.00004333432,0.00160995,0.0001241388,0.0001167465,0.0008827265],"genre_scores_gemma":[0.9635617,0.00003928059,0.03226579,0.0001051704,0.0002670084,0.003255983,0.00002368191,0.00005067838,0.0004307544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9620636,"threshold_uncertainty_score":0.814691,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1544040121","doi":"10.1023/a:1018674822185","title":"Universality and Conformal Invariance for the Ising Model in Domains with Boundary","year":2000,"lang":"en","type":"article","venue":"Journal of Statistical Physics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Statistical physics; Physics; Mathematics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.05269549209718155,"gpt":0.3442890203115299,"spread":0.2915935282143483,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006333579,0.00008156723,0.0002187502,0.00001317932,0.00008269939,0.00003922003,0.00008092825,0.00002728208,0.000007372588],"category_scores_gemma":[0.0001261602,0.00004911035,0.0000316001,0.00005504401,0.0001557642,0.0001519275,0.00001184683,0.0001976015,1.247141e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003712598,"about_ca_system_score_gemma":0.0001130187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007135896,"about_ca_topic_score_gemma":0.00001407256,"domain_scores_codex":[0.9993378,0.0000487761,0.0002500017,0.00006488165,0.0001603025,0.000138264],"domain_scores_gemma":[0.9982392,0.001398868,0.0001238137,0.00008902093,0.00008860465,0.00006048064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009196062,0.0001244804,0.00008483639,0.0001340917,0.00006990826,0.00003300759,0.00142795,0.002152846,0.00007283936,0.9121309,0.0009731886,0.08187634],"study_design_scores_gemma":[0.002531632,0.0003287977,0.0003758061,0.0001309038,0.0001464034,0.00005489808,0.0005345388,0.2945872,0.00006114363,0.6989262,0.002154412,0.0001680898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05762791,0.00003433873,0.9406952,0.0001950672,0.00002162519,0.0001206509,0.00004677525,0.000002669095,0.001255752],"genre_scores_gemma":[0.4991457,0.0000640361,0.5003461,0.0001888514,0.000106646,0.000001821664,9.308827e-7,0.00001324915,0.0001327392],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4415177,"threshold_uncertainty_score":0.2002662,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}