{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":5,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":5,"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":"fe8441027123","filters":{"venue":"Adaptation, learning, and optimization"}},"results":[{"id":"W1862757251","doi":"10.1007/978-3-642-27645-3_11","title":"Bayesian Reinforcement Learning","year":2012,"lang":"en","type":"book-chapter","venue":"Adaptation, learning, and optimization","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Bayesian probability; Artificial intelligence; Computer science; Machine learning; Posterior probability; Prior probability; Domain (mathematical analysis); Bayesian inference; Function (biology); Bellman equation; Mathematics; Mathematical optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.0155417566294811,"gpt":0.2220733637607066,"spread":0.2065316071312255,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004745243,0.0004845855,0.00037769,0.0003638072,0.0006252856,0.0003835787,0.0003715091,0.0003965187,0.0008058111],"category_scores_gemma":[0.0001702994,0.000534241,0.0001053878,0.000115875,0.00008759643,0.0009160081,0.0002403387,0.0008579915,0.0001325124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001255834,"about_ca_system_score_gemma":0.000122112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001334501,"about_ca_topic_score_gemma":0.000002521361,"domain_scores_codex":[0.997571,0.00009182664,0.0006475091,0.0005934309,0.000654572,0.0004416507],"domain_scores_gemma":[0.9980338,0.0001454081,0.0009095754,0.0003573959,0.0003405299,0.000213347],"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":[0.000004958766,0.0000038262,0.00009262943,0.00003892205,0.00005314526,0.000002432134,0.001634021,0.9017928,8.623766e-7,0.08558561,0.0002192403,0.01057152],"study_design_scores_gemma":[0.0002634528,0.0001691044,0.00001182984,0.00009166006,0.00005549957,0.00001010239,0.00005229205,0.7765715,0.000002312868,0.0002543843,0.2220662,0.0004517145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[3.011519e-7,0.0007295642,0.7731844,0.00009499544,0.0002676383,0.0002676888,2.02991e-7,0.0003426714,0.2251126],"genre_scores_gemma":[0.01270158,0.005031708,0.1351108,0.0001447029,0.0003300501,0.00001930815,0.0004014485,0.0001228186,0.8461376],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6380736,"threshold_uncertainty_score":0.9997109,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4298113282","doi":"10.1007/978-3-031-11748-0","title":"Federated and Transfer Learning","year":2022,"lang":"en","type":"book","venue":"Adaptation, learning, and optimization","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; University of Alberta; University of New Brunswick","funders":"","keywords":"Computer science; Transfer of learning; Domain (mathematical analysis); Data collection; Data science; World Wide Web; Artificial intelligence; Sociology; Mathematics; Social science","retraction":null,"screen_n_in":null,"score":{"opus":0.01791179426004466,"gpt":0.2304727798809663,"spread":0.2125609856209217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004162591,0.0002939409,0.0002793421,0.0003005982,0.0009304004,0.0004742974,0.002035634,0.0002954122,0.000272843],"category_scores_gemma":[0.003302519,0.000335151,0.00003555743,0.0002731579,0.0001068268,0.0006902434,0.0062062,0.0009887883,0.000005285535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001051694,"about_ca_system_score_gemma":0.0002145333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002163198,"about_ca_topic_score_gemma":0.0000108171,"domain_scores_codex":[0.9980854,0.0002116598,0.0003293032,0.0007610327,0.0003632464,0.000249393],"domain_scores_gemma":[0.9986584,0.0002144552,0.0001911365,0.0007123544,0.0001555808,0.00006803638],"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":[0.00002470066,0.00003347138,0.0002931107,0.0001568954,0.0001322725,0.00002736097,0.002217998,0.8261259,0.000009353192,0.01028664,0.07880767,0.0818846],"study_design_scores_gemma":[0.0002913612,0.0001469549,0.00002317802,0.00003865411,0.00002510146,0.00001827492,0.0001378377,0.8296042,0.000003901758,0.005385937,0.1640027,0.000321902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003552131,0.001507395,0.9704068,0.001461934,0.0001715001,0.0002576752,0.000005561823,0.0009852397,0.02516836],"genre_scores_gemma":[0.004125043,0.01605258,0.4994356,0.0002603887,0.0001462886,0.0001132723,0.002526665,0.0001747835,0.4771654],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4709712,"threshold_uncertainty_score":0.9999101,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W177935800","doi":"10.1007/978-3-642-10701-6_4","title":"Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mapping","year":2010,"lang":"en","type":"book-chapter","venue":"Adaptation, learning, and optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Fidelity; Computer science; Optimization problem; Space (punctuation); Surrogate model; Mathematical optimization; Variable (mathematics); Vector optimization; Algorithm; Mathematics; Multi-swarm optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.02037993522443914,"gpt":0.2512348071006574,"spread":0.2308548718762183,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004057719,0.000657888,0.0006902873,0.0005943393,0.0007473146,0.0001892547,0.0003707329,0.0007085324,0.0003795014],"category_scores_gemma":[0.0007484413,0.0007572875,0.0001527834,0.0005681982,0.0003147307,0.001419578,0.0001895698,0.0008998658,0.00001753083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002160009,"about_ca_system_score_gemma":0.0006912508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005542741,"about_ca_topic_score_gemma":0.00002959426,"domain_scores_codex":[0.9967768,0.0001969382,0.0009274875,0.001229228,0.0005080258,0.0003615364],"domain_scores_gemma":[0.9934292,0.0005359263,0.001658138,0.0006111343,0.003610579,0.0001549815],"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":[0.00002417047,0.00005908244,0.00001417248,0.00006188134,0.0000807565,0.000001816804,0.002975894,0.9521655,0.00002861721,0.04277613,0.00006322152,0.001748703],"study_design_scores_gemma":[0.001075773,0.0001707427,0.00001515012,0.0002136306,0.00009318066,0.00001115334,0.0004413677,0.9785115,0.00009936116,0.002847893,0.01581975,0.0007005347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001132721,0.0006267851,0.9220762,0.0001025882,0.0006808431,0.0008348861,0.00002522611,0.0003619582,0.07529037],"genre_scores_gemma":[0.000510543,0.0007509303,0.9391211,0.00008382916,0.0001662029,0.00006140309,0.0006453875,0.0001206286,0.05854002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03992824,"threshold_uncertainty_score":0.9994878,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W30176373","doi":"10.1007/978-3-642-13425-8_12","title":"VISPLORE: Exploring Particle Swarms by Visual Inspection","year":2010,"lang":"en","type":"book-chapter","venue":"Adaptation, learning, and optimization","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Visualization; Computer science; Particle swarm optimization; Population; Swarm behaviour; Multi-swarm optimization; Data mining; Artificial intelligence; Theoretical computer science; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.01118193080910407,"gpt":0.2392271323414819,"spread":0.2280452015323778,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001333041,0.0002153239,0.0001610582,0.00007019179,0.0002142694,0.00007415654,0.00005808448,0.0003074661,0.000123506],"category_scores_gemma":[0.00007953542,0.0002405085,0.00006809235,0.00003790515,0.00008436906,0.00002425734,0.00004687594,0.0002591065,0.000008738087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001615727,"about_ca_system_score_gemma":0.00003249459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002482801,"about_ca_topic_score_gemma":0.00004919632,"domain_scores_codex":[0.9990236,0.00002670749,0.0002507389,0.0004173763,0.0001488154,0.0001327325],"domain_scores_gemma":[0.9993409,0.00001007196,0.0002385134,0.0001433138,0.0002009615,0.00006627032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005125981,0.0003952279,0.003041215,0.0002410586,0.001237607,0.00001823504,0.004489184,0.2784706,0.5803427,0.006613635,0.02378742,0.1008506],"study_design_scores_gemma":[0.000871561,0.0009252311,0.00006426112,0.00007396236,0.0003896427,0.00001772845,0.0003914361,0.1850637,0.1717907,0.0004477932,0.6386937,0.001270254],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01221802,0.002223687,0.9440714,0.00008813317,0.0001342834,0.0004475241,0.000004874479,0.0003497651,0.04046232],"genre_scores_gemma":[0.5525365,0.03064809,0.02228717,0.0002184878,0.001014809,0.000143335,0.005040532,0.000321567,0.3877895],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9217842,"threshold_uncertainty_score":0.980765,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4298126048","doi":"10.1007/978-3-031-11748-0_5","title":"A Unifying Framework for Federated Learning","year":2022,"lang":"en","type":"book-chapter","venue":"Adaptation, learning, and optimization","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"","keywords":"Computer science; Unification; Scheme (mathematics); Convergence (economics); Theoretical computer science; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03380261828517568,"gpt":0.2661298969526333,"spread":0.2323272786674576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004877254,0.0003355588,0.0003134734,0.0003368675,0.00129978,0.0004755281,0.003011525,0.0004325403,0.0003313326],"category_scores_gemma":[0.0123416,0.0003971668,0.00007386784,0.0001928039,0.00007620193,0.0006762014,0.007756181,0.001122858,0.000007228288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001242229,"about_ca_system_score_gemma":0.0001421414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001604452,"about_ca_topic_score_gemma":0.000005921465,"domain_scores_codex":[0.998024,0.00008926999,0.0004058629,0.0008202984,0.000363619,0.0002969224],"domain_scores_gemma":[0.9973001,0.0005910831,0.0006397908,0.00112153,0.0002827542,0.00006466552],"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":[0.00001787385,0.00001046108,0.00003557716,0.00006943071,0.00007511711,0.00000577823,0.0005974593,0.6051188,0.000001536005,0.3554379,0.004961979,0.03366802],"study_design_scores_gemma":[0.0001854066,0.0001434853,0.000002398378,0.00007961328,0.00002415439,0.000006599153,0.00009657675,0.6856759,0.000002797389,0.1254697,0.1880008,0.0003125551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000025155,0.0008543738,0.9756944,0.002181489,0.000301438,0.0004446766,0.000008341091,0.001169638,0.01934314],"genre_scores_gemma":[0.0006362101,0.002741467,0.9426682,0.0001035861,0.00008720221,0.00008141118,0.0007831098,0.0000908974,0.0528079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2299682,"threshold_uncertainty_score":0.999848,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}