{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":762,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":762,"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","author_layer_release":"2026-06-26"},"query_hash":"531a2dd68840","filters":{"topic":"Metaheuristic Optimization Algorithms Research"}},"results":[{"id":"W2738900493","doi":"10.1016/j.advengsoft.2017.07.002","title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","year":2017,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4981,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Mathematical optimization; Convergence (economics); Computer science; Swarm behaviour; Pareto principle; Multi-objective optimization; Algorithm; Mathematics","authors":[{"name":"Seyedali Mirjalili","is_ca":true},{"name":"Amir H. Gandomi","is_ca":false},{"name":"Seyedeh Zahra Mirjalili","is_ca":true},{"name":"Shahrzad Saremi","is_ca":true},{"name":"Hossam Faris","is_ca":false},{"name":"Seyed Mohammad Mirjalili","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02226714207504294,"gpt":0.2812625154124628,"spread":0.2589953733374199,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005771847,0.0008914983,0.001020917,0.0006318583,0.000385254,0.0006862427,0.0008949707,0.001290711,0.002271881],"category_scores_gemma":[0.001555702,0.0003307128,0.0006344423,0.0007302326,0.0004125882,0.0005150054,0.0006471821,0.001135541,0.0009155584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003867499,"about_ca_system_score_gemma":0.0007870241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001748276,"about_ca_topic_score_gemma":0.001866352,"domain_scores_codex":[0.9997389,0.0000730133,0.00001281402,0.00002325849,0.0001368337,0.00001515108],"domain_scores_gemma":[0.9996938,0.0001438036,0.00003120493,0.00003526055,0.00007791637,0.00001801293],"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":[0.00008003681,0.00006386064,0.0003844457,0.0002128483,0.00008384379,0.00009258897,0.00007478644,0.7892789,0.006122083,0.0142196,0.007664883,0.1817221],"study_design_scores_gemma":[0.00002111048,0.00002140151,0.00004878722,0.000009299189,0.00000973004,0.00001607936,0.000005525657,0.9925562,0.0007642505,0.002641986,0.003901627,0.000004068978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006000032,0.0003971681,0.9859042,0.000202611,0.0001277664,0.00006865242,0.00007304639,0.0007879387,0.006438504],"genre_scores_gemma":[0.1506293,0.0006716617,0.8398271,0.0002649351,0.0001144188,0.0004392186,0.0002621971,0.0003882228,0.007403009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002271881,"threshold_uncertainty_score":0.007600248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1801849579","doi":"10.1016/s0167-739x(00)00043-1","title":"– Ant System","year":2000,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2698,"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":"Travelling salesman problem; Ant colony optimization algorithms; Computer science; Benchmark (surveying); Quadratic assignment problem; Extremal optimization; Mathematical optimization; Parallel metaheuristic; Combinatorial optimization; Metaheuristic; Ant colony; Optimization problem; Artificial intelligence; Algorithm; Meta-optimization; Mathematics","authors":[{"name":"Thomas Stützle","is_ca":false},{"name":"Holger H. Hoos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01681498692808548,"gpt":0.2320439440966263,"spread":0.2152289571685409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002720156,0.0006507418,0.0003908795,0.0007576571,0.0008520136,0.001757101,0.001166928,0.0008551918,0.04204626],"category_scores_gemma":[0.0009044369,0.0002595831,0.0003728202,0.0007688242,0.0002984381,0.0009418175,0.0009229765,0.0007508033,0.02846852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004749129,"about_ca_system_score_gemma":0.001004075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001996703,"about_ca_topic_score_gemma":0.002230614,"domain_scores_codex":[0.9996065,0.00006622361,0.00002365518,0.00009081976,0.0001711731,0.00004158531],"domain_scores_gemma":[0.9996589,0.00003388383,0.00002749376,0.00008198991,0.0001574627,0.00004023792],"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":[0.0006575084,0.0003658125,0.002069082,0.001027194,0.000135567,0.0004180951,0.0002583549,0.03680173,0.04295867,0.08431405,0.1853733,0.6456206],"study_design_scores_gemma":[0.0001041022,0.0001851267,0.001122932,0.00005281956,0.00006106806,0.0005407026,0.00006869144,0.06795353,0.01971604,0.01437358,0.8957636,0.00005771813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.03676422,0.002038814,0.1865063,0.001309705,0.002320279,0.0008883241,0.002745408,0.02016042,0.7472665],"genre_scores_gemma":[0.3493871,0.001760088,0.1912919,0.001347288,0.000540477,0.0006040222,0.005856886,0.001945056,0.4472671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04204626,"threshold_uncertainty_score":0.1406587,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123682012","doi":"10.1109/tevc.2007.894200","title":"Opposition-Based Differential Evolution","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1602,"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":"Differential evolution; Ode; Initialization; Evolutionary algorithm; Curse of dimensionality; Population; Mathematical optimization; Benchmark (surveying); Evolutionary computation; Computer science; Nonlinear system; Algorithm; Mathematics; Artificial intelligence; Applied mathematics","authors":[{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"M.M.A. Salama","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02639547269231373,"gpt":0.262270554169624,"spread":0.2358750814773102,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005859855,0.0004588368,0.0008109875,0.0004108646,0.0002468894,0.0007535898,0.000870219,0.0007318342,0.001780473],"category_scores_gemma":[0.001918171,0.0001985928,0.0005089188,0.000468329,0.0005411235,0.0005393349,0.000801159,0.0007652857,0.0003629478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426997,"about_ca_system_score_gemma":0.000311899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006315226,"about_ca_topic_score_gemma":0.0005505938,"domain_scores_codex":[0.9996587,0.00009477348,0.00001695944,0.00004467518,0.0001622686,0.00002258449],"domain_scores_gemma":[0.9995366,0.0002708391,0.00004702167,0.00003565259,0.00009225002,0.000017578],"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":[0.00006908864,0.00005885887,0.0006666077,0.0001683944,0.00005733885,0.0001512686,0.0000919676,0.7957305,0.00892707,0.06446205,0.001460133,0.1281567],"study_design_scores_gemma":[0.00001840298,0.00004587315,0.0001250525,0.00001111057,0.000008786184,0.00007198095,0.000007601315,0.9859945,0.001430371,0.007767322,0.004508683,0.00001041552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01617374,0.0004506005,0.9675655,0.0001635855,0.00009932745,0.00006900707,0.00003924708,0.0001499232,0.01528914],"genre_scores_gemma":[0.6355399,0.0008745897,0.3512546,0.0003049475,0.00006730368,0.0003362929,0.0001334631,0.00006270253,0.01142617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001780473,"threshold_uncertainty_score":0.005956292,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137849348","doi":"10.1016/s0166-218x(01)00338-9","title":"A survey of very large-scale neighborhood search techniques","year":2002,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":639,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Heuristic; Mathematics; Mathematical optimization; Local search (optimization); Variable neighborhood search; Search algorithm; Computation; Algorithm; Incremental heuristic search; Scale (ratio); Computer science; Beam search; Metaheuristic","authors":[{"name":"Ravindra K. Ahuja","is_ca":false},{"name":"Özlem Ergün","is_ca":false},{"name":"James B. Orlin","is_ca":false},{"name":"Abraham P. Punnen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04136196228381948,"gpt":0.2908560751222848,"spread":0.2494941128384653,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001328225,0.0008437841,0.002302706,0.00136538,0.0006839976,0.001596672,0.002067382,0.001179235,0.003302742],"category_scores_gemma":[0.00346646,0.0004796176,0.0009951688,0.004584826,0.0005364628,0.002311946,0.001358384,0.001130303,0.001388694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004738735,"about_ca_system_score_gemma":0.0006907508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001399079,"about_ca_topic_score_gemma":0.001745038,"domain_scores_codex":[0.9989433,0.000318186,0.00008322144,0.0001403413,0.0004786184,0.00003640838],"domain_scores_gemma":[0.9989828,0.0005419221,0.0000615024,0.0001600892,0.0002244348,0.00002915501],"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":[0.00009216317,0.0001415605,0.0006670111,0.001424403,0.000169688,0.00005558025,0.00008349164,0.1043857,0.001962469,0.07450391,0.009366123,0.8071479],"study_design_scores_gemma":[0.00007156312,0.0002091713,0.0007582846,0.0003295308,0.0001324928,0.0003734373,0.00008615424,0.8044679,0.002098088,0.1063068,0.08511978,0.00004682048],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003397173,0.04669939,0.9398813,0.0003934644,0.0002779967,0.00007981484,0.0000755027,0.0003281831,0.008867119],"genre_scores_gemma":[0.1116146,0.06275196,0.8151459,0.0004634432,0.0008712019,0.0003948021,0.0006096577,0.0003304344,0.007817999],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003302742,"threshold_uncertainty_score":0.01104879,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2026267944","doi":"10.5267/j.ijiec.2012.03.007","title":"An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":504,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Computer science; Population; Algorithm; Mathematical optimization; Optimization algorithm; Population-based incremental learning; Range (aeronautics); Optimization problem; Process (computing); Artificial intelligence; Machine learning; Mathematics; Genetic algorithm; Engineering","authors":[{"name":"R. Venkata Rao","is_ca":false},{"name":"Vivek Patel","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04507322606150652,"gpt":0.3144906225427194,"spread":0.2694173964812128,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001074746,0.0009416537,0.001217175,0.0007087695,0.0006151241,0.0006145742,0.001682452,0.001520229,0.002094659],"category_scores_gemma":[0.001881642,0.0003392733,0.000597135,0.001034572,0.0007498115,0.0007175222,0.001085282,0.001141025,0.0003846437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007295218,"about_ca_system_score_gemma":0.001506249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004070561,"about_ca_topic_score_gemma":0.005197797,"domain_scores_codex":[0.9996537,0.00008540758,0.00002002318,0.00004765213,0.0001584246,0.00003487402],"domain_scores_gemma":[0.9995611,0.0002428953,0.00004747817,0.00002166715,0.0001033008,0.00002357789],"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":[0.00005275791,0.00009325039,0.0004783391,0.00009760924,0.00006201752,0.00008298884,0.00007909946,0.8612114,0.001859535,0.007365529,0.001495565,0.127122],"study_design_scores_gemma":[0.0000288572,0.00003038092,0.00005423303,0.000007664867,0.000007178467,0.00002204525,0.000005723381,0.9975489,0.0004079527,0.001136571,0.0007461896,0.000004248342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008518294,0.000275705,0.9879352,0.0001263015,0.00004900073,0.00007599651,0.00001746698,0.0002725633,0.002729531],"genre_scores_gemma":[0.2347603,0.0005423786,0.7571639,0.0003134085,0.00007715858,0.0007783538,0.0001425794,0.0001153862,0.006106568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004070561,"threshold_uncertainty_score":0.008093715,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2038636021","doi":"10.1016/j.ins.2011.03.016","title":"Enhancing particle swarm optimization using generalized opposition-based learning","year":2011,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":473,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ontario Institute of Technology","funders":"National Natural Science Foundation of China","keywords":"Premature convergence; Initialization; Mathematical optimization; Particle swarm optimization; Local optimum; Differential evolution; Population; Benchmark (surveying); Computer science; Mathematics; Ode; Applied mathematics","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Zhijian Wu","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Yong Liu","is_ca":false},{"name":"Mario Ventresca","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09140839569055396,"gpt":0.3139838274931784,"spread":0.2225754318026244,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009431838,0.0005577746,0.0008656816,0.0005238506,0.0003181257,0.0007326042,0.0008004783,0.0009391207,0.001016115],"category_scores_gemma":[0.001885458,0.000250182,0.0005452265,0.0006128279,0.0005058362,0.000833162,0.0009060065,0.0007107533,0.0002315575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926345,"about_ca_system_score_gemma":0.0003761816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008796167,"about_ca_topic_score_gemma":0.00079711,"domain_scores_codex":[0.9996213,0.0001217532,0.00001510705,0.00003122654,0.0001823243,0.00002827817],"domain_scores_gemma":[0.9995375,0.0002292601,0.00005844891,0.00004230701,0.0001156346,0.00001695664],"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":[0.0001299381,0.0001394966,0.0005332733,0.0001307334,0.00007582215,0.00008112991,0.00007261633,0.8663871,0.01063663,0.01561754,0.0009274464,0.1052682],"study_design_scores_gemma":[0.00001177765,0.00003250171,0.00005984579,0.000003223164,0.00000668296,0.00001581168,0.000003600136,0.9973668,0.0007240406,0.001446373,0.0003264216,0.000002969864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03855208,0.0002919854,0.9528069,0.000135586,0.0001129635,0.00004758492,0.00001067608,0.0001750241,0.007867237],"genre_scores_gemma":[0.6893696,0.0003466222,0.3066112,0.0001388843,0.00008530896,0.0001370865,0.00004342635,0.00008444172,0.003183291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001016115,"threshold_uncertainty_score":0.004988074,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2045050140","doi":"10.1016/j.ins.2014.10.042","title":"Metaheuristics in large-scale global continues optimization: A survey","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":470,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Metaheuristic; Computer science; Parallel metaheuristic; Decomposition; Engineering optimization; Search-based software engineering; Scale (ratio); Field (mathematics); Mathematical optimization; Optimization problem; Global optimization; Management science; Industrial engineering; Artificial intelligence; Algorithm; Meta-optimization; Engineering; Mathematics; Software; Software development","authors":[{"name":"Sedigheh Mahdavi","is_ca":false},{"name":"Mohammad Ebrahim Shiri","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02750561603582747,"gpt":0.3127759119738857,"spread":0.2852702959380582,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001960969,0.002002171,0.003024088,0.001742358,0.000659817,0.002673404,0.002476307,0.002370625,0.003080935],"category_scores_gemma":[0.004244551,0.0006545319,0.001399668,0.005418752,0.0009038282,0.002496429,0.001359546,0.002083213,0.001549761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000797194,"about_ca_system_score_gemma":0.00160627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002310767,"about_ca_topic_score_gemma":0.002528324,"domain_scores_codex":[0.9991612,0.0002678983,0.00007788045,0.0001366607,0.0002975883,0.00005875815],"domain_scores_gemma":[0.9985467,0.001010122,0.00010537,0.0001076706,0.000182719,0.00004750521],"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":[0.0001257263,0.000382773,0.0009990274,0.003201721,0.0003191829,0.00007836406,0.0001012374,0.2548856,0.001438446,0.05830602,0.009448622,0.6707132],"study_design_scores_gemma":[0.0001167924,0.0002756599,0.0007802006,0.0008941398,0.0002329032,0.0002212798,0.0001768203,0.8056086,0.002033651,0.1031004,0.08650527,0.00005421231],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.009147745,0.2704243,0.6943471,0.002114431,0.0006237625,0.0001972781,0.0001860948,0.0004318518,0.02252744],"genre_scores_gemma":[0.1438005,0.3310582,0.5136182,0.001261874,0.002045461,0.0005006187,0.0006913755,0.0003606368,0.006663096],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003080935,"threshold_uncertainty_score":0.01037067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2760225670","doi":"10.1016/j.swevo.2017.09.010","title":"Opposition based learning: A literature review","year":2017,"lang":"en","type":"review","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":465,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Computer science; Opposition (politics); Reinforcement learning; Variety (cybernetics); Artificial intelligence; Artificial neural network; Machine learning; Management science; Operations research; Law; Mathematics; Political science","authors":[{"name":"Sedigheh Mahdavi","is_ca":true},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Kalyanmoy Deb","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0812765835477228,"gpt":0.3908341177917897,"spread":0.3095575342440668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009942453,0.00088803,0.001637226,0.004136431,0.0003197936,0.002102606,0.001041711,0.001311113,0.004331523],"category_scores_gemma":[0.003230849,0.0003690397,0.0007453365,0.006887313,0.0005726715,0.002171006,0.0008037086,0.001073421,0.001105553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006547559,"about_ca_system_score_gemma":0.002513963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001800941,"about_ca_topic_score_gemma":0.003030312,"domain_scores_codex":[0.9996064,0.0000674464,0.00008836911,0.00007932106,0.000135914,0.00002246506],"domain_scores_gemma":[0.998215,0.001235982,0.000163427,0.00002839409,0.0003051105,0.00005221139],"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":[0.00008520803,0.0001167511,0.0004637722,0.05461767,0.0001575244,0.0001617776,0.00008275391,0.0009294026,0.0006151065,0.004498194,0.01750306,0.9207688],"study_design_scores_gemma":[0.00006808218,0.0002561223,0.002959542,0.04951474,0.001391395,0.002299101,0.000429622,0.00161006,0.0009459555,0.009816876,0.930598,0.0001103886],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002118748,0.9978192,0.0005152286,0.0002976711,0.0001566836,0.00001023239,0.00003276661,0.000006782308,0.0009495715],"genre_scores_gemma":[0.001584796,0.9967925,0.0008902611,0.0001852225,0.0002019274,0.00001264563,0.00004114722,0.00000280754,0.0002888358],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004331523,"threshold_uncertainty_score":0.01449043,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2157833270","doi":"10.1109/cec.2007.4424748","title":"Quasi-oppositional Differential Evolution","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":447,"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":"Ode; Initialization; Differential evolution; Benchmark (surveying); Applied mathematics; Computer science; Test suite; Suite; Population; Algorithm; Mathematical optimization; Mathematics; Test case; Machine learning","authors":[{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"M.M.A. Salama","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01624056719856473,"gpt":0.2861569665247169,"spread":0.2699163993261522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009710958,0.0004102949,0.0006240642,0.0004482427,0.0002623016,0.0008999603,0.0009768151,0.0007416289,0.002060041],"category_scores_gemma":[0.002293054,0.0002163835,0.0006406784,0.0003984958,0.0007449804,0.0008866865,0.0009783105,0.0008029703,0.0003925303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004246844,"about_ca_system_score_gemma":0.0003514295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004071727,"about_ca_topic_score_gemma":0.0003490064,"domain_scores_codex":[0.9993555,0.0002386402,0.00002996691,0.0000838686,0.0002505466,0.0000414731],"domain_scores_gemma":[0.9992452,0.0004156774,0.00007716796,0.00008257182,0.0001425936,0.00003683056],"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":[0.000149606,0.0001021122,0.001339918,0.0002537663,0.0001127074,0.0003458288,0.0001767193,0.569536,0.02025535,0.2621356,0.002123713,0.1434687],"study_design_scores_gemma":[0.00001907501,0.00008027072,0.0001625167,0.00001196592,0.00001226749,0.0001381113,0.000009379297,0.9707445,0.001539577,0.02154823,0.005718741,0.00001551632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01063519,0.000197043,0.980358,0.0001502541,0.0001111754,0.00004037703,0.00002608369,0.00007310093,0.008408712],"genre_scores_gemma":[0.6010509,0.0005173595,0.3864653,0.0004116073,0.00008508342,0.000238687,0.0001087818,0.00006736015,0.01105484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002060041,"threshold_uncertainty_score":0.006891549,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2062355752","doi":"10.1016/j.ins.2012.10.012","title":"Diversity enhanced particle swarm optimization with neighborhood search","year":2012,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":402,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ontario Institute of Technology","funders":"Nanchang Institute of Technology; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Particle swarm optimization; Premature convergence; Convergence (economics); Mathematical optimization; Computer science; Set (abstract data type); Diversity (politics); Multi-swarm optimization; Metaheuristic; Local search (optimization); Swarm behaviour; Artificial intelligence; Mathematics; Geography","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Hui Sun","is_ca":false},{"name":"Changhe Li","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Jeng‐Shyang Pan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04076355132915047,"gpt":0.2876941417484873,"spread":0.2469305904193368,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009028745,0.0004726197,0.0009060943,0.0006384137,0.0004810097,0.0007733746,0.0009113143,0.001163989,0.001570774],"category_scores_gemma":[0.002762036,0.000334069,0.0005073816,0.0007425698,0.0004430706,0.001097114,0.001007104,0.0006919664,0.0002477513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003832664,"about_ca_system_score_gemma":0.0004324639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001350622,"about_ca_topic_score_gemma":0.001015464,"domain_scores_codex":[0.9996352,0.0001387579,0.00001455761,0.00004130375,0.0001485038,0.00002165901],"domain_scores_gemma":[0.999378,0.0003444698,0.0000512474,0.00005676336,0.0001472425,0.00002224157],"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":[0.0001519068,0.00008885039,0.0006669632,0.0001132632,0.00007423006,0.00008561657,0.00006881546,0.8903753,0.002785126,0.02591783,0.001464506,0.07820754],"study_design_scores_gemma":[0.00001588709,0.00003406527,0.00008695238,0.000004757611,0.000009355449,0.00001836308,0.000003833238,0.9967715,0.0004013073,0.002175112,0.0004750493,0.000003764025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03932991,0.0009204596,0.9474046,0.0002057363,0.0002158777,0.00006851181,0.00003061041,0.0001254331,0.01169887],"genre_scores_gemma":[0.7054039,0.0004757194,0.2863978,0.0001225399,0.0001636761,0.0002582414,0.00007312821,0.0000741061,0.007030868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001570774,"threshold_uncertainty_score":0.005254745,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084993914","doi":"10.1016/j.camwa.2006.07.013","title":"A novel population initialization method for accelerating evolutionary algorithms","year":2007,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":377,"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":"Initialization; Population; Benchmark (surveying); Convergence (economics); Computer science; Mathematical optimization; Evolutionary algorithm; Algorithm; Set (abstract data type); Mathematics; Artificial intelligence","authors":[{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"M.M.A. Salama","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0573682288893775,"gpt":0.3569625786743005,"spread":0.2995943497849231,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006455357,0.0008263589,0.0007851514,0.00092221,0.0006848753,0.0007741141,0.001506528,0.001225744,0.004561184],"category_scores_gemma":[0.001811476,0.0004316446,0.0005743024,0.001226735,0.0003631507,0.0009829383,0.001036061,0.00118917,0.001663647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461393,"about_ca_system_score_gemma":0.0006478794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531319,"about_ca_topic_score_gemma":0.0024195,"domain_scores_codex":[0.999637,0.00008650971,0.00001784541,0.00004942467,0.0001811727,0.00002804806],"domain_scores_gemma":[0.9995666,0.0001298187,0.00003227678,0.00005498042,0.0001900472,0.00002619568],"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":[0.0001752636,0.0001329604,0.0007622702,0.0002458233,0.0001353347,0.0002227491,0.0001848024,0.2166878,0.03448196,0.03956727,0.01713331,0.6902704],"study_design_scores_gemma":[0.00006239369,0.00006430029,0.0002456584,0.00002132314,0.00003786369,0.0001653627,0.00001262556,0.9762396,0.006082026,0.003935791,0.01310744,0.00002552971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003743808,0.0002926717,0.990665,0.00009495293,0.0003303265,0.00006712769,0.00003201156,0.0006429414,0.004131239],"genre_scores_gemma":[0.05366831,0.0002824742,0.9382101,0.0001521779,0.000141493,0.0003060356,0.0001197261,0.0002317634,0.006887927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004561184,"threshold_uncertainty_score":0.01525867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1982970548","doi":"10.1007/s00500-010-0591-1","title":"DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization","year":2010,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":367,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"China University of Geosciences; China Scholarship Council","keywords":"Differential evolution; Benchmark (surveying); Evolutionary algorithm; Computer science; Global optimization; Curse of dimensionality; Mathematical optimization; Convergence (economics); Range (aeronautics); Evolutionary computation; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Engineering","authors":[{"name":"Wenyin Gong","is_ca":false},{"name":"Zhihua Cai","is_ca":false},{"name":"Charles X. Ling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009130652461568212,"gpt":0.2613821391107193,"spread":0.2522514866491511,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007940945,0.0009916603,0.001272711,0.0006987,0.0004812984,0.0008493396,0.001724586,0.001618557,0.003782372],"category_scores_gemma":[0.001478088,0.0005670589,0.0008783257,0.0008207189,0.0005222402,0.0006531937,0.001425974,0.001496946,0.0009714456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005080947,"about_ca_system_score_gemma":0.000814612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00293221,"about_ca_topic_score_gemma":0.003789688,"domain_scores_codex":[0.9996611,0.0001175482,0.00001688843,0.00003555763,0.0001401731,0.0000287635],"domain_scores_gemma":[0.9996207,0.0001945642,0.00003047313,0.00004433116,0.00007868426,0.00003129008],"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":[0.00009653495,0.00009452814,0.0004591641,0.0001901026,0.0001538918,0.00006389886,0.00004240085,0.8719544,0.004872026,0.01863943,0.002594677,0.1008389],"study_design_scores_gemma":[0.00002080982,0.00001833736,0.00004766079,0.000006868762,0.000009399146,0.00001018786,0.000002380164,0.9958034,0.0005865328,0.001477386,0.002012456,0.000004518727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005715339,0.0003254401,0.9871991,0.000121198,0.0001542467,0.00008005651,0.00008204109,0.0006026555,0.0057199],"genre_scores_gemma":[0.1386303,0.000360299,0.8533965,0.0002774937,0.00009291258,0.000520546,0.0002261136,0.0004596847,0.006036158],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003782372,"threshold_uncertainty_score":0.01265323,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2961079568","doi":"10.5267/j.ijiec.2019.6.002","title":"Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems","year":2019,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":327,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Algorithm; Simple (philosophy); Dimension (graph theory); Mathematical optimization; Computer science; Optimization problem; Optimization algorithm; Continuous optimization; Process (computing); Mathematics; Multi-swarm optimization","authors":[{"name":"R. Venkata Rao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06204859873689692,"gpt":0.3061649612971565,"spread":0.2441163625602596,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001657712,0.002149701,0.001971378,0.001679329,0.0005784247,0.001549823,0.002876843,0.00236124,0.005090734],"category_scores_gemma":[0.005227244,0.0006800185,0.001621592,0.001585715,0.00114754,0.001684176,0.002120752,0.001893308,0.001946941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006838855,"about_ca_system_score_gemma":0.001470119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001403195,"about_ca_topic_score_gemma":0.002578373,"domain_scores_codex":[0.9986093,0.0005211755,0.00008420133,0.0001643462,0.0005223052,0.00009862166],"domain_scores_gemma":[0.9985142,0.0007134895,0.0002094072,0.0002428784,0.0002295764,0.00009039045],"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":[0.0002598932,0.0002254242,0.0008968927,0.0006801115,0.000231182,0.0001052274,0.0001787271,0.4161955,0.007486544,0.0623208,0.007200964,0.5042187],"study_design_scores_gemma":[0.0001217626,0.0002369761,0.0003201217,0.00004447886,0.00004510818,0.0001490294,0.00003739692,0.9704189,0.002753499,0.01518336,0.01062582,0.0000636268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004439692,0.000490572,0.9910551,0.0001041554,0.00007794541,0.000153783,0.00004341432,0.0009448189,0.002690531],"genre_scores_gemma":[0.05139292,0.0004449674,0.9433118,0.0001976328,0.00008354903,0.0006522962,0.0001605973,0.00030395,0.003452429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005090734,"threshold_uncertainty_score":0.01703024,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4229942708","doi":"10.1007/978-3-319-41192-7","title":"Search and Optimization by Metaheuristics","year":2016,"lang":"en","type":"book","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":296,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Metaheuristic; Computer science; Mathematical optimization; Mathematics; Artificial intelligence","authors":[{"name":"Ke-Lin Du","is_ca":true},{"name":"M. N. S. Swamy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02225960806332289,"gpt":0.2743247402065508,"spread":0.252065132143228,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005468316,0.002155209,0.001847136,0.001681861,0.0004096772,0.002699853,0.001320226,0.001630534,0.02269649],"category_scores_gemma":[0.001609477,0.000844995,0.001168723,0.003650044,0.001410832,0.003123678,0.001387106,0.003747533,0.01567072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009372629,"about_ca_system_score_gemma":0.0008955981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000908658,"about_ca_topic_score_gemma":0.001309357,"domain_scores_codex":[0.9994423,0.00009832568,0.00002035141,0.00007346267,0.0003382572,0.00002731911],"domain_scores_gemma":[0.9995932,0.0002333979,0.00002580614,0.0000674686,0.0000612841,0.00001886779],"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":[0.00005692495,0.00008258774,0.00009980601,0.001326541,0.0001319645,0.00006184603,0.0000918974,0.03268854,0.002003765,0.2443416,0.2033434,0.5157712],"study_design_scores_gemma":[0.00002818929,0.00004389745,0.0001846931,0.0005602704,0.00004461528,0.0002016873,0.00003221112,0.04222293,0.001054667,0.2390545,0.7165398,0.00003261211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001181409,0.215782,0.4065636,0.003474242,0.007729833,0.0001364413,0.0003170417,0.0009306033,0.3638848],"genre_scores_gemma":[0.02623895,0.147423,0.3095002,0.002175915,0.004784116,0.0005293657,0.0007461103,0.001436337,0.507166],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02269649,"threshold_uncertainty_score":0.07592732,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3035777076","doi":"10.1109/tfuzz.2020.3003506","title":"Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":273,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Mathematical optimization; Job shop scheduling; Fuzzy logic; Selection (genetic algorithm); Particle swarm optimization; Differential evolution; Ant colony optimization algorithms; Algorithm; Metaheuristic; Scheduling (production processes); Cuckoo search; Artificial intelligence; Mathematics","authors":[{"name":"Da Gao","is_ca":false},{"name":"Gai‐Ge Wang","is_ca":false},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03094422708337601,"gpt":0.2688349960740788,"spread":0.2378907689907028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006509179,0.0007644676,0.001121498,0.0005603554,0.0005529615,0.0004985005,0.0007326521,0.0008274805,0.0007789136],"category_scores_gemma":[0.0008845128,0.0002971474,0.0007155014,0.0006585945,0.000306159,0.0005400633,0.0004613182,0.0005719016,0.0001129397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004333437,"about_ca_system_score_gemma":0.0008502606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002507854,"about_ca_topic_score_gemma":0.002574974,"domain_scores_codex":[0.9997227,0.00008301969,0.00002398984,0.00005658088,0.00007666922,0.0000370891],"domain_scores_gemma":[0.9996735,0.0001807135,0.0000374841,0.00002241922,0.00006576344,0.00002006207],"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":[0.0000995319,0.0001525196,0.0009479261,0.000137494,0.00007961474,0.000201907,0.00007471847,0.8858784,0.0100385,0.006980407,0.0007388193,0.09467014],"study_design_scores_gemma":[0.00003082358,0.00007809087,0.0001676083,0.000004457845,0.00001366881,0.00005354091,0.00001085192,0.9965398,0.001576462,0.0009560591,0.0005620168,0.000006550326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07920949,0.0004744865,0.9158454,0.0001438815,0.00006634098,0.0001263296,0.00003113131,0.000195823,0.003907092],"genre_scores_gemma":[0.619339,0.0004128278,0.3774059,0.00006981607,0.00004327708,0.0002380181,0.0001115602,0.00002290424,0.002356702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002507854,"threshold_uncertainty_score":0.004986525,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2066173509","doi":"10.1109/tsmcb.2012.2213808","title":"Gaussian Bare-Bones Differential Evolution","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":268,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Benchmark (surveying); Differential evolution; Gaussian; Task (project management); Mathematical optimization; Algorithm; Computer science; Differential (mechanical device); Gaussian process; Optimization problem; Mathematics; Engineering; Physics; Geology","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hui Sun","is_ca":false},{"name":"Mahamed G. H. Omran","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02244593156429011,"gpt":0.2691512136448326,"spread":0.2467052820805425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006650216,0.0006109323,0.000963596,0.0005026172,0.0003306583,0.00071337,0.001671388,0.001261573,0.003600853],"category_scores_gemma":[0.002000487,0.0003600687,0.0007497672,0.0006518914,0.0007801991,0.0007333873,0.001256862,0.0009575713,0.0008706892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004267486,"about_ca_system_score_gemma":0.0006243147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001915048,"about_ca_topic_score_gemma":0.002001404,"domain_scores_codex":[0.9995843,0.00007342926,0.00001629669,0.00007004344,0.0002135592,0.00004241022],"domain_scores_gemma":[0.9994905,0.0002170669,0.0000438099,0.00007736407,0.0001356472,0.00003557293],"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":[0.0001413655,0.00006376205,0.0006919042,0.000135153,0.0000661318,0.0001647194,0.00008198321,0.855106,0.008501227,0.04131633,0.003155184,0.09057625],"study_design_scores_gemma":[0.00001238729,0.00002453228,0.00008534309,0.000005783346,0.000005379511,0.00004314509,0.000005091218,0.9921003,0.0009018072,0.004322805,0.002485111,0.000008324825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01233845,0.0002014389,0.9797729,0.0001049706,0.00006933523,0.00004244004,0.00007109714,0.0004394721,0.006959848],"genre_scores_gemma":[0.4134485,0.0004175564,0.5725651,0.0003163239,0.00007399191,0.0002251092,0.0003952424,0.0003557338,0.0122024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003600853,"threshold_uncertainty_score":0.0120461,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2893066337","doi":"10.1007/s00500-018-3536-8","title":"Phasor particle swarm optimization: a simple and efficient variant of PSO","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":262,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Particle swarm optimization; Phasor; Multi-swarm optimization; Benchmark (surveying); Mathematical optimization; Mathematics; Derivative-free optimization; Algorithm; Meta-optimization; Computer science; Electric power system; Power (physics)","authors":[{"name":"Mojtaba Ghasemi","is_ca":false},{"name":"Ebrahim Akbari","is_ca":false},{"name":"Abolfazl Rahimnejad","is_ca":true},{"name":"S. Ehsan Razavi","is_ca":false},{"name":"Sahand Ghavidel","is_ca":false},{"name":"Li Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02121247710130197,"gpt":0.2948523991375553,"spread":0.2736399220362534,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003521229,0.0006341381,0.000969358,0.0003732837,0.0002897357,0.0007279058,0.0008442481,0.0008028903,0.002781375],"category_scores_gemma":[0.001035615,0.0002557556,0.0004280776,0.0009478082,0.0003006639,0.0006926288,0.0006741728,0.001002835,0.001463903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002021693,"about_ca_system_score_gemma":0.0004398738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00208523,"about_ca_topic_score_gemma":0.002573081,"domain_scores_codex":[0.9996777,0.00008143877,0.00001831784,0.00004604917,0.0001591899,0.00001731435],"domain_scores_gemma":[0.9998035,0.0000541102,0.00001900991,0.0000510615,0.00005789641,0.00001443354],"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":[0.000221837,0.0001617679,0.0009419307,0.0003162348,0.0001797464,0.0002387622,0.00008682874,0.3571639,0.01785267,0.04276186,0.01180174,0.5682727],"study_design_scores_gemma":[0.00005615005,0.00007007334,0.000429727,0.00001336711,0.00003024438,0.0001311996,0.0000122904,0.9698994,0.003752335,0.008225502,0.01736234,0.00001734319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004313685,0.0003517309,0.9864432,0.0001101416,0.0002220708,0.0001083566,0.00008488332,0.0008369219,0.007529083],"genre_scores_gemma":[0.1659925,0.0007009256,0.8174807,0.0001695503,0.0002109864,0.0002717632,0.0002996281,0.0003389903,0.01453496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002781375,"threshold_uncertainty_score":0.009304583,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2013517059","doi":"10.1016/j.ins.2014.04.013","title":"Multi-strategy ensemble artificial bee colony algorithm","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":260,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ontario Institute of Technology","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Computer science; Mathematical optimization; Artificial bee colony algorithm; Evolutionary algorithm; Set (abstract data type); Process (computing); Population; Algorithm; Local search (optimization); Search algorithm; Artificial intelligence; Mathematics","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Zhijian Wu","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hui Sun","is_ca":false},{"name":"Yong Liu","is_ca":false},{"name":"Jeng‐Shyang Pan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06196425370856051,"gpt":0.3306889462073624,"spread":0.2687246924988019,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009204742,0.0007020808,0.001548454,0.0007691423,0.0007405579,0.0008479543,0.001439767,0.001202108,0.002296877],"category_scores_gemma":[0.001818002,0.0003141878,0.0007548299,0.0008604624,0.000312037,0.001078612,0.0009490675,0.0007558492,0.0004183741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004495924,"about_ca_system_score_gemma":0.0007013408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002235306,"about_ca_topic_score_gemma":0.002814559,"domain_scores_codex":[0.9994799,0.0001575057,0.00002858891,0.00007359243,0.000187479,0.00007279148],"domain_scores_gemma":[0.9993641,0.0002403743,0.00005150479,0.00006193425,0.0002318517,0.00005036334],"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":[0.0001966197,0.0002111799,0.001908139,0.00008293276,0.0002528611,0.0001106889,0.00005886094,0.8224668,0.003691079,0.008134837,0.003506922,0.1593791],"study_design_scores_gemma":[0.000009412244,0.00003080343,0.0001519101,0.000003573184,0.00001676275,0.00001839927,0.000005370782,0.9983741,0.000268424,0.0008254054,0.0002917572,0.000003985931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09833131,0.001561173,0.8795939,0.0004098226,0.0003687492,0.0001183606,0.00009930953,0.0004278445,0.01908949],"genre_scores_gemma":[0.7928898,0.0005006984,0.1988444,0.0002435856,0.0001215326,0.0002132434,0.0002119884,0.00006807722,0.006906664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002296877,"threshold_uncertainty_score":0.007683754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098540440","doi":"10.1109/tsmcb.2010.2056367","title":"Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":229,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Differential evolution; Benchmark (surveying); Computer science; Mathematical optimization; Scalability; Convergence (economics); Evolutionary algorithm; Optimization problem; Global optimization; Evolution strategy; Adaptive strategies; Adaptation (eye); Artificial intelligence; Algorithm; Mathematics","authors":[{"name":"Wenyin Gong","is_ca":false},{"name":"Zhihua Cai","is_ca":false},{"name":"Charles X. Ling","is_ca":true},{"name":"Hui Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01794864544707643,"gpt":0.2511247999847204,"spread":0.233176154537644,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353442,0.0009339147,0.0008265675,0.0007200458,0.0002494229,0.0007089542,0.001107016,0.001045735,0.001362559],"category_scores_gemma":[0.003471045,0.0003180414,0.0007174399,0.0009318406,0.0006409615,0.0007718186,0.0009911285,0.001286231,0.000379982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000509208,"about_ca_system_score_gemma":0.0004957977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009789887,"about_ca_topic_score_gemma":0.0009810303,"domain_scores_codex":[0.9993618,0.0002392043,0.00004190009,0.00006383677,0.0002677263,0.00002554154],"domain_scores_gemma":[0.9991307,0.0005287298,0.00007792776,0.00009259942,0.0001478519,0.00002232647],"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":[0.00004150572,0.00005858081,0.0004269947,0.000175609,0.00007956914,0.0001087639,0.00007330392,0.8612688,0.006443951,0.04664662,0.0009092612,0.08376703],"study_design_scores_gemma":[0.00001189836,0.00002623682,0.00005414963,0.00000907678,0.000007073555,0.00002446098,0.000002403348,0.9920994,0.0007431766,0.004530911,0.002485755,0.000005471412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003498178,0.0005847794,0.9932513,0.00007864459,0.00004690059,0.00004326845,0.00001058827,0.0001205169,0.002365949],"genre_scores_gemma":[0.2308707,0.00125536,0.7639229,0.0001574671,0.00007640626,0.0004398731,0.00006912625,0.00008539283,0.003122892],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001362559,"threshold_uncertainty_score":0.007157803,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153260109","doi":"10.1109/tcyb.2013.2279211","title":"Differential Evolution With Two-Level Parameter Adaptation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Beijing Information Science and Technology University; Huazhong University of Science and Technology; Shenyang Institute of Automation; National Natural Science Foundation of China; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Population; Convergence (economics); Computer science; Adaptive control; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence; Control (management)","authors":[{"name":"Wei–Jie Yu","is_ca":false},{"name":"Meie Shen","is_ca":false},{"name":"Wei–Neng Chen","is_ca":false},{"name":"Zhi‐Hui Zhan","is_ca":false},{"name":"Yue‐Jiao Gong","is_ca":false},{"name":"Ying Lin","is_ca":false},{"name":"Ou Liu","is_ca":false},{"name":"Jun Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03422211415081033,"gpt":0.2601096161588607,"spread":0.2258875020080504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007424201,0.0007334974,0.0009039672,0.0004771372,0.0002347588,0.0008249489,0.001170008,0.0009051244,0.001192311],"category_scores_gemma":[0.002324385,0.0003216849,0.0006096835,0.0005520844,0.0006113301,0.0007149445,0.0009550497,0.0008922334,0.0003457372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005376937,"about_ca_system_score_gemma":0.0003896819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001281183,"about_ca_topic_score_gemma":0.000789927,"domain_scores_codex":[0.9995542,0.0001082019,0.00003295041,0.0001027537,0.0001643861,0.00003741082],"domain_scores_gemma":[0.9994565,0.0002618322,0.00005877675,0.00008839276,0.0001185219,0.00001600209],"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":[0.00004842758,0.00006151292,0.001390435,0.0001087242,0.00008044641,0.0001214711,0.00009237971,0.8681617,0.01045619,0.0144117,0.0007398148,0.1043273],"study_design_scores_gemma":[0.00001306351,0.00003083028,0.0001811742,0.000005114097,0.00001052076,0.00004648192,0.000004177828,0.9948427,0.001445296,0.001994747,0.001415883,0.000009978427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02439478,0.0004838273,0.9697177,0.0001112992,0.00005544264,0.00006842487,0.0000264076,0.0003789366,0.004763243],"genre_scores_gemma":[0.7710205,0.0004613568,0.2229196,0.0001747129,0.00003665577,0.0003358077,0.0001088508,0.0000694965,0.00487295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001281183,"threshold_uncertainty_score":0.003988624,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155370091","doi":"10.1007/s00500-010-0642-7","title":"Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems","year":2010,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":220,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Differential evolution; Initialization; Ode; Opposition (politics); Mathematical optimization; Computer science; Population; Mathematics; Evolutionary algorithm; Algorithm; Applied mathematics","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Zhijian Wu","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0106242622329757,"gpt":0.2517901850349561,"spread":0.2411659228019804,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081965,0.0004695036,0.0008605927,0.0005125307,0.0002711334,0.0006655682,0.0009398346,0.0009779983,0.001418378],"category_scores_gemma":[0.001923887,0.0002556206,0.0005524831,0.0006782946,0.0004910338,0.0005221253,0.000900068,0.0009536071,0.0002343558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000394328,"about_ca_system_score_gemma":0.0003777859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009949105,"about_ca_topic_score_gemma":0.001044934,"domain_scores_codex":[0.9996208,0.0001311655,0.0000148489,0.00002104338,0.000185066,0.00002699368],"domain_scores_gemma":[0.9994445,0.000369418,0.00004194074,0.00002771289,0.00009649862,0.00001981299],"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":[0.0000955182,0.00008051068,0.0003689151,0.0001335299,0.00005658489,0.00009298875,0.00007394546,0.9097999,0.005472382,0.01994836,0.0007168828,0.06316054],"study_design_scores_gemma":[0.000007744957,0.00001793721,0.00003394068,0.000003144878,0.000004086209,0.00001123644,0.000002119857,0.9982843,0.000277058,0.001030165,0.0003262794,0.000001937725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02775759,0.0004391391,0.9631743,0.0001506242,0.0001023895,0.00004271826,0.00001832414,0.0001066642,0.008208241],"genre_scores_gemma":[0.6148564,0.0003948423,0.3789649,0.0001607102,0.00007696508,0.0002326704,0.0000630631,0.00007990221,0.005170454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001418378,"threshold_uncertainty_score":0.005721986,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2088999732","doi":"10.1080/15732470500254535","title":"A modified shuffled frog-leaping optimization algorithm: applications to project management","year":2006,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":188,"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":"Benchmark (surveying); Computer science; Algorithm; Particle swarm optimization; Flowchart; Range (aeronautics); Mathematical optimization; USable; Domain (mathematical analysis); Evolutionary algorithm; Mathematics; Artificial intelligence; Engineering","authors":[{"name":"Emad Elbeltagi","is_ca":false},{"name":"Tarek Hegazy","is_ca":true},{"name":"Donald E. Grierson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005988490860227859,"gpt":0.2329618345007944,"spread":0.2269733436405665,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006106395,0.0006110377,0.0006848224,0.0004523038,0.0004744776,0.0006567367,0.001134054,0.00143839,0.002830201],"category_scores_gemma":[0.001719984,0.0002660384,0.0005859762,0.00103618,0.0004158072,0.0007455582,0.0007449126,0.0008306816,0.0006698351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003461697,"about_ca_system_score_gemma":0.0007186414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002065054,"about_ca_topic_score_gemma":0.001734226,"domain_scores_codex":[0.9997175,0.0001153029,0.00001640734,0.00003757348,0.00009559633,0.00001748568],"domain_scores_gemma":[0.9996421,0.0001697953,0.00004290751,0.0000376771,0.00008482589,0.00002259866],"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":[0.00004797898,0.00004916541,0.0005281885,0.0001212031,0.00005744249,0.0001274669,0.00007530322,0.7770162,0.003807757,0.02251107,0.002684224,0.1929739],"study_design_scores_gemma":[0.00002284566,0.00005356342,0.0001878247,0.00001598498,0.00001036562,0.0001157235,0.00001054004,0.9789242,0.001324172,0.01050039,0.008816583,0.00001779146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006806046,0.0004617858,0.9882979,0.0002429642,0.00004832424,0.00007088755,0.00004581281,0.0003538079,0.003672478],"genre_scores_gemma":[0.1318412,0.0007446803,0.8609252,0.0001389453,0.00004462384,0.0003361403,0.0001046564,0.0001257124,0.005738808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002830201,"threshold_uncertainty_score":0.009467959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994895518","doi":"10.1007/s10898-007-9234-1","title":"Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search","year":2007,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Air Force Office of Scientific Research","keywords":"Mathematics; Variable neighborhood search; Mathematical optimization; Metaheuristic; Convergence (economics); Local search (optimization); Variable (mathematics); Guided Local Search; Algorithm","authors":[{"name":"Charles Audet","is_ca":true},{"name":"Vincent Béchard","is_ca":true},{"name":"Sébastien Le Digabel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02169005376408899,"gpt":0.2979204776592476,"spread":0.2762304238951586,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006809657,0.000515179,0.0009296249,0.0006402929,0.0004171714,0.0006307816,0.001034767,0.0009996236,0.002264737],"category_scores_gemma":[0.00216161,0.0004668385,0.0005224016,0.0005627158,0.0005957462,0.0008694226,0.0007840968,0.000689013,0.0002641147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004297268,"about_ca_system_score_gemma":0.0005349988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002425428,"about_ca_topic_score_gemma":0.003502799,"domain_scores_codex":[0.999763,0.00009480579,0.000008408339,0.00002736666,0.0000934366,0.00001300042],"domain_scores_gemma":[0.9993697,0.000416061,0.00004988132,0.00004521253,0.00009936777,0.00001977881],"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":[0.0001209324,0.00008448622,0.0003802429,0.00009678913,0.00006681492,0.00005774172,0.00006079334,0.883984,0.003023651,0.03233323,0.001394908,0.07839645],"study_design_scores_gemma":[0.00000841282,0.00001184817,0.000029978,0.000001997913,0.00000311802,0.000005814073,0.000002162143,0.9977348,0.0001598811,0.001796435,0.0002436842,0.000001945514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02208251,0.0003674022,0.97013,0.0001053763,0.000110113,0.0000538091,0.00001949861,0.0001812133,0.006950123],"genre_scores_gemma":[0.3814583,0.0002581498,0.611039,0.0001188363,0.00008430937,0.000279533,0.00006293492,0.0001892534,0.006509734],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002425428,"threshold_uncertainty_score":0.007576287,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2154369526","doi":"10.1109/cec.2006.1688554","title":"Opposition-Based Differential Evolution Algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":184,"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":"Differential evolution; Initialization; Evolutionary algorithm; Computer science; Benchmark (surveying); Population; Evolutionary computation; Swarm intelligence; Mathematical optimization; Computational intelligence; Algorithm; Artificial intelligence; Cultural algorithm; Machine learning; Optimization problem; Particle swarm optimization; Meta-optimization; Mathematics","authors":[{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"M.M.A. Salama","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01136418331225883,"gpt":0.2479830212986889,"spread":0.23661883798643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005626142,0.0005902453,0.001137524,0.000443878,0.0003381678,0.0008938527,0.001232782,0.001000309,0.002951191],"category_scores_gemma":[0.00144628,0.0002621823,0.000482541,0.0006381263,0.0005195999,0.0006399973,0.0009627659,0.0008417187,0.0008276961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004072758,"about_ca_system_score_gemma":0.0003626262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006923886,"about_ca_topic_score_gemma":0.0006333336,"domain_scores_codex":[0.9996513,0.00009407301,0.00001847903,0.00004543117,0.0001642005,0.00002645484],"domain_scores_gemma":[0.9996935,0.0001577736,0.00003716201,0.0000291725,0.00006637727,0.0000160467],"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":[0.00007612183,0.00008102524,0.0005291638,0.0002156155,0.00006342609,0.0001323263,0.00008380775,0.6942729,0.006086124,0.08259121,0.002900375,0.212968],"study_design_scores_gemma":[0.00002596822,0.00003662057,0.00008362962,0.00001250316,0.0000095184,0.00006337197,0.000007200581,0.9806459,0.001121907,0.01031681,0.007666334,0.00001022791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00525869,0.0006985576,0.9803423,0.0001266155,0.00009226798,0.00007485954,0.00003712066,0.0002358052,0.01313366],"genre_scores_gemma":[0.4309149,0.001705342,0.5497909,0.0003425345,0.000110206,0.0005209054,0.0002076343,0.0001102871,0.01629727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002951191,"threshold_uncertainty_score":0.009872735,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138588773","doi":"10.1007/s10489-014-0613-2","title":"Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution","year":2014,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":169,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Curse of dimensionality; Differential evolution; Computer science; Particle swarm optimization; Divergence (linguistics); Population; Mathematical optimization; Swarm behaviour; Algorithm; Artificial intelligence; Mathematics","authors":[{"name":"Stephen Chen","is_ca":true},{"name":"James Montgomery","is_ca":false},{"name":"Antonio Bolufé-Röhler","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02736205520661054,"gpt":0.2593924310048275,"spread":0.2320303757982169,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004009423,0.0005250773,0.0007791378,0.0008875145,0.0005784307,0.0011988,0.0003768194,0.001024765,0.000776485],"category_scores_gemma":[0.0615416,0.0003233474,0.0004035956,0.001249022,0.00118008,0.003040781,0.001092482,0.00102699,0.000138221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006594647,"about_ca_system_score_gemma":0.0006271615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002144262,"about_ca_topic_score_gemma":0.001876825,"domain_scores_codex":[0.997687,0.001223891,0.0001475496,0.0001783304,0.0006544753,0.0001087558],"domain_scores_gemma":[0.9421757,0.05007955,0.001750873,0.003308406,0.002396642,0.000288792],"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":[0.0007879927,0.0003679066,0.02337108,0.0006620308,0.0002532314,0.0003835721,0.0007876997,0.7695581,0.01883749,0.03845323,0.002249619,0.1442881],"study_design_scores_gemma":[0.00002023825,0.0001834089,0.008397265,0.00004028637,0.00005560001,0.0001922995,0.00009743821,0.9659481,0.01323021,0.01070004,0.001083721,0.00005153472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6889029,0.004323776,0.2923732,0.001345328,0.000343788,0.00008211078,0.0002393797,0.0004244688,0.01196506],"genre_scores_gemma":[0.958481,0.0006068082,0.03987869,0.00006436654,0.00003775965,0.00003299152,0.00006475158,0.000109227,0.0007244669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004009423,"threshold_uncertainty_score":0.02120411,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083110812","doi":"10.1016/j.amc.2010.03.123","title":"A real-coded biogeography-based optimization with mutation","year":2010,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Benchmark (surveying); Mutation; Computer science; Operator (biology); Range (aeronautics); Domain (mathematical analysis); Global optimization; Mathematical optimization; Population; Optimization problem; Algorithm; Mathematics; Engineering; Biology; Genetics; Geography; Cartography","authors":[{"name":"Wenyin Gong","is_ca":false},{"name":"Zhihua Cai","is_ca":false},{"name":"Charles X. Ling","is_ca":true},{"name":"Hui Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01263419133212613,"gpt":0.2575149655768707,"spread":0.2448807742447446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006464103,0.0005929851,0.0007493382,0.0005588026,0.0004215592,0.0007465276,0.001507057,0.001391802,0.002710794],"category_scores_gemma":[0.001232604,0.0003103539,0.0006279381,0.0005969398,0.0007405631,0.0005903175,0.0009432233,0.0006716467,0.0002976532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006072685,"about_ca_system_score_gemma":0.0007257031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003114693,"about_ca_topic_score_gemma":0.002159257,"domain_scores_codex":[0.9997839,0.00007174075,0.000008713663,0.00003435796,0.00008198852,0.00001928155],"domain_scores_gemma":[0.99977,0.0001020346,0.00001970561,0.00002824513,0.00006234713,0.00001770099],"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":[0.00006278556,0.00005858893,0.0003447833,0.00008120485,0.00005089575,0.00007577156,0.0000465706,0.9323156,0.006153544,0.01871476,0.0006960501,0.04139942],"study_design_scores_gemma":[0.00001904476,0.00002362327,0.0000587326,0.000004415022,0.00000817893,0.00001787468,0.000002452829,0.9979479,0.0004961046,0.0009531064,0.0004642501,0.000004227045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03264212,0.0003008177,0.953689,0.0002715154,0.0002075362,0.0001540103,0.00004386832,0.000336412,0.01235472],"genre_scores_gemma":[0.509058,0.0001975953,0.4836882,0.00017928,0.00006708794,0.0003368878,0.00005910939,0.0001302002,0.006283749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003114693,"threshold_uncertainty_score":0.009068489,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2770783293","doi":"10.1007/s11721-017-0150-9","title":"Self-adaptive particle swarm optimization: a review and analysis of convergence","year":2017,"lang":"en","type":"review","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation","keywords":"Particle swarm optimization; Computer science; Flocking (texture); A priori and a posteriori; Mathematical optimization; Convergence (economics); Population; Swarm behaviour; Multi-swarm optimization; Algorithm; Mathematics","authors":[{"name":"Kyle Robert Harrison","is_ca":false},{"name":"Andries P. Engelbrecht","is_ca":false},{"name":"Beatrice Ombuki-Berman","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.15445702536842,"gpt":0.4157993763360676,"spread":0.2613423509676475,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001544468,0.001050482,0.001780737,0.002045058,0.0003309737,0.001756109,0.001608146,0.00142249,0.002381037],"category_scores_gemma":[0.003732527,0.0004053048,0.0008029199,0.004322752,0.000706201,0.001865026,0.001057117,0.001217014,0.001092434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006178451,"about_ca_system_score_gemma":0.001733294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001558179,"about_ca_topic_score_gemma":0.00134629,"domain_scores_codex":[0.9994067,0.000114752,0.00008968136,0.00009545466,0.0002643472,0.00002900041],"domain_scores_gemma":[0.9983797,0.0009877644,0.0001323885,0.00006442483,0.0003968321,0.00003898511],"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":[0.0000598217,0.00008128888,0.0004518304,0.01276774,0.0001239237,0.00007152602,0.00006285118,0.006563284,0.001004235,0.01028423,0.00867842,0.9598508],"study_design_scores_gemma":[0.00008495071,0.0007608062,0.003159459,0.01156514,0.0009212254,0.001882855,0.0002866214,0.04457279,0.006139386,0.03295736,0.8974964,0.0001731138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009329474,0.9805341,0.01431868,0.0005805902,0.0003554232,0.00002974849,0.00003816053,0.00004153656,0.003168686],"genre_scores_gemma":[0.01121533,0.9739583,0.01287166,0.0002994825,0.0005977519,0.00005028782,0.0000781109,0.00001993436,0.0009091643],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002381037,"threshold_uncertainty_score":0.008167982,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3083389769","doi":"10.3390/app10186173","title":"A Spring Search Algorithm Applied to Engineering Optimization Problems","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Mathematical optimization; Spring (device); Algorithm; Mathematics; Engineering; Mechanical engineering","authors":[{"name":"Mohammad Dehghani","is_ca":false},{"name":"Zeinab Montazeri","is_ca":false},{"name":"Gaurav Dhiman","is_ca":false},{"name":"O.P. Malik","is_ca":true},{"name":"Rubén Morales-Menéndez","is_ca":false},{"name":"Ricardo A. Ramírez-Mendoza","is_ca":false},{"name":"Ali Dehghani","is_ca":false},{"name":"Josep M. Guerrero","is_ca":false},{"name":"Lizeth Parra-Arroyo","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0391604103543382,"gpt":0.2599731436853561,"spread":0.2208127333310179,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001002947,0.001388388,0.001047178,0.0009443482,0.0007994339,0.0009169413,0.0008932256,0.001565204,0.004702879],"category_scores_gemma":[0.002616683,0.0004740018,0.001105916,0.001263296,0.000854607,0.0008684864,0.001199206,0.001410792,0.001393977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005850727,"about_ca_system_score_gemma":0.001582108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00314224,"about_ca_topic_score_gemma":0.00275844,"domain_scores_codex":[0.999438,0.0001974447,0.00004339321,0.00007770531,0.0002103104,0.00003321097],"domain_scores_gemma":[0.9993945,0.0003487126,0.00004490109,0.00004906164,0.0001397162,0.00002297921],"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":[0.00005887153,0.00007409257,0.0009125069,0.0002601907,0.0001307618,0.0001983465,0.0001139462,0.7527279,0.003667969,0.06538389,0.006391313,0.1700802],"study_design_scores_gemma":[0.00002414286,0.00004570276,0.0001392285,0.00003302258,0.00001557627,0.00006299064,0.00001414769,0.9715647,0.0008559353,0.01687604,0.01035848,0.00001005008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003155171,0.000569027,0.9871428,0.0002920005,0.0001338638,0.00008280134,0.00004569072,0.0003506411,0.008228127],"genre_scores_gemma":[0.1610595,0.001417095,0.8224576,0.0003445281,0.0002128849,0.0007063446,0.0002508599,0.0002990252,0.01325217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004702879,"threshold_uncertainty_score":0.01573265,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056678525","doi":"10.1016/j.ins.2006.09.016","title":"Exchange strategies for multiple Ant Colony System","year":2006,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":140,"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":"Ant colony optimization algorithms; Travelling salesman problem; Computer science; Weighting; Ant colony; Scheme (mathematics); Local search (optimization); Metaheuristic; Mathematical optimization; Artificial intelligence; Machine learning; Algorithm; Mathematics","authors":[{"name":"Ismail Ellabib","is_ca":true},{"name":"Paul H. Calamai","is_ca":true},{"name":"Otman Basir","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03510963660855574,"gpt":0.2952731545360752,"spread":0.2601635179275195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006761959,0.0006342786,0.0006759801,0.0005905511,0.000675501,0.0009296905,0.001190264,0.0009578226,0.003979328],"category_scores_gemma":[0.001881432,0.0002574486,0.0003504778,0.0005646416,0.0004575156,0.001004428,0.0008475832,0.0005622081,0.0003444687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005509838,"about_ca_system_score_gemma":0.000475531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720975,"about_ca_topic_score_gemma":0.001244712,"domain_scores_codex":[0.9996874,0.0001331009,0.00001450909,0.00003046055,0.00009076243,0.0000437006],"domain_scores_gemma":[0.9995689,0.0002254351,0.00005007855,0.0000328629,0.00009015395,0.0000325359],"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":[0.0001691625,0.0001272408,0.0004487816,0.0001601632,0.00007664483,0.0001730006,0.0002589232,0.8225521,0.004924398,0.09110589,0.002569691,0.07743397],"study_design_scores_gemma":[0.00002072708,0.00005219381,0.00007749219,0.000005565486,0.00001140266,0.00003595757,0.0000281284,0.9857314,0.0003981051,0.01272296,0.0009106991,0.000005354829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1347568,0.001412247,0.8273141,0.0006949479,0.0001847693,0.0002162313,0.00004902351,0.0002505035,0.03512123],"genre_scores_gemma":[0.9177901,0.0003870265,0.06929144,0.00007599818,0.00003541245,0.0001690287,0.00003391182,0.00005146597,0.0121656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003979328,"threshold_uncertainty_score":0.01331216,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7021242","doi":"10.13140/2.1.1320.2568","title":"Swarm Intelligence: Concepts, Models and Applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence","authors":[{"name":"Hazem Ahmed","is_ca":false},{"name":"Janice Glasgow","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06248270455043398,"gpt":0.3585503422520119,"spread":0.296067637701578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007186784,0.001042586,0.000859016,0.001423276,0.0004934347,0.003214311,0.0007107859,0.001763265,0.004794496],"category_scores_gemma":[0.00189028,0.0003818242,0.0006794902,0.002230293,0.001950639,0.002056241,0.001313038,0.001866642,0.002318745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009026174,"about_ca_system_score_gemma":0.0007873672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001404211,"about_ca_topic_score_gemma":0.0007752665,"domain_scores_codex":[0.9993112,0.0001860408,0.00005282376,0.000114148,0.0002912989,0.00004461393],"domain_scores_gemma":[0.9994603,0.000255623,0.00008725946,0.0000589465,0.000105691,0.00003229564],"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":[0.00006467039,0.00005525098,0.0013626,0.00167466,0.0001115832,0.0003316436,0.000603699,0.09235904,0.003551189,0.5473525,0.0536731,0.29886],"study_design_scores_gemma":[0.0000228863,0.0001121655,0.001524779,0.0006013869,0.00005641414,0.0007469705,0.0002963398,0.08037753,0.001317487,0.5536376,0.361246,0.00006049746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009948434,0.1904379,0.6169727,0.01136532,0.003602653,0.0002774764,0.001097153,0.001024565,0.1652738],"genre_scores_gemma":[0.4583496,0.1946664,0.2513228,0.002604131,0.004776523,0.0009198494,0.001958303,0.0004420529,0.0849603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004794496,"threshold_uncertainty_score":0.01603925,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391145932","doi":"10.3390/biomimetics9020065","title":"Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2024,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Metaheuristic; Algorithm; Test suite; Computer science; Evolutionary algorithm; Optimization algorithm; Optimization problem; Mathematical optimization; Artificial intelligence; Test case; Mathematics; Machine learning","authors":[{"name":"Osama Al-Baik","is_ca":false},{"name":"Saleh Ali Alomari","is_ca":false},{"name":"Omar Alssayed","is_ca":false},{"name":"Saikat Gochhait","is_ca":false},{"name":"Irina Leonova","is_ca":false},{"name":"Uma Dutta","is_ca":false},{"name":"O.P. Malik","is_ca":true},{"name":"Zeinab Montazeri","is_ca":false},{"name":"Mohammad Dehghani","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0328331190701672,"gpt":0.2881612102099395,"spread":0.2553280911397723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008339377,0.001283038,0.001214311,0.001051081,0.0005121524,0.000982467,0.001646757,0.001758704,0.002724025],"category_scores_gemma":[0.001674135,0.0004509722,0.001289818,0.001251225,0.0006512586,0.001178582,0.001145312,0.001524327,0.0006655573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007420682,"about_ca_system_score_gemma":0.001940385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004276724,"about_ca_topic_score_gemma":0.003877057,"domain_scores_codex":[0.9995989,0.0001219022,0.00002979386,0.00006660999,0.0001447,0.00003808688],"domain_scores_gemma":[0.9996386,0.0001763175,0.00005144274,0.00003593523,0.00007812048,0.00001947711],"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":[0.00007369729,0.00007689331,0.0009402679,0.0002894742,0.0001889296,0.00009270354,0.0000582579,0.8380838,0.003780222,0.01390405,0.004834717,0.137677],"study_design_scores_gemma":[0.00004001507,0.00005117245,0.0001406795,0.00002641327,0.00002272278,0.00005602406,0.00001288754,0.9880838,0.0007885713,0.003993694,0.006773862,0.00001009853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01136564,0.001794385,0.9789627,0.0003935413,0.0001483422,0.0001232002,0.0001484964,0.0007444008,0.00631938],"genre_scores_gemma":[0.1509514,0.001643077,0.8383847,0.0005349283,0.00010597,0.00076019,0.0005958879,0.0003022393,0.006721597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004276724,"threshold_uncertainty_score":0.009112775,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2073491843","doi":"10.1007/s11590-008-0089-2","title":"Mesh adaptive direct search algorithms for mixed variable optimization","year":2008,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subsequence; Variable (mathematics); Algorithm; Iterated function; Convergence (economics); Mathematics; Categorical variable; Mathematical optimization; Class (philosophy); Computer science; Artificial intelligence","authors":[{"name":"Mark A. Abramson","is_ca":false},{"name":"Charles Audet","is_ca":true},{"name":"James W. Chrissis","is_ca":false},{"name":"Jennifer G. Walston","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04295862349070323,"gpt":0.27375931317915,"spread":0.2308006896884468,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008223174,0.000764899,0.0009776424,0.0007214831,0.0004365897,0.000891683,0.001323662,0.001491499,0.004979472],"category_scores_gemma":[0.00487436,0.0005448836,0.0005453954,0.001039878,0.0006807603,0.001006483,0.001450167,0.001704527,0.0008951297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004938229,"about_ca_system_score_gemma":0.0005200109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002294822,"about_ca_topic_score_gemma":0.002751891,"domain_scores_codex":[0.9996264,0.000182054,0.00001409523,0.00002860726,0.0001268351,0.00002205357],"domain_scores_gemma":[0.9988441,0.0008223177,0.00006386678,0.00008942289,0.0001503265,0.00002999889],"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":[0.0001117778,0.0000495522,0.0003365754,0.0001399698,0.00008034515,0.00004526441,0.00006102412,0.8044745,0.001497344,0.05795414,0.004506757,0.1307427],"study_design_scores_gemma":[0.00001337025,0.000009456747,0.00003032911,0.000006277308,0.000004079217,0.000006450817,0.000003668841,0.9884343,0.0001401868,0.01016759,0.001181414,0.000002841251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004479685,0.0008708935,0.9883153,0.0001777229,0.000152246,0.0000301686,0.00003343017,0.0001766491,0.005763876],"genre_scores_gemma":[0.2298672,0.001054511,0.7548122,0.0002431503,0.000239434,0.000510974,0.0001753535,0.0002983017,0.01279889],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004979472,"threshold_uncertainty_score":0.01665795,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2618467698","doi":"10.1007/s12293-017-0234-5","title":"A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function","year":2017,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Premature convergence; Crossover; Maxima and minima; Algorithm; Population; Genetic algorithm; Population-based incremental learning; Mathematical optimization; Computer science; Benchmark (surveying); Curse of dimensionality; Operator (biology); Convergence (economics); Mathematics; Artificial intelligence","authors":[{"name":"Mohamed A. Tawhid","is_ca":true},{"name":"Ahmed F. Ali","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0210543000855937,"gpt":0.2711286795990178,"spread":0.2500743795134241,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006238335,0.00060274,0.00117167,0.0008007289,0.0004368832,0.0007489477,0.001277591,0.001739429,0.002594984],"category_scores_gemma":[0.000769774,0.0003733227,0.0007817206,0.0009138841,0.0004460704,0.0007444198,0.0007192286,0.0007228454,0.0006513545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004491221,"about_ca_system_score_gemma":0.0006347676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002236996,"about_ca_topic_score_gemma":0.002229369,"domain_scores_codex":[0.9997055,0.00007125396,0.00001175253,0.00003654836,0.0001518161,0.00002309279],"domain_scores_gemma":[0.9998627,0.00005714991,0.00001178908,0.00001675751,0.00004288122,0.000008736868],"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":[0.0001359743,0.00009939056,0.000472845,0.0001823371,0.000164277,0.0001896523,0.00006350255,0.8098974,0.0158872,0.02121896,0.003005912,0.1486825],"study_design_scores_gemma":[0.00001716154,0.0000372302,0.00009028826,0.000006357017,0.00001716942,0.00003299624,0.00000377957,0.9961553,0.001100622,0.001364262,0.001168408,0.000006324195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01553629,0.0007802204,0.975054,0.0001836572,0.0001278815,0.00007419106,0.00003514773,0.0004790617,0.007729596],"genre_scores_gemma":[0.3533109,0.0005869452,0.6331524,0.0002361009,0.0001128508,0.0002751616,0.0001046706,0.0001933374,0.01202762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002594984,"threshold_uncertainty_score":0.008681059,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2094252454","doi":"10.5267/j.ijiec.2012.09.001","title":"Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Optimization algorithm; Computer science; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","authors":[{"name":"R. Venkata Rao","is_ca":false},{"name":"Vivek Patel","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04034164815511341,"gpt":0.3085967055323746,"spread":0.2682550573772612,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002497943,0.0009600043,0.001216077,0.001102468,0.0005958609,0.0008923758,0.001042142,0.001519831,0.00128611],"category_scores_gemma":[0.00555477,0.0002199561,0.0006328042,0.0009092595,0.0006631147,0.0009236046,0.0006217507,0.0006724359,0.0002481754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007863876,"about_ca_system_score_gemma":0.001660411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00627173,"about_ca_topic_score_gemma":0.004744984,"domain_scores_codex":[0.9990693,0.0003588184,0.00008059871,0.0001102726,0.0002790731,0.0001019683],"domain_scores_gemma":[0.9975094,0.001607147,0.0001547467,0.0001507891,0.0004864342,0.00009145858],"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":[0.0003148259,0.0002635529,0.001517574,0.0002031186,0.0001205106,0.00004380616,0.00007658489,0.9252742,0.001917942,0.00215054,0.0006401974,0.06747709],"study_design_scores_gemma":[0.00003185532,0.0001019206,0.0003688633,0.0000109553,0.00001522209,0.00001676215,0.00001920804,0.9974172,0.001337353,0.0003830749,0.000289867,0.000007762703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5164575,0.003137067,0.456733,0.0005568948,0.0002049623,0.0002060485,0.0001376571,0.001074015,0.02149291],"genre_scores_gemma":[0.8040621,0.0006388192,0.1921115,0.0001200666,0.00002609815,0.0001951596,0.0002758328,0.0001377454,0.002432733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00627173,"threshold_uncertainty_score":0.01321054,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4254212488","doi":"10.1007/978-3-540-68830-3_6","title":"Opposition-Based Differential Evolution","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":123,"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":"Ode; Initialization; Opposition (politics); Differential evolution; Suite; Test suite; Mathematical optimization; Benchmark (surveying); Population; Computer science; Algorithm; Mathematics; Applied mathematics; Test case; Law","authors":[{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"M.M.A. Salama","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.126897753637026,"gpt":0.3686390694557175,"spread":0.2417413158186915,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003025191,0.0003889891,0.0005744633,0.0003332334,0.0002247236,0.0008338455,0.0008830845,0.0007281164,0.005348167],"category_scores_gemma":[0.0006912605,0.0001870437,0.0003273698,0.0006023929,0.0005977203,0.0005201589,0.0006743807,0.001052446,0.00118696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003995019,"about_ca_system_score_gemma":0.0002696452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004748835,"about_ca_topic_score_gemma":0.0005730808,"domain_scores_codex":[0.9998521,0.00002821142,0.000004724702,0.00001563571,0.00008920072,0.00001016492],"domain_scores_gemma":[0.9998916,0.00005667389,0.000006998941,0.00001099735,0.00002576718,0.000007934267],"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":[0.00006337761,0.00006658,0.0002322997,0.0002446508,0.00004295981,0.0001063731,0.0001237156,0.2208634,0.008691371,0.4570939,0.0147813,0.2976902],"study_design_scores_gemma":[0.0000334131,0.00007957705,0.0002518174,0.00006693351,0.00001823377,0.0002195207,0.0000236075,0.7955711,0.003008836,0.1186213,0.0820808,0.00002490849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008433635,0.005120977,0.7655209,0.0005853436,0.0008816612,0.00007384843,0.00006580015,0.0002961268,0.2190218],"genre_scores_gemma":[0.4274801,0.007412313,0.3779903,0.0006011318,0.0003937531,0.0002983684,0.000351335,0.0003258608,0.1851468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005348167,"threshold_uncertainty_score":0.01789141,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083028490","doi":"10.1016/j.jpdc.2012.02.019","title":"Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems","year":2012,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":120,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Differential evolution; Speedup; Benchmark (surveying); Computational complexity theory; Graphics; Parallel computing; Optimization problem; Execution time; Mathematical optimization; Algorithm; Mathematics; Computer graphics (images)","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Zhijian Wu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01425295086016035,"gpt":0.2382615037605564,"spread":0.2240085529003961,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00141381,0.0005892972,0.001018337,0.0005713843,0.0004232609,0.0006317403,0.001150629,0.0009492646,0.0009941903],"category_scores_gemma":[0.00261192,0.0004150118,0.0006102191,0.0007809838,0.0007925735,0.0007086053,0.0009840046,0.001024679,0.0001540044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005382113,"about_ca_system_score_gemma":0.0006028435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002574914,"about_ca_topic_score_gemma":0.002590309,"domain_scores_codex":[0.999644,0.0001369248,0.00002072601,0.0000374771,0.000131425,0.00002940447],"domain_scores_gemma":[0.9992657,0.0004373467,0.00006981775,0.00005311646,0.0001466537,0.00002735031],"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":[0.00005689889,0.0000661184,0.0002304832,0.00004935397,0.00004063512,0.00003351229,0.00003748169,0.9556822,0.001479594,0.005576372,0.0002902811,0.03645718],"study_design_scores_gemma":[0.000008658118,0.00001568028,0.00002728991,0.000001588136,0.000003010146,0.000005199056,0.000001744962,0.9988275,0.000162477,0.0008388064,0.0001062718,0.000001767507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05160889,0.0005259758,0.9414863,0.0002077968,0.0001362165,0.0000793643,0.00001590645,0.0002064264,0.005733124],"genre_scores_gemma":[0.7522427,0.0002870147,0.2440888,0.0001262654,0.00005910301,0.000248066,0.00004418601,0.00007055204,0.002833138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002574914,"threshold_uncertainty_score":0.007477045,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148600622","doi":"10.1109/cec.2006.1688534","title":"Opposition-Based Differential Evolution for Optimization of Noisy Problems","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":120,"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":"Russian Science Foundation","keywords":"Initialization; Differential evolution; Computer science; Benchmark (surveying); Mathematical optimization; Population; Optimization problem; Convergence (economics); Optimization algorithm; Algorithm; Artificial intelligence; Mathematics","authors":[{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"M.M.A. Salama","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01455068373189594,"gpt":0.2494523002541933,"spread":0.2349016165222974,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001053785,0.0005949734,0.0008841812,0.0004881197,0.0003109687,0.0006263503,0.0007557001,0.0008182977,0.001090011],"category_scores_gemma":[0.002237935,0.0002435426,0.000543116,0.0006985982,0.0005862847,0.000555923,0.0009001778,0.001132512,0.0002466546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004843523,"about_ca_system_score_gemma":0.0003748208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006557347,"about_ca_topic_score_gemma":0.0005587824,"domain_scores_codex":[0.999567,0.0001726904,0.0000211788,0.00004068543,0.0001774014,0.00002105357],"domain_scores_gemma":[0.999447,0.000384092,0.00005334537,0.00003168616,0.00006838788,0.00001544373],"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":[0.0000367235,0.00003516685,0.0003590564,0.0001484248,0.00004234923,0.0001075132,0.00008811097,0.8802868,0.005454262,0.04467652,0.0008171193,0.06794793],"study_design_scores_gemma":[0.000009009672,0.00002261325,0.00005435248,0.00000776829,0.000005092451,0.00002573151,0.000004528512,0.9895177,0.0007863662,0.007443025,0.002117787,0.00000589229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004397307,0.0003383759,0.9920213,0.00009238667,0.00003861134,0.00002227462,0.000009594694,0.00006575244,0.003014482],"genre_scores_gemma":[0.4211079,0.001115756,0.5724615,0.0002182156,0.0001063727,0.0004554066,0.00009486209,0.00008806169,0.004351966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001090011,"threshold_uncertainty_score":0.005572975,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153654605","doi":"10.1016/j.dam.2005.05.020","title":"First vs. best improvement: An empirical study","year":2005,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristics; Constructive; Travelling salesman problem; Mathematics; Enhanced Data Rates for GSM Evolution; Heuristic; Mathematical optimization; Greedy algorithm; Algorithm; Combinatorics; Computer science; Artificial intelligence; Process (computing)","authors":[{"name":"Pierre Hansen","is_ca":true},{"name":"Nenad Mladenović","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03618088057861051,"gpt":0.3343979954373477,"spread":0.2982171148587372,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01761632,0.0008676432,0.001969558,0.004463752,0.001327844,0.002048174,0.00224899,0.001920997,0.005765733],"category_scores_gemma":[0.1054603,0.0004904683,0.001801585,0.003129821,0.001010893,0.004367893,0.001226549,0.003541416,0.001507354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001680406,"about_ca_system_score_gemma":0.001479418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002216036,"about_ca_topic_score_gemma":0.003118022,"domain_scores_codex":[0.9898432,0.003713263,0.0007181701,0.001424391,0.003389417,0.0009115223],"domain_scores_gemma":[0.6548538,0.294845,0.01121482,0.023351,0.01104132,0.004694001],"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":[0.01399885,0.006157978,0.2621965,0.002184174,0.001681499,0.0004693818,0.001514112,0.1098241,0.00425588,0.01037535,0.02041923,0.5669231],"study_design_scores_gemma":[0.001569916,0.0236206,0.3929496,0.0006710176,0.00351511,0.005431843,0.003045578,0.4978823,0.02111485,0.02811505,0.02173116,0.0003530141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581467,0.008086776,0.01447681,0.0008260066,0.0001592618,0.0002177962,0.00213207,0.001086635,0.01486806],"genre_scores_gemma":[0.9815547,0.0007355266,0.01347094,0.00009824863,0.00007219275,0.00006051844,0.001152574,0.0003241666,0.002531114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01761632,"threshold_uncertainty_score":0.0931651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165687335","doi":"10.1109/tevc.2009.2017517","title":"Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cluster analysis; Local optimum; Mathematical optimization; Genetic algorithm; Population; Artificial intelligence; Algorithm; Mathematics; Machine learning","authors":[{"name":"Jie Yao","is_ca":true},{"name":"Nawwaf Kharma","is_ca":true},{"name":"Peter Grogono","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01815384982913662,"gpt":0.2780752823084465,"spread":0.2599214324793099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001311211,0.0009597199,0.0007694697,0.001039375,0.000515644,0.0007534968,0.001269253,0.001358229,0.001990734],"category_scores_gemma":[0.002077529,0.0003077912,0.0007321153,0.0009799999,0.0005686372,0.0006661948,0.0008115685,0.001220209,0.0005607543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000831533,"about_ca_system_score_gemma":0.0007987269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002184038,"about_ca_topic_score_gemma":0.002061784,"domain_scores_codex":[0.9995191,0.0002390623,0.00001756054,0.0000480787,0.0001423463,0.00003385364],"domain_scores_gemma":[0.9995658,0.0002415861,0.00003916704,0.000033028,0.0001004,0.00001995969],"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":[0.00003579979,0.00004779471,0.0005278365,0.00009170071,0.0001077646,0.00008455555,0.0001012996,0.8678176,0.003899591,0.02356572,0.001515336,0.102205],"study_design_scores_gemma":[0.00001329141,0.00002793988,0.0001076409,0.00001093843,0.00001252756,0.00003980636,0.00000926355,0.9926184,0.0005878315,0.004151711,0.002411726,0.000008896256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00699664,0.0005145943,0.9889846,0.000136095,0.00004269508,0.00005131518,0.00002429039,0.0002112499,0.003038503],"genre_scores_gemma":[0.2426919,0.000682249,0.7495264,0.0002425526,0.00005449457,0.0005398022,0.0001938273,0.0001309121,0.005937896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002184038,"threshold_uncertainty_score":0.006934464,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392190020","doi":"10.1016/j.swevo.2024.101517","title":"Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":114,"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":"Shaanxi Key Science and Technology Innovation Team Project","keywords":"Reinforcement learning; Computer science; Evolutionary algorithm; Evolutionary computation; Artificial intelligence; Machine learning; Adaptation (eye); Optimization problem; Population; Algorithm","authors":[{"name":"Yanjie Song","is_ca":false},{"name":"Yutong Wu","is_ca":false},{"name":"Yangyang Guo","is_ca":false},{"name":"Ran Yan","is_ca":false},{"name":"Ponnuthurai Nagaratnam Suganthan","is_ca":false},{"name":"Yue Zhang","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Swagatam Das","is_ca":false},{"name":"Rammohan Mallipeddi","is_ca":false},{"name":"Oladayo S. Ajani","is_ca":false},{"name":"Qiang Feng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1356344590218789,"gpt":0.362117218612814,"spread":0.2264827595909351,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005721,0.0006535374,0.001521628,0.0007963516,0.0002431158,0.001389954,0.001134877,0.0009905716,0.001397765],"category_scores_gemma":[0.002114051,0.0002633164,0.0005556229,0.002029638,0.0004283248,0.001219518,0.0006187322,0.0009217861,0.0005713938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003582337,"about_ca_system_score_gemma":0.0008195802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391615,"about_ca_topic_score_gemma":0.001145683,"domain_scores_codex":[0.9995829,0.0001331926,0.00003413331,0.00007618086,0.0001481051,0.00002549725],"domain_scores_gemma":[0.9991401,0.0005449906,0.00004958815,0.00005211783,0.0001794618,0.00003368165],"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":[0.00007850229,0.0002354547,0.001590404,0.001336301,0.0001259888,0.00005787011,0.0000655346,0.1025116,0.001410637,0.02906066,0.004532165,0.8589948],"study_design_scores_gemma":[0.00005353636,0.0004379567,0.001582066,0.0005891379,0.0001503611,0.0003600246,0.0001383681,0.8618307,0.002053577,0.04114188,0.09160891,0.00005349798],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01277703,0.3465473,0.6150962,0.00157639,0.000534381,0.00009967555,0.00007041849,0.0003577766,0.02294083],"genre_scores_gemma":[0.339674,0.3742428,0.273003,0.0007914521,0.001565719,0.000196226,0.0002867676,0.0001534649,0.01008658],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.001521628,"threshold_uncertainty_score":0.00531882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387873795","doi":"10.3390/biomimetics8060507","title":"Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Metaheuristic; Algorithm; Benchmark (surveying); Computer science; Mathematical optimization; Test suite; Optimization problem; Suite; Mathematics; Test case; Machine learning","authors":[{"name":"Mohammad Dehghani","is_ca":false},{"name":"Gulnara Bektemyssova","is_ca":false},{"name":"Zeinab Montazeri","is_ca":false},{"name":"Galymzhan Shaikemelev","is_ca":false},{"name":"O.P. Malik","is_ca":true},{"name":"Gaurav Dhiman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04427160285548675,"gpt":0.2948410349910398,"spread":0.250569432135553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009174922,0.001250822,0.001148285,0.001039092,0.0004519038,0.0009768547,0.001583963,0.001467899,0.00201017],"category_scores_gemma":[0.001386586,0.0003937221,0.001173536,0.001035819,0.0005271671,0.0008901648,0.0008741574,0.00144607,0.0007372796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006671523,"about_ca_system_score_gemma":0.001445358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003105245,"about_ca_topic_score_gemma":0.003251774,"domain_scores_codex":[0.9995757,0.0001534675,0.00003254711,0.0000694192,0.0001346699,0.00003425982],"domain_scores_gemma":[0.9996775,0.0001621175,0.00005254187,0.00002832246,0.00006292487,0.00001653197],"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":[0.00009367017,0.0001191176,0.0009421664,0.0002983354,0.0001887154,0.00009594227,0.00006522279,0.8227984,0.005340057,0.01546201,0.004552731,0.1500436],"study_design_scores_gemma":[0.00003749664,0.00005889696,0.0001336294,0.00002554724,0.00002181489,0.00004155593,0.00001114534,0.989208,0.0009631726,0.003224591,0.006262999,0.00001096342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006376265,0.001223472,0.9867963,0.0002275525,0.00008928403,0.0001065751,0.0001009654,0.0006446253,0.004434983],"genre_scores_gemma":[0.1042161,0.001184164,0.8876662,0.0004735503,0.00009345912,0.0008191972,0.0005149452,0.0002341418,0.004798288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003105245,"threshold_uncertainty_score":0.006724656,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2802761309","doi":"10.1016/j.aci.2018.04.001","title":"Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems","year":2018,"lang":"en","type":"article","venue":"Applied Computing and Informatics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Particle swarm optimization; Computer science; Multi-swarm optimization; Feature (linguistics); Algorithm; Feature selection; Binary number; Set (abstract data type); Binary search algorithm; Metaheuristic; Swarm behaviour; Hybrid algorithm (constraint satisfaction); Bat algorithm; Mathematical optimization; Meta-optimization; Selection (genetic algorithm); Feature vector; Search algorithm; Artificial intelligence; Mathematics; Constraint satisfaction","authors":[{"name":"Mohamed A. Tawhid","is_ca":true},{"name":"Kevin Bradley Dsouza","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01267380492240101,"gpt":0.2571261389554496,"spread":0.2444523340330486,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009006101,0.0007925085,0.001156642,0.0007429215,0.00043698,0.000822297,0.001112346,0.001054845,0.00155926],"category_scores_gemma":[0.001785839,0.0003869119,0.0006294061,0.0009797669,0.0004238409,0.0007949008,0.0007142792,0.0009762564,0.0005438218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003280337,"about_ca_system_score_gemma":0.0007532724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003538234,"about_ca_topic_score_gemma":0.002388276,"domain_scores_codex":[0.9993683,0.0001912541,0.00004191275,0.00008693962,0.0002610641,0.00005064517],"domain_scores_gemma":[0.9993904,0.0002862177,0.00005619061,0.00004711075,0.0001933572,0.00002670616],"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":[0.0001714594,0.0001309578,0.001787377,0.0002063017,0.0001568138,0.0001099271,0.0001012538,0.7651731,0.008025309,0.009385897,0.003231858,0.2115198],"study_design_scores_gemma":[0.00002166161,0.00003207285,0.000216778,0.000005454077,0.000008692415,0.00002821579,0.000006920654,0.9968705,0.0006514309,0.0011892,0.0009637468,0.0000054272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01066385,0.0004284235,0.986226,0.0001362445,0.00007362621,0.00005346229,0.00003234344,0.0002612731,0.002124771],"genre_scores_gemma":[0.3399542,0.0005218489,0.653441,0.0002850641,0.000107996,0.0004239919,0.0002854631,0.0001087289,0.004871733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003538234,"threshold_uncertainty_score":0.007035255,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2474290527","doi":"10.1007/s12351-016-0251-z","title":"A new monarch butterfly optimization with an improved crossover operator","year":2016,"lang":"en","type":"article","venue":"Operational Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"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":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Crossover; Operator (biology); Benchmark (surveying); Butterfly; Computer science; Swarm intelligence; Mathematical optimization; Metaheuristic; Artificial intelligence; Particle swarm optimization; Mathematics; Algorithm; Ecology","authors":[{"name":"Gai‐Ge Wang","is_ca":true},{"name":"Suash Deb","is_ca":false},{"name":"Xinchao Zhao","is_ca":false},{"name":"Zhihua Cui","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05427543962770417,"gpt":0.3647605437736715,"spread":0.3104851041459673,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003175778,0.0005751158,0.0008603788,0.0007534071,0.0004710794,0.0006747976,0.00134672,0.001118801,0.004447746],"category_scores_gemma":[0.0004511955,0.0002989541,0.0007328211,0.0009908363,0.0003732117,0.0006740633,0.000779914,0.0007023361,0.0004847349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006042303,"about_ca_system_score_gemma":0.0006725387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002530701,"about_ca_topic_score_gemma":0.004039065,"domain_scores_codex":[0.999814,0.00003675311,0.000008437775,0.00003383512,0.00007674147,0.0000302762],"domain_scores_gemma":[0.999881,0.00003337377,0.00001330856,0.00002332773,0.00003340603,0.00001550643],"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":[0.0003805264,0.0002412378,0.000800691,0.0002425411,0.0001968994,0.0002291814,0.0001024812,0.5439628,0.04211713,0.05924146,0.009053798,0.3434312],"study_design_scores_gemma":[0.00006335675,0.0001227608,0.0003612725,0.00001273533,0.00003933704,0.00008920014,0.00001218839,0.9874725,0.003005672,0.003483633,0.005316576,0.00002064709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06902066,0.001252785,0.9048914,0.0004878411,0.0006356025,0.0001591536,0.0002057627,0.0009178977,0.02242892],"genre_scores_gemma":[0.4164694,0.0006284144,0.5482933,0.0003883954,0.0002427664,0.000309626,0.0003056994,0.0002575457,0.03310487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004447746,"threshold_uncertainty_score":0.01487923,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2969862035","doi":"10.1007/s13042-019-00996-5","title":"Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm","year":2019,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Computer science; Feature (linguistics); Heuristic; Algorithm; Artificial intelligence; Computational intelligence; Selection (genetic algorithm); Rough set; Optimization problem; Set (abstract data type); Wilcoxon signed-rank test; Pattern recognition (psychology); Machine learning; Mathematics","authors":[{"name":"Mohamed A. Tawhid","is_ca":true},{"name":"Abdelmonem M. Ibrahim","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009192440010017096,"gpt":0.2599653760498017,"spread":0.2507729360397846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002030239,0.0009272498,0.002875395,0.002356986,0.0007037508,0.001473424,0.001215089,0.00096925,0.001049205],"category_scores_gemma":[0.003706808,0.0004182937,0.001845524,0.002007637,0.0004994633,0.001550845,0.0007404904,0.0006157397,0.0002301657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005411911,"about_ca_system_score_gemma":0.0007818561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00199391,"about_ca_topic_score_gemma":0.001143084,"domain_scores_codex":[0.998394,0.0004528495,0.0001608029,0.0002461938,0.0006312074,0.000115046],"domain_scores_gemma":[0.9989923,0.0005032588,0.00009033416,0.00009834703,0.0002879041,0.00002779095],"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":[0.0003967458,0.0002521965,0.002586162,0.0004036691,0.000712074,0.0002653447,0.0001556334,0.5728887,0.007127888,0.02162505,0.003913928,0.3896725],"study_design_scores_gemma":[0.0000291716,0.0001130277,0.0007101404,0.00001601835,0.0001149649,0.00007826847,0.00002142902,0.9907206,0.001786905,0.005809966,0.000573668,0.00002584357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02635641,0.000499083,0.9715499,0.0001368254,0.00007892244,0.00007804552,0.00004743579,0.0002408441,0.001012494],"genre_scores_gemma":[0.6455013,0.0005014965,0.3509945,0.0001195711,0.0001319372,0.0003091675,0.0002347774,0.0000662965,0.002140858],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002875395,"threshold_uncertainty_score":0.01073706,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2548265241","doi":"10.1007/s11721-016-0128-z","title":"Inertia weight control strategies for particle swarm optimization","year":2016,"lang":"en","type":"article","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertia; Benchmark (surveying); Particle swarm optimization; Computer science; Control (management); Mathematical optimization; Selection (genetic algorithm); Convergence (economics); Population; Control theory (sociology); Mathematics; Artificial intelligence; Economics","authors":[{"name":"Kyle Robert Harrison","is_ca":false},{"name":"Andries P. Engelbrecht","is_ca":false},{"name":"Beatrice Ombuki-Berman","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02980599871837959,"gpt":0.2991908573740253,"spread":0.2693848586556458,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005557633,0.000888413,0.000583626,0.0006794948,0.0004164301,0.0009745818,0.0009978276,0.0009098042,0.002033972],"category_scores_gemma":[0.002009542,0.0003146821,0.0003336275,0.0007113215,0.0005717474,0.0009407623,0.0007296195,0.0008656163,0.0004488809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003609911,"about_ca_system_score_gemma":0.000442005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003388107,"about_ca_topic_score_gemma":0.002415521,"domain_scores_codex":[0.9998304,0.00004505238,0.00001125748,0.0000151429,0.00008206468,0.00001614],"domain_scores_gemma":[0.9997359,0.00009638416,0.00003589413,0.0000235738,0.00009353918,0.0000146654],"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":[0.00009177937,0.0001131481,0.0003402437,0.0001643653,0.00006250702,0.00008182457,0.0001203518,0.7728268,0.004630705,0.05550225,0.003452151,0.1626139],"study_design_scores_gemma":[0.00001668522,0.00003555603,0.00007182405,0.00001055357,0.000009218008,0.000009275645,0.000008289789,0.9931525,0.0003969869,0.005084356,0.001199805,0.000004902594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01374502,0.001414104,0.9725405,0.0002493576,0.0002987066,0.00007116289,0.00001629858,0.0001436099,0.01152118],"genre_scores_gemma":[0.7553723,0.001878168,0.2253932,0.0002099857,0.0003102885,0.0003807478,0.00008953259,0.0001685496,0.01619718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003388107,"threshold_uncertainty_score":0.006804347,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318323524","doi":"10.1016/j.ins.2023.01.103","title":"PSO-ELPM: PSO with elite learning, enhanced parameter updating, and exponential mutation operator","year":2023,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Benchmark (surveying); Operator (biology); Exponential function; Mutation; Population; Mathematics; Wilcoxon signed-rank test; Computer science; Statistics","authors":[{"name":"Hadi Moazen","is_ca":true},{"name":"Sajjad Molaei","is_ca":false},{"name":"Leili Farzinvash","is_ca":false},{"name":"Masoud Sabaei","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02021766652312251,"gpt":0.2964951871606678,"spread":0.2762775206375453,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007860842,0.0006691502,0.001036868,0.0006847875,0.0003553104,0.0008369105,0.001420948,0.001262634,0.002502235],"category_scores_gemma":[0.002969985,0.0002305687,0.0005882459,0.001193966,0.0004540256,0.001297703,0.0009977539,0.001449743,0.001008426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004048032,"about_ca_system_score_gemma":0.0006631956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001950725,"about_ca_topic_score_gemma":0.001801398,"domain_scores_codex":[0.9995376,0.000116567,0.00003499876,0.00008950025,0.0001846031,0.00003672341],"domain_scores_gemma":[0.9995807,0.000114006,0.0000439248,0.00008952861,0.000136741,0.00003505085],"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":[0.0004470936,0.0003878868,0.001627064,0.0004491492,0.0002453293,0.0002491141,0.0001049027,0.3856869,0.01445504,0.02775735,0.01983293,0.5487572],"study_design_scores_gemma":[0.00009206402,0.00008444314,0.0003290676,0.00001540119,0.00002734668,0.0001326201,0.000006069738,0.9844632,0.005562665,0.003021856,0.006246323,0.00001889639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01270681,0.0007785211,0.9771006,0.0002843331,0.0003638828,0.000110403,0.0001509435,0.002799315,0.005705256],"genre_scores_gemma":[0.3041593,0.0007468928,0.6805368,0.0003564934,0.0002194209,0.0002920927,0.0006628333,0.0006507104,0.01237548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002502235,"threshold_uncertainty_score":0.008370817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1835894572","doi":"10.1109/ccece.2002.1013043","title":"Enhancing the particle swarm optimizer via proper parameters selection","year":2003,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Range (aeronautics); Mathematical optimization; Selection (genetic algorithm); Swarm intelligence; Computer science; Swarm behaviour; Multi-swarm optimization; Process (computing); Metaheuristic; Algorithm; Mathematics; Artificial intelligence; Engineering","authors":[{"name":"Ahmed I. EL-Gallad","is_ca":true},{"name":"M.E. El-Hawary","is_ca":true},{"name":"Abdelhay A. Sallam","is_ca":false},{"name":"Ahmed Kalas","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02582213623270858,"gpt":0.2681246103243953,"spread":0.2423024740916868,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00203624,0.001330997,0.001025585,0.0007927,0.0003595273,0.001042688,0.0008064658,0.001486133,0.0009341025],"category_scores_gemma":[0.009598386,0.000526601,0.0004638988,0.000655329,0.0005257186,0.001364494,0.0007058441,0.001115239,0.0007906337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002332301,"about_ca_system_score_gemma":0.0004892264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008903701,"about_ca_topic_score_gemma":0.0006875162,"domain_scores_codex":[0.9991296,0.0003612776,0.00007230043,0.00009824825,0.0002938086,0.00004472561],"domain_scores_gemma":[0.9980691,0.001105829,0.0001945223,0.0002317762,0.0003656576,0.00003317134],"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":[0.0002605533,0.0002214744,0.002222889,0.0003680548,0.0001080472,0.0003550206,0.0003309482,0.5793641,0.04216274,0.009380511,0.002429735,0.362796],"study_design_scores_gemma":[0.00006403478,0.0001637887,0.0007345913,0.00003598372,0.00005954569,0.0002405631,0.00003640615,0.9731909,0.0172726,0.002711571,0.005450704,0.00003927886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01831596,0.0004286807,0.9771627,0.0001622712,0.00007141851,0.0001429794,0.00001828597,0.0007219172,0.002975768],"genre_scores_gemma":[0.343202,0.0008171573,0.6533318,0.000149436,0.00008783191,0.0003578803,0.00007917551,0.0003242538,0.0016505],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00203624,"threshold_uncertainty_score":0.01076883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2304131403","doi":"10.1007/s00500-016-2116-z","title":"Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism","year":2016,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"Science Foundation of Ministry of Education of China; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Firefly algorithm; Benchmark (surveying); Swarm intelligence; Convergence (economics); Premature convergence; Computer science; Mathematical optimization; Set (abstract data type); Mechanism (biology); Optimization problem; Algorithm; Particle swarm optimization; Mathematics","authors":[{"name":"Hui Wang","is_ca":false},{"name":"Zhihua Cui","is_ca":false},{"name":"Hui Sun","is_ca":false},{"name":"Shahryar Rahnamayan","is_ca":true},{"name":"Xin‐She Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01366166025855101,"gpt":0.261132166591145,"spread":0.247470506332594,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001034909,0.0007182135,0.001320205,0.001011897,0.0008431576,0.0008928598,0.002514694,0.001536279,0.001960914],"category_scores_gemma":[0.001933182,0.0004149482,0.0007641901,0.001122921,0.0005550848,0.001230006,0.001019372,0.0007474408,0.0004543073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000638323,"about_ca_system_score_gemma":0.0007868242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001798297,"about_ca_topic_score_gemma":0.001420231,"domain_scores_codex":[0.9993882,0.0001799988,0.00002836648,0.00009750258,0.0002536106,0.00005228961],"domain_scores_gemma":[0.9996145,0.0001182169,0.00005215013,0.0000605847,0.0001276759,0.00002689179],"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":[0.0004018492,0.0003284678,0.001231896,0.00022732,0.0002593387,0.0002169436,0.0002397854,0.6648703,0.01646488,0.05482553,0.005431823,0.2555019],"study_design_scores_gemma":[0.00007763569,0.00008733286,0.0001705762,0.000007112585,0.00003824948,0.00008890693,0.00001089765,0.9927731,0.001815211,0.002904774,0.002006512,0.00001961298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03016598,0.000640736,0.9590775,0.000195953,0.0001740153,0.0001150692,0.00002816933,0.0005543249,0.009048309],"genre_scores_gemma":[0.5292224,0.0004544463,0.4574807,0.0002016718,0.0001253457,0.000461861,0.0001245332,0.0001345357,0.01179442],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002514694,"threshold_uncertainty_score":0.006559849,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134788587","doi":"10.1109/sis.2007.368044","title":"Applying Opposition-Based Ideas to the Ant Colony System","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":96,"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":"Travelling salesman problem; Ant colony optimization algorithms; Synchronizing; Opposition (politics); Computer science; ANT; Ant colony; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics; Computer network","authors":[{"name":"Alice R. Malisia","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02070064834814609,"gpt":0.293804749325385,"spread":0.2731041009772389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073246,0.0004430345,0.0006842737,0.0006931694,0.0004893834,0.001470977,0.001017854,0.0007990211,0.001570853],"category_scores_gemma":[0.00254864,0.0002653661,0.0006737421,0.0005941523,0.001486898,0.001348506,0.001506116,0.001326668,0.0004362566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004907582,"about_ca_system_score_gemma":0.0004824538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005707981,"about_ca_topic_score_gemma":0.0004864378,"domain_scores_codex":[0.999109,0.000388889,0.00003982447,0.00006771251,0.0003503199,0.00004429241],"domain_scores_gemma":[0.9992464,0.0004267013,0.00006465736,0.00008520238,0.0001429211,0.00003415734],"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":[0.0001145531,0.00006529785,0.0005851975,0.0003229002,0.0001155243,0.0003550659,0.0005384251,0.3149227,0.009574953,0.5205598,0.001813812,0.1510317],"study_design_scores_gemma":[0.00005941869,0.0001583205,0.0002166627,0.00006923032,0.00004604132,0.0003994758,0.0000888896,0.7319046,0.003554787,0.2162307,0.04722418,0.00004757164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008400971,0.0007362071,0.9711547,0.0003610906,0.0001684675,0.00006055206,0.00001134024,0.0001133179,0.01899334],"genre_scores_gemma":[0.3960944,0.001982113,0.5912178,0.0003446956,0.0002416011,0.000305892,0.00005323443,0.0001116037,0.009648737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001570853,"threshold_uncertainty_score":0.005675912,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1551741615","doi":"10.1007/3-540-45724-0_28","title":"Using Genetic Algorithms to Optimize ACS-TSP","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Genetic algorithm; Ant colony optimization algorithms; Algorithm; Variable (mathematics); Ant colony; Travelling salesman problem; Population-based incremental learning; Mathematical optimization; Machine learning; Mathematics","authors":[{"name":"Marcin L. Pilat","is_ca":true},{"name":"Tony White","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05726925925110299,"gpt":0.3053369914652968,"spread":0.2480677322141938,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005188548,0.0006442076,0.0006188561,0.0007765912,0.0003938543,0.0008392595,0.000884917,0.001082842,0.002403787],"category_scores_gemma":[0.001756773,0.0003667324,0.00054758,0.001528746,0.0004655886,0.0007362191,0.0004656665,0.0008235878,0.0004582799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006399524,"about_ca_system_score_gemma":0.0007828138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003929835,"about_ca_topic_score_gemma":0.005110591,"domain_scores_codex":[0.9997707,0.00006602291,0.000008145905,0.00002436923,0.0001024274,0.00002838887],"domain_scores_gemma":[0.999734,0.000133684,0.0000261873,0.00003031631,0.00006521263,0.00001061194],"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":[0.00004641194,0.00005322165,0.0002506944,0.00006064028,0.00003461944,0.0000466115,0.00003179468,0.9047716,0.003004581,0.01344489,0.001582796,0.0766722],"study_design_scores_gemma":[0.00001533384,0.00003273488,0.00007870391,0.000006482697,0.00001649444,0.00001758009,0.000008889776,0.991693,0.0009714625,0.005367676,0.001788009,0.000003626047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09336653,0.001474715,0.8525985,0.0004114477,0.0003651386,0.0001453072,0.00009526192,0.001363578,0.05017962],"genre_scores_gemma":[0.4677069,0.0007009792,0.5212255,0.000171943,0.0001024291,0.0001646642,0.0001606707,0.0003575613,0.009409347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003929835,"threshold_uncertainty_score":0.008041501,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}