{"meta":{"query_hash":"1bfadf58f4d0","filters":{"venue":"Swarm and Evolutionary Computation"},"cohort_total":29,"direct_labels_cover":0,"predictions_cover":29,"exported":29,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/1bfadf58f4d0","api":"https://metacan.xera.ac/api/v1/cohort?venue=Swarm+and+Evolutionary+Computation"},"results":[{"id":"W1997912974","doi":"10.1016/j.swevo.2012.02.001","title":"A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection","year":2012,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Statistics; Scan statistic; Outbreak; Pattern recognition (psychology); Artificial intelligence; Mathematics; Medicine; Virology","score_opus":0.012641377421825062,"score_gpt":0.267918497261658,"score_spread":0.2552771198398329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997912974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010156195,0.00011529782,0.9879551,0.00014044027,0.00006282112,0.00004062768,0.000118046104,0.00035608513,0.0010554463],"genre_scores_gemma":[0.31064472,0.00017516573,0.6862469,0.00016493129,0.00010792573,0.00021836262,0.0005814034,0.00027768832,0.0015829637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948907,0.00013687162,0.000038291182,0.00012304714,0.00016747645,0.000045161647],"domain_scores_gemma":[0.99850047,0.0007135936,0.00012921616,0.00013290775,0.0004241698,0.000099556404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082275865,0.0008256127,0.0017006245,0.0013071414,0.00041364663,0.0010969255,0.0018659934,0.0013645256,0.0017682676],"category_scores_gemma":[0.005371348,0.0008051919,0.0011911104,0.0012197661,0.00067457417,0.0012186203,0.0012146368,0.00096379983,0.0003403547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006158924,0.000042856176,0.0009544571,0.00004175803,0.000047233872,0.000048137554,0.000033418433,0.95103276,0.0023385717,0.0068223947,0.0013173223,0.037259493],"study_design_scores_gemma":[0.0000035723724,0.000006105262,0.00006111936,0.000001253796,0.0000018054764,0.0000068242603,0.0000015118416,0.99911505,0.0001059902,0.00058108533,0.00011346206,0.0000022236964],"about_ca_topic_score_codex":0.010630602,"about_ca_topic_score_gemma":0.007104618,"teacher_disagreement_score":0.010630602,"about_ca_system_score_codex":0.0010950404,"about_ca_system_score_gemma":0.0018065433,"threshold_uncertainty_score":0.021137476},"labels":[],"label_agreement":null},{"id":"W2605533099","doi":"10.1016/j.swevo.2017.04.005","title":"Micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) of a solar thermal combisystem","year":2017,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Economic Affairs; Concordia University","keywords":"Benchmark (surveying); Computer science; Multi-objective optimization; Mathematical optimization; Pareto principle; Particle swarm optimization; Evolutionary algorithm; Multi-swarm optimization; Metaheuristic; Optimization problem; Population; Engineering optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.01362871856067218,"score_gpt":0.2526617303293274,"score_spread":0.23903301176865524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605533099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10784395,0.00047192845,0.8849508,0.00015285386,0.00009886432,0.00010114148,0.000059005542,0.00023279017,0.006088651],"genre_scores_gemma":[0.72425556,0.00035926647,0.27106366,0.00008665111,0.000033549823,0.00023755983,0.00012243095,0.000043569835,0.0037977307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998679,0.000037421698,0.000008118218,0.000028042983,0.00004577038,0.000012739903],"domain_scores_gemma":[0.99977726,0.00011493402,0.000039096387,0.000021176696,0.000036363104,0.0000111616555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039083892,0.00056907686,0.00046605305,0.00027911007,0.0002614751,0.0004864944,0.00042604964,0.00053115946,0.000710122],"category_scores_gemma":[0.00071384944,0.00024215225,0.0005433847,0.00030682376,0.00028348117,0.00040740357,0.00033901373,0.00046591114,0.00011755108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052624382,0.000042578373,0.0012255225,0.00008747321,0.000048131486,0.00007089257,0.00003541707,0.93014115,0.0078107943,0.0030893884,0.00051537115,0.05688079],"study_design_scores_gemma":[0.000007804948,0.00006350496,0.0004090165,0.0000046387836,0.000008255665,0.000019204646,0.000010348863,0.996185,0.001684118,0.0007059135,0.0008979102,0.0000042151337],"about_ca_topic_score_codex":0.0017664133,"about_ca_topic_score_gemma":0.002595744,"teacher_disagreement_score":0.0017664133,"about_ca_system_score_codex":0.0002967833,"about_ca_system_score_gemma":0.0005542878,"threshold_uncertainty_score":0.0035122037},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.0812765835477228,"score_gpt":0.39083411779178967,"score_spread":0.30955753424406685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760225670","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021187482,0.9978192,0.0005152286,0.00029767107,0.00015668357,0.000010232389,0.000032766606,0.0000067823084,0.0009495715],"genre_scores_gemma":[0.0015847957,0.9967925,0.00089026114,0.00018522247,0.00020192737,0.000012645628,0.00004114722,0.0000028075397,0.00028883576],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960643,0.0000674464,0.00008836911,0.00007932106,0.00013591403,0.000022465061],"domain_scores_gemma":[0.99821496,0.0012359819,0.00016342697,0.000028394094,0.00030511047,0.000052211388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009942453,0.00088803,0.0016372256,0.0041364306,0.00031979362,0.0021026065,0.0010417114,0.0013111127,0.0043315226],"category_scores_gemma":[0.0032308486,0.00036903968,0.00074533647,0.0068873125,0.00057267153,0.0021710063,0.0008037086,0.0010734214,0.001105553],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085208034,0.000116751144,0.00046377216,0.054617666,0.00015752441,0.00016177756,0.00008275391,0.0009294026,0.00061510655,0.0044981944,0.017503064,0.9207688],"study_design_scores_gemma":[0.000068082176,0.00025612232,0.0029595415,0.049514737,0.0013913951,0.0022991009,0.00042962196,0.0016100602,0.0009459555,0.009816876,0.930598,0.00011038862],"about_ca_topic_score_codex":0.0018009406,"about_ca_topic_score_gemma":0.0030303118,"teacher_disagreement_score":0.0043315226,"about_ca_system_score_codex":0.0006547559,"about_ca_system_score_gemma":0.0025139626,"threshold_uncertainty_score":0.014490426},"labels":[],"label_agreement":null},{"id":"W2782645119","doi":"10.1016/j.swevo.2018.01.006","title":"Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm","year":2018,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation, United Arab Emirates; National Research Foundation","keywords":"Benchmark (surveying); Particle swarm optimization; Acceleration; Computer science; Parameter space; Mathematical optimization; Set (abstract data type); Swarm behaviour; Algorithm; Mathematics; Statistics","score_opus":0.01850253828105613,"score_gpt":0.2757660492307485,"score_spread":0.25726351094969235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782645119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16838256,0.0015875216,0.81270266,0.0010050329,0.000069993956,0.000076101074,0.00008215737,0.000178497,0.01591542],"genre_scores_gemma":[0.9489261,0.00055546465,0.048299227,0.00008371255,0.000035452445,0.00009401586,0.00006638351,0.00011841805,0.0018213165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996635,0.00017274873,0.000013364596,0.000037294387,0.00007888463,0.00003427996],"domain_scores_gemma":[0.9952043,0.0039447066,0.00026133982,0.0001469818,0.00037443184,0.000068225105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002879776,0.00060505094,0.00053305656,0.0008710424,0.0005228118,0.0013043123,0.0006146453,0.0010614344,0.001208936],"category_scores_gemma":[0.017997684,0.00065886194,0.00047894192,0.0004506821,0.0014810644,0.0015740172,0.00082704495,0.0010109397,0.00017480482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001212608,0.000020942485,0.00039626972,0.000048171274,0.000015605194,0.00004294034,0.00011570797,0.9673058,0.0018426621,0.022911685,0.000290377,0.006888698],"study_design_scores_gemma":[0.000009004475,0.000017503942,0.00020938039,0.000011937686,0.0000067760816,0.000008211728,0.000011150932,0.9928148,0.0005581382,0.0061760745,0.0001697912,0.0000072661464],"about_ca_topic_score_codex":0.003873004,"about_ca_topic_score_gemma":0.0016485183,"teacher_disagreement_score":0.003873004,"about_ca_system_score_codex":0.00071337406,"about_ca_system_score_gemma":0.00081830897,"threshold_uncertainty_score":0.01522994},"labels":[],"label_agreement":null},{"id":"W2949820503","doi":"10.1016/j.swevo.2021.100893","title":"Techniques for inferring context-free Lindenmayer systems with genetic algorithm","year":2021,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Sequence (biology); Rewriting; Inference; Set (abstract data type); String (physics); Grammar induction; Formal grammar; Symbol (formal)","score_opus":0.010981675878658538,"score_gpt":0.22630345791797501,"score_spread":0.21532178203931648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949820503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012132847,0.00010211007,0.9861688,0.0000640705,0.000012627171,0.00003145756,0.000051495546,0.00069857977,0.00073806895],"genre_scores_gemma":[0.3142688,0.00016638552,0.6837344,0.00007146338,0.000024296203,0.00012163915,0.00025261595,0.00023778116,0.001122622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998955,0.00028986018,0.00007951946,0.00029290156,0.00030243135,0.000080164544],"domain_scores_gemma":[0.99708456,0.002080844,0.00022969546,0.0003347255,0.00022253401,0.000047758393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014861892,0.0011831316,0.0012409195,0.002796502,0.0014622214,0.0019421328,0.002409871,0.002120421,0.0027882871],"category_scores_gemma":[0.012361556,0.0011253313,0.0017750654,0.0016149875,0.0016450877,0.0034333186,0.0020154112,0.0021815163,0.0006495991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011765937,0.00012057428,0.0029258842,0.00016570668,0.00015136793,0.0003049529,0.00044021962,0.74655396,0.0068025445,0.117029816,0.00092143053,0.124465875],"study_design_scores_gemma":[0.000008188208,0.0000055144833,0.00009571602,0.000011961018,0.000013047038,0.000020237347,0.000021146878,0.94184846,0.001195835,0.056361087,0.00040741215,0.00001146156],"about_ca_topic_score_codex":0.01019243,"about_ca_topic_score_gemma":0.017325707,"teacher_disagreement_score":0.01019243,"about_ca_system_score_codex":0.0013142789,"about_ca_system_score_gemma":0.0013777929,"threshold_uncertainty_score":0.020266175},"labels":[],"label_agreement":null},{"id":"W2978868208","doi":"10.1016/j.swevo.2019.100591","title":"Multilevel thresholding by fuzzy type II sets using evolutionary algorithms","year":2019,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Histogram; Thresholding; Evolutionary algorithm; Algorithm; Image segmentation; Fitness function; Fuzzy logic; Particle swarm optimization; Benchmark (surveying); Entropy (arrow of time); Artificial intelligence; Segmentation; Pattern recognition (psychology); Mathematical optimization; Image (mathematics); Mathematics; Machine learning; Genetic algorithm","score_opus":0.024728815565025476,"score_gpt":0.29719712747352517,"score_spread":0.2724683119084997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978868208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0144994315,0.00015132483,0.983433,0.000054657925,0.000035226796,0.000025211233,0.000009473716,0.00008089984,0.0017106932],"genre_scores_gemma":[0.36389112,0.00021189185,0.6325045,0.000068739464,0.00004032273,0.00015195204,0.00004149497,0.000080827514,0.0030091272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996718,0.00008695426,0.000021430846,0.000063739884,0.00012545928,0.000030604522],"domain_scores_gemma":[0.99915063,0.0005407175,0.000068802954,0.0000626672,0.00014872199,0.00002832792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008982765,0.00044350143,0.0010252886,0.00086617336,0.00056878594,0.001277433,0.0009880696,0.0010828334,0.0018210531],"category_scores_gemma":[0.002424815,0.0004776232,0.0009584069,0.0007763229,0.0007210775,0.00083186344,0.00089462847,0.0008460657,0.0002872019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018865161,0.000097407814,0.0010856336,0.00022252984,0.00013603597,0.00012589553,0.00031854215,0.6887779,0.033688866,0.05523136,0.0012404151,0.21888667],"study_design_scores_gemma":[0.0000058706746,0.000019353498,0.00010853715,0.000009988908,0.000010611104,0.000015930322,0.000008454772,0.9943388,0.001456637,0.003703883,0.000315633,0.0000062831305],"about_ca_topic_score_codex":0.001950176,"about_ca_topic_score_gemma":0.0018000783,"teacher_disagreement_score":0.001950176,"about_ca_system_score_codex":0.00079275056,"about_ca_system_score_gemma":0.00048490916,"threshold_uncertainty_score":0.006092012},"labels":[],"label_agreement":null},{"id":"W3028365645","doi":"10.1016/j.swevo.2020.100713","title":"Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems","year":2020,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Surrogate model; Robustness (evolution); Global optimization; Computation; Optimization problem; Local search (optimization); Mathematical optimization; Artificial intelligence; Radial basis function; Algorithm; Data mining; Machine learning; Mathematics; Artificial neural network","score_opus":0.023331193630724365,"score_gpt":0.251606041909398,"score_spread":0.22827484827867364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028365645","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015359717,0.0004197533,0.9792063,0.0002326738,0.00006109463,0.00003192756,0.00003368115,0.0001595054,0.0044952207],"genre_scores_gemma":[0.62697214,0.0006035681,0.3641323,0.00020696006,0.00007231691,0.0002860443,0.00019235634,0.00021369083,0.0073205866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937576,0.00033286668,0.000020763475,0.000040420495,0.00018137888,0.000048922793],"domain_scores_gemma":[0.9985013,0.0010841523,0.00009079655,0.00008491562,0.00017196225,0.000066879606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002177823,0.00062642276,0.0016925709,0.0005195975,0.00041605384,0.0010859024,0.0009508899,0.0019028164,0.002729967],"category_scores_gemma":[0.0043971995,0.000548479,0.0006363164,0.0008050063,0.0009793269,0.00094719435,0.0012828992,0.0014597026,0.00041977936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054510227,0.000027945798,0.00013477496,0.0000676976,0.000021296997,0.000046538706,0.000021918788,0.9776405,0.0009078897,0.009472525,0.00057829387,0.011026159],"study_design_scores_gemma":[0.00000392,0.00000849278,0.000017752982,0.000003707504,0.000001449667,0.0000047244785,0.0000015745551,0.9977549,0.00013178578,0.0019053235,0.00016516814,0.0000012669498],"about_ca_topic_score_codex":0.0013093532,"about_ca_topic_score_gemma":0.0011058842,"teacher_disagreement_score":0.002729967,"about_ca_system_score_codex":0.0005006488,"about_ca_system_score_gemma":0.0009957146,"threshold_uncertainty_score":0.011517525},"labels":[],"label_agreement":null},{"id":"W3103264709","doi":"10.1016/j.swevo.2020.100801","title":"Global optimization with one-class classification-assisted selection","year":2020,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Selection (genetic algorithm); Fitness proportionate selection; Artificial intelligence; Machine learning; Classifier (UML); Class (philosophy); Evolutionary algorithm; Mathematical optimization; Population; Set (abstract data type); Genetic algorithm; Fitness function; Mathematics","score_opus":0.024264736390021194,"score_gpt":0.24265682985947187,"score_spread":0.21839209346945068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103264709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020729853,0.00034631026,0.97249055,0.0002850297,0.00016394311,0.00008572774,0.00004230867,0.00046302562,0.00539336],"genre_scores_gemma":[0.6320423,0.00021606515,0.35627252,0.00036753708,0.00020211619,0.000465572,0.00027020715,0.00029936314,0.009864299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990181,0.00037688916,0.000046461657,0.00020003073,0.000244767,0.000113817885],"domain_scores_gemma":[0.9981589,0.0010084595,0.00009914645,0.00020668899,0.00045040957,0.00007653134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023156097,0.0013324958,0.0025785496,0.0011310924,0.00095834385,0.0014219736,0.0025106536,0.0023564894,0.003410148],"category_scores_gemma":[0.004502539,0.0005330307,0.0012331078,0.0012412493,0.0012630888,0.0014022939,0.0015243487,0.0014049795,0.0006041552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024637702,0.00016165384,0.0009472253,0.00012556375,0.00014792176,0.00006957533,0.00010029325,0.779551,0.0019302896,0.022690428,0.00584014,0.18818952],"study_design_scores_gemma":[0.000012816687,0.000020882171,0.0000788897,0.0000032035944,0.000008575823,0.000009623848,0.000003697463,0.9977428,0.00018950138,0.0017249559,0.0002008624,0.000004283548],"about_ca_topic_score_codex":0.0050497414,"about_ca_topic_score_gemma":0.0044988003,"teacher_disagreement_score":0.0050497414,"about_ca_system_score_codex":0.001014697,"about_ca_system_score_gemma":0.0013167703,"threshold_uncertainty_score":0.012246251},"labels":[],"label_agreement":null},{"id":"W4290755172","doi":"10.1016/j.swevo.2022.101145","title":"ACDB-EA: Adaptive convergence-diversity balanced evolutionary algorithm for many-objective optimization","year":2022,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Science and Technology Program of Zhejiang Province; National Natural Science Foundation of China","keywords":"Convergence (economics); Computer science; Evolutionary algorithm; Mathematical optimization; Benchmark (surveying); Similarity (geometry); Pareto principle; Selection (genetic algorithm); Cosine similarity; Population; Evolutionary computation; Algorithm; Mathematics; Artificial intelligence; Cluster analysis","score_opus":0.015022912938113494,"score_gpt":0.24065138581094397,"score_spread":0.22562847287283047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290755172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007406432,0.00042024048,0.98800814,0.000092874965,0.00011789932,0.000060166505,0.000042964588,0.0006954631,0.0031558417],"genre_scores_gemma":[0.24383952,0.00040002173,0.7479777,0.0001985652,0.00009842458,0.00046226612,0.00030485992,0.0003262184,0.006392412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940836,0.00017098388,0.000022722335,0.00007559283,0.00025896385,0.00006324757],"domain_scores_gemma":[0.9996166,0.00014258192,0.000029531813,0.000049137638,0.00013107232,0.000030956515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009856706,0.0009432422,0.0012287229,0.00088600325,0.0005437715,0.0007802028,0.0016245997,0.0014972527,0.0039503495],"category_scores_gemma":[0.0021343527,0.00032868827,0.00063041283,0.0012092545,0.00048161598,0.0007674361,0.0014628718,0.0014441643,0.00091263023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019987099,0.0001548799,0.0006802285,0.00016697792,0.00012823341,0.00008143754,0.00008167741,0.6878572,0.0084296055,0.018925743,0.0056907027,0.27760333],"study_design_scores_gemma":[0.00004124036,0.00004742827,0.0001116713,0.000009631531,0.0000091973,0.000027314421,0.0000045637403,0.9947373,0.0009233624,0.002044527,0.0020373778,0.000006380732],"about_ca_topic_score_codex":0.0021259745,"about_ca_topic_score_gemma":0.0021946712,"teacher_disagreement_score":0.0039503495,"about_ca_system_score_codex":0.00046090063,"about_ca_system_score_gemma":0.0008794725,"threshold_uncertainty_score":0.013215244},"labels":[],"label_agreement":null},{"id":"W4315489741","doi":"10.1016/j.swevo.2023.101246","title":"A GV-drone arc routing approach for urban traffic patrol by coordinating a ground vehicle and multiple drones","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China","keywords":"Drone; Computer science; Simulated annealing; Patrolling; Real-time computing; Algorithm","score_opus":0.008861737378046743,"score_gpt":0.20159632298019328,"score_spread":0.19273458560214654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315489741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038363278,0.00023609235,0.9529921,0.00015722132,0.00009964579,0.00007731206,0.00006581896,0.00045023902,0.0075583137],"genre_scores_gemma":[0.7194363,0.00016824408,0.27267998,0.00009537746,0.000047355963,0.000119848235,0.00015384857,0.00009377383,0.007205251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999856,0.000037038088,0.0000047775525,0.000036486443,0.000039117218,0.000026537844],"domain_scores_gemma":[0.99987614,0.000038846396,0.000014577349,0.0000148931285,0.000033546377,0.000022065555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026726408,0.0006649988,0.0007076454,0.00049020443,0.00046730533,0.00061040843,0.001182691,0.00083063217,0.0029501112],"category_scores_gemma":[0.0005313433,0.00041985934,0.00052654784,0.00053184363,0.00029458306,0.0005136122,0.0008121944,0.00054662704,0.00037362674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039352875,0.000027979342,0.00029516232,0.0000219,0.000030547842,0.000054168137,0.000037602415,0.96523273,0.0012905416,0.0025843638,0.0006981091,0.029687526],"study_design_scores_gemma":[0.0000044944272,0.000014549493,0.000041872558,0.0000013097267,0.000003741465,0.0000074757795,0.000011478038,0.999071,0.00012295041,0.00040014184,0.0003192237,0.0000016313371],"about_ca_topic_score_codex":0.012084651,"about_ca_topic_score_gemma":0.014218729,"teacher_disagreement_score":0.012084651,"about_ca_system_score_codex":0.00047613334,"about_ca_system_score_gemma":0.0008752896,"threshold_uncertainty_score":0.024028659},"labels":[],"label_agreement":null},{"id":"W4321214511","doi":"10.1016/j.swevo.2023.101262","title":"Cooperative coevolutionary multi-guide particle swarm optimization algorithm for large-scale multi-objective optimization problems","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metaheuristic; Multi-swarm optimization; Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Imperialist competitive algorithm; Optimization problem; Metric (unit); Scalability; Heuristic; Parallel metaheuristic; Continuous optimization; Scale (ratio); Algorithm; Artificial intelligence; Mathematics","score_opus":0.02114357018829575,"score_gpt":0.28732133859146136,"score_spread":0.2661777684031656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321214511","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021155007,0.00035966837,0.97472656,0.000114091454,0.00006131268,0.00005320521,0.000011554333,0.00016169371,0.0033568926],"genre_scores_gemma":[0.6017949,0.00036944746,0.3906923,0.00018692375,0.0000633464,0.00040497893,0.00010157229,0.00008462162,0.0063019157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995228,0.00014946143,0.000024185241,0.00007025582,0.00019155443,0.000041689036],"domain_scores_gemma":[0.99925214,0.00034682223,0.00005931584,0.00007880451,0.00021880896,0.000044070755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013958167,0.00079956837,0.0011225155,0.0006411319,0.00069304876,0.000835884,0.0019168714,0.0016255395,0.0012458183],"category_scores_gemma":[0.002524537,0.00048428928,0.00066910015,0.0010130391,0.00067988306,0.00086718984,0.0015576829,0.0010130034,0.0003205766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060767088,0.00008292588,0.0006297244,0.000052194064,0.000089410896,0.00008294074,0.00009825271,0.9335371,0.0020161832,0.007857403,0.0014872167,0.05400577],"study_design_scores_gemma":[0.000007752901,0.000016377413,0.000055616838,0.0000016818681,0.0000052729547,0.000008178542,0.0000036371646,0.99899656,0.00013086502,0.00053651945,0.00023540838,0.000002071347],"about_ca_topic_score_codex":0.005784427,"about_ca_topic_score_gemma":0.0058235917,"teacher_disagreement_score":0.005784427,"about_ca_system_score_codex":0.0005333938,"about_ca_system_score_gemma":0.0010481926,"threshold_uncertainty_score":0.011501491},"labels":[],"label_agreement":null},{"id":"W4377090272","doi":"10.1016/j.swevo.2023.101325","title":"Flow measurement data quality improvement-oriented optimal flow sensor configuration","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"National Natural Science Foundation of China","keywords":"Computer science; Observability; Initialization; Mathematical optimization; Redundancy (engineering); Evolutionary algorithm; Population; Artificial intelligence; Mathematics","score_opus":0.05776581992754409,"score_gpt":0.31222624011525335,"score_spread":0.25446042018770926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377090272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03850408,0.00013873982,0.9562325,0.00030515678,0.00005456715,0.0000906789,0.00006131207,0.0005759185,0.0040369593],"genre_scores_gemma":[0.87131655,0.00007811919,0.12683557,0.000094400726,0.000036872294,0.000083848754,0.00010920108,0.00005516253,0.0013902553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992986,0.00015417358,0.000039373943,0.00021215051,0.00019216037,0.00010348594],"domain_scores_gemma":[0.99916244,0.00014564897,0.00014891975,0.000109162,0.00037413454,0.00005977575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078620366,0.0009870708,0.00082301936,0.001060219,0.00055598293,0.0013113699,0.0009951887,0.0007566047,0.0018875197],"category_scores_gemma":[0.002876572,0.00038024053,0.0003575987,0.0007336148,0.00052579603,0.0015743243,0.0009726199,0.0006053688,0.00029418143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006325728,0.00031962318,0.0045477576,0.000180984,0.00007714358,0.00017252986,0.00014905153,0.67150515,0.06432539,0.010601443,0.0038164232,0.24367194],"study_design_scores_gemma":[0.000031098352,0.00011111839,0.0013975435,0.000011116189,0.000022430686,0.000053883647,0.00004003922,0.98203343,0.012756942,0.0027755764,0.00075282,0.000013936989],"about_ca_topic_score_codex":0.002834866,"about_ca_topic_score_gemma":0.0028712035,"teacher_disagreement_score":0.002834866,"about_ca_system_score_codex":0.0007584059,"about_ca_system_score_gemma":0.001414752,"threshold_uncertainty_score":0.006314397},"labels":[],"label_agreement":null},{"id":"W4384831640","doi":"10.1016/j.swevo.2023.101360","title":"Redefined decision variable analysis method for large-scale optimization and its application to feature selection","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Guangdong Provincial Pearl River Talents Program; Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Variable (mathematics); Scale (ratio); Feature (linguistics); Artificial intelligence; Data mining; Machine learning; Mathematics","score_opus":0.008654312035199448,"score_gpt":0.293204422974132,"score_spread":0.28455011093893257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384831640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016366491,0.00013751567,0.99759835,0.00003261894,0.000042571897,0.000012815858,0.000014277612,0.00007792773,0.00044730018],"genre_scores_gemma":[0.121737555,0.00040809318,0.8732686,0.00010698509,0.00010234635,0.0002409939,0.00012551366,0.0001970773,0.003812738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993149,0.00026836147,0.000040963245,0.000108664266,0.00023185689,0.00003531784],"domain_scores_gemma":[0.99903893,0.00051952846,0.00005432337,0.00007431621,0.00028636988,0.00002651636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016140337,0.0009045463,0.0012137463,0.00110757,0.00044033522,0.00095832377,0.001201458,0.0008398492,0.0031475215],"category_scores_gemma":[0.002939025,0.00039642854,0.0010923247,0.0011272653,0.00055974134,0.0008517688,0.00088582723,0.0016438273,0.0006693779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001521799,0.00015235298,0.00068531587,0.00027144965,0.00019472645,0.0001695277,0.00009167024,0.5369456,0.020335209,0.055234198,0.0041751307,0.38159278],"study_design_scores_gemma":[0.0000057695343,0.000015012949,0.00009133353,0.0000051725465,0.0000106714315,0.00001804873,0.0000026187054,0.9950423,0.000848591,0.0029660542,0.0009874139,0.0000069146126],"about_ca_topic_score_codex":0.0017226221,"about_ca_topic_score_gemma":0.001554763,"teacher_disagreement_score":0.0031475215,"about_ca_system_score_codex":0.000480335,"about_ca_system_score_gemma":0.0007712811,"threshold_uncertainty_score":0.0105294585},"labels":[],"label_agreement":null},{"id":"W4386170085","doi":"10.1016/j.swevo.2023.101387","title":"Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Differential evolution; Benchmark (surveying); Evolutionary algorithm; Artificial intelligence; Convergence (economics); Neuroevolution; Evolutionary computation; Artificial neural network; Machine learning; Population; Flexibility (engineering); Generality; Mathematical optimization; Mathematics","score_opus":0.02676152662992749,"score_gpt":0.2939925407969376,"score_spread":0.2672310141670101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386170085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033374965,0.0005310381,0.95805776,0.0002906083,0.00012991142,0.000051221108,0.000034393965,0.00022601795,0.00730407],"genre_scores_gemma":[0.8365816,0.00022298541,0.1565014,0.00025018142,0.000052000632,0.00020072756,0.000074631236,0.00010520998,0.006011364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997819,0.00007003932,0.000010430092,0.000029958146,0.000070666574,0.000036913938],"domain_scores_gemma":[0.99898463,0.0006666514,0.00006665189,0.0000601204,0.00016942971,0.00005237747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093309424,0.0005993579,0.0011230434,0.00048656698,0.00038849955,0.0006934964,0.0012066747,0.0014076647,0.0024720281],"category_scores_gemma":[0.002385334,0.00040645333,0.0004895376,0.00050264155,0.0007792335,0.0006122686,0.0014022666,0.0014127862,0.0002804943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029450337,0.00003996992,0.00028418962,0.000034276,0.000027185442,0.000028682567,0.000025907204,0.9659887,0.00069525244,0.0085997125,0.00058209075,0.023664607],"study_design_scores_gemma":[0.0000021545472,0.0000050798753,0.000013769424,0.0000013077154,0.0000011733681,0.0000019764414,9.88486e-7,0.9992849,0.000046655026,0.00055875705,0.00008248004,7.1930623e-7],"about_ca_topic_score_codex":0.0066088033,"about_ca_topic_score_gemma":0.0064009633,"teacher_disagreement_score":0.0066088033,"about_ca_system_score_codex":0.0009925256,"about_ca_system_score_gemma":0.0009843389,"threshold_uncertainty_score":0.013140678},"labels":[],"label_agreement":null},{"id":"W4388896605","doi":"10.1016/j.swevo.2023.101432","title":"A constrained multi-objective evolutionary algorithm with clustering based weight vector adaptation","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; CHEO Research Institute","keywords":"Cluster analysis; Mathematical optimization; Computer science; Population; Evolutionary algorithm; Classification of discontinuities; Convergence (economics); Multi-objective optimization; Set (abstract data type); Pareto principle; Optimization problem; Algorithm; Mathematics; Artificial intelligence","score_opus":0.01732310024901803,"score_gpt":0.24958132146428144,"score_spread":0.2322582212152634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388896605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008571883,0.0001813875,0.9885844,0.0000758723,0.000081954946,0.000059065802,0.000016986229,0.00018182238,0.00224672],"genre_scores_gemma":[0.21797624,0.00018403171,0.77573675,0.00016201386,0.00006130685,0.00039102224,0.00011186715,0.00010955916,0.0052671996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996494,0.00008047542,0.000022743232,0.00007241728,0.00014982102,0.000025092777],"domain_scores_gemma":[0.99960774,0.00013184204,0.00003495886,0.000040689967,0.00016026829,0.00002460146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080495875,0.00074651686,0.0012343228,0.0007326349,0.00053612946,0.0007905697,0.0018153882,0.001746195,0.0022428103],"category_scores_gemma":[0.0018194226,0.00055380835,0.0006710539,0.0012956082,0.0004161611,0.0007537661,0.0010390838,0.00082538417,0.0005161695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008498507,0.0001130762,0.00048938056,0.00008582636,0.0001273794,0.00009480833,0.000059710812,0.82550144,0.008405154,0.006910592,0.0018314712,0.15629609],"study_design_scores_gemma":[0.000010176325,0.000016850277,0.000074563686,0.0000030751728,0.0000064428737,0.000015553978,0.0000022231768,0.99879885,0.0003552229,0.000343452,0.00036892045,0.0000046480955],"about_ca_topic_score_codex":0.0040459055,"about_ca_topic_score_gemma":0.004039944,"teacher_disagreement_score":0.0040459055,"about_ca_system_score_codex":0.00042664324,"about_ca_system_score_gemma":0.00077548815,"threshold_uncertainty_score":0.00804472},"labels":[],"label_agreement":null},{"id":"W4391508438","doi":"10.1016/j.swevo.2024.101493","title":"Reliable network-level pavement maintenance budget allocation: Algorithm selection and parameter tuning matter","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National University of Singapore","keywords":"Computer science; Sorting; Reliability (semiconductor); Selection (genetic algorithm); Genetic algorithm; Reliability engineering; Differential evolution; Cluster analysis; Pavement management; Mathematical optimization; Algorithm; Machine learning; Mathematics; Engineering; Transport engineering","score_opus":0.007500497427992747,"score_gpt":0.21104274413525917,"score_spread":0.20354224670726642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391508438","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06116211,0.00022027285,0.93454254,0.000334247,0.000036119472,0.00004036241,0.000033598284,0.0004899667,0.0031407597],"genre_scores_gemma":[0.8860725,0.000099782956,0.11232845,0.000057682137,0.000031766336,0.0000648878,0.00005451899,0.00009028896,0.0012000123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996184,0.00014176378,0.000020590473,0.000094467105,0.0000772958,0.0000474231],"domain_scores_gemma":[0.9985335,0.000874222,0.00014864231,0.00017357526,0.00021976125,0.000050345385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012272005,0.000530728,0.000751161,0.00053838344,0.0003042215,0.00094501156,0.0010075278,0.0008494096,0.0017069709],"category_scores_gemma":[0.007310082,0.00030448727,0.0002535501,0.0004596116,0.00037222423,0.0013810403,0.00057037035,0.00070153864,0.00031130246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100006226,0.00005149343,0.0013145766,0.000039509105,0.000040648156,0.000024236246,0.00004750778,0.9135368,0.002566874,0.0032546495,0.0008628203,0.078160994],"study_design_scores_gemma":[0.000010734721,0.000012157029,0.00019085761,0.0000034279913,0.0000059132567,0.000009894204,0.0000075788967,0.99761593,0.0005713663,0.001394725,0.00017513726,0.000002253487],"about_ca_topic_score_codex":0.0022403405,"about_ca_topic_score_gemma":0.0022772895,"teacher_disagreement_score":0.0022403405,"about_ca_system_score_codex":0.000554039,"about_ca_system_score_gemma":0.0009143666,"threshold_uncertainty_score":0.0064901114},"labels":[],"label_agreement":null},{"id":"W4391677279","doi":"10.1016/j.swevo.2024.101507","title":"A customized adaptive large neighborhood search algorithm for solving a multi-objective home health care problem in a pandemic environment","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Ministry of Education of the People's Republic of China","keywords":"Computer science; Heuristics; Workload; Pareto principle; Simulated annealing; Mathematical optimization; Heuristic; Multi-objective optimization; Constraint programming; Vehicle routing problem; Context (archaeology); Scheduling (production processes); Solver; Operations research; Algorithm; Routing (electronic design automation); Machine learning; Artificial intelligence; Stochastic programming; Mathematics","score_opus":0.017681700088664726,"score_gpt":0.28390980542097294,"score_spread":0.26622810533230823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391677279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046222247,0.0002780051,0.947942,0.00021863116,0.000112788155,0.00011776519,0.000057461923,0.00024095987,0.004810251],"genre_scores_gemma":[0.5822699,0.00018724531,0.41255462,0.00016356594,0.000057629182,0.00041753292,0.0001824428,0.00007945026,0.004087609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997788,0.00008528437,0.000011408257,0.000040098283,0.00005524209,0.000029130255],"domain_scores_gemma":[0.99958724,0.00024290926,0.000035868128,0.000021780237,0.00008342548,0.00002885088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078098266,0.00059176417,0.00089668506,0.00057146,0.00046085272,0.00050405745,0.0013067015,0.0013652191,0.001996707],"category_scores_gemma":[0.0017017672,0.00035421798,0.00058618153,0.000539894,0.00041122606,0.000540079,0.0009043192,0.00059427996,0.00016718231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041197232,0.000040839852,0.0003270366,0.000027311684,0.00002222759,0.000050241975,0.000022531034,0.97672325,0.0006016125,0.002447123,0.0006856129,0.019011093],"study_design_scores_gemma":[0.000008219634,0.000015125287,0.000038341037,0.0000015196081,0.0000025305928,0.0000056042286,0.000004268737,0.9994666,0.00005191685,0.00026980165,0.00013468953,0.0000014241214],"about_ca_topic_score_codex":0.0075142286,"about_ca_topic_score_gemma":0.007131267,"teacher_disagreement_score":0.0075142286,"about_ca_system_score_codex":0.00051419344,"about_ca_system_score_gemma":0.0010368095,"threshold_uncertainty_score":0.014940977},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.1356344590218789,"score_gpt":0.36211721861281404,"score_spread":0.22648275959093514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392190020","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01277703,0.3465473,0.6150962,0.0015763898,0.000534381,0.00009967555,0.00007041849,0.00035777662,0.022940831],"genre_scores_gemma":[0.33967403,0.37424278,0.27300298,0.00079145207,0.0015657188,0.000196226,0.00028676758,0.00015346489,0.010086578],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958295,0.00013319263,0.00003413331,0.000076180855,0.00014810511,0.000025497247],"domain_scores_gemma":[0.99914014,0.0005449906,0.000049588154,0.000052117834,0.00017946176,0.000033681652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010057214,0.0006535374,0.0015216281,0.0007963516,0.00024311578,0.0013899541,0.0011348769,0.0009905716,0.0013977649],"category_scores_gemma":[0.002114051,0.00026331638,0.0005556229,0.0020296383,0.00042832477,0.001219518,0.00061873224,0.0009217861,0.0005713938],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007850229,0.00023545467,0.0015904041,0.001336301,0.00012598882,0.00005787011,0.0000655346,0.102511555,0.0014106373,0.029060664,0.004532165,0.85899484],"study_design_scores_gemma":[0.00005353636,0.00043795674,0.0015820657,0.0005891379,0.0001503611,0.00036002463,0.0001383681,0.86183065,0.0020535772,0.041141875,0.09160891,0.00005349798],"about_ca_topic_score_codex":0.0013916151,"about_ca_topic_score_gemma":0.001145683,"teacher_disagreement_score":0.0015216281,"about_ca_system_score_codex":0.00035823372,"about_ca_system_score_gemma":0.0008195802,"threshold_uncertainty_score":0.0053188205},"labels":[],"label_agreement":null},{"id":"W4396527603","doi":"10.1016/j.swevo.2024.101575","title":"Information interaction and partial growth-based multi-population growable genetic algorithm for multi-dimensional resources utilization optimization of cloud computing","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Chengdu Science and Technology Program; Key Research and Development Program of Sichuan Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Population; Crossover; Convergence (economics); Distributed computing; Mathematical optimization; Adaptability; Genetic algorithm; Dimension (graph theory); Resource (disambiguation); Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.024392673956720675,"score_gpt":0.26946205155558917,"score_spread":0.2450693775988685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396527603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07003297,0.00060906506,0.92130214,0.00038747626,0.00010460787,0.000077049786,0.000049685346,0.00022050588,0.0072165076],"genre_scores_gemma":[0.87918377,0.000327167,0.11648587,0.0001165778,0.000045676825,0.0002197763,0.00012292361,0.00006031434,0.0034379994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996587,0.00010780554,0.000014609903,0.000052944426,0.000119471515,0.000046395606],"domain_scores_gemma":[0.9995808,0.00022740988,0.000038627117,0.000020768666,0.00010958643,0.000022823257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007463568,0.0006347537,0.0009666572,0.00059173995,0.0006853784,0.0008099646,0.0015128555,0.00095575425,0.0012343033],"category_scores_gemma":[0.0018256762,0.00034609172,0.00064912013,0.000772232,0.0006019739,0.001059039,0.0011675395,0.0008066818,0.00011816232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003122906,0.000027570588,0.00035075698,0.000029213807,0.00002351868,0.000031220126,0.000046028592,0.9760559,0.0012932253,0.005170995,0.0004828664,0.016457455],"study_design_scores_gemma":[0.00000298734,0.00001015266,0.000032991607,0.0000011444423,0.000002910759,0.0000036643603,0.0000029909634,0.99942076,0.00008102899,0.00037265054,0.000067367924,0.0000013811891],"about_ca_topic_score_codex":0.009137156,"about_ca_topic_score_gemma":0.005533281,"teacher_disagreement_score":0.009137156,"about_ca_system_score_codex":0.00094493636,"about_ca_system_score_gemma":0.0011025617,"threshold_uncertainty_score":0.018167913},"labels":[],"label_agreement":null},{"id":"W4399555383","doi":"10.1016/j.swevo.2024.101614","title":"Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing hyperspectral images","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Science and Technology Foundation of Shenzhen City","keywords":"Hyperspectral imaging; Computer science; Particle swarm optimization; Selection (genetic algorithm); Swarm behaviour; Remote sensing; Artificial intelligence; Mathematical optimization; Algorithm; Geology; Mathematics","score_opus":0.019255237625432547,"score_gpt":0.24838162116408438,"score_spread":0.22912638353865183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399555383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02727437,0.00020758944,0.9702967,0.00010538005,0.000038785973,0.000035624133,0.000016071825,0.00015542956,0.0018701252],"genre_scores_gemma":[0.5344316,0.00025184182,0.45989397,0.0001499625,0.00009174589,0.00022737058,0.00009654333,0.00010549067,0.004751424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977833,0.00008749659,0.000010529045,0.000036531237,0.000066324814,0.000020940266],"domain_scores_gemma":[0.999741,0.00013320491,0.000032978773,0.000018750496,0.000059860526,0.000014126807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086772296,0.0007628174,0.00082734245,0.0005721162,0.0003744815,0.0007660522,0.00074610155,0.001042103,0.0012675402],"category_scores_gemma":[0.0010309828,0.00040099947,0.00071414816,0.0004869393,0.0004222766,0.00048524459,0.0005526272,0.0006887209,0.00024423675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075210526,0.00008411155,0.00042673716,0.00004932455,0.00009139186,0.00005676349,0.00005090387,0.917936,0.0049605565,0.0033300966,0.0008228587,0.07211607],"study_design_scores_gemma":[0.0000030064832,0.000009100222,0.000051916002,0.000001061343,0.0000042778365,0.0000030613282,0.0000018319975,0.9994655,0.00017929588,0.0002109956,0.00006877831,0.0000011744916],"about_ca_topic_score_codex":0.0029368277,"about_ca_topic_score_gemma":0.0028508895,"teacher_disagreement_score":0.0029368277,"about_ca_system_score_codex":0.00035403145,"about_ca_system_score_gemma":0.00049406535,"threshold_uncertainty_score":0.005839467},"labels":[],"label_agreement":null},{"id":"W4401044108","doi":"10.1016/j.swevo.2024.101680","title":"Two-stage knowledge-assisted coevolutionary NSGA-II for bi-objective path planning of multiple unmanned aerial vehicles","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Motion planning; Path (computing); Stage (stratigraphy); Artificial intelligence; Operations research; Real-time computing; Computer network; Robot; Geology","score_opus":0.028959557971983425,"score_gpt":0.29903018347065285,"score_spread":0.27007062549866945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401044108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17580394,0.0003164975,0.8129934,0.00023127269,0.000104813575,0.00021449119,0.0000646976,0.00045351437,0.009817355],"genre_scores_gemma":[0.9070509,0.00006936655,0.08881659,0.00009040936,0.000014047865,0.00034011144,0.000091235495,0.00003092174,0.003496412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970776,0.00007361588,0.000016110122,0.00004820725,0.000089576635,0.00006472633],"domain_scores_gemma":[0.99964154,0.0001361953,0.000034585446,0.000037819565,0.000111385074,0.00003846855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007452783,0.00081595825,0.0009851247,0.00044198942,0.00055472384,0.000629626,0.0016180705,0.001111657,0.0015541665],"category_scores_gemma":[0.0013822707,0.0004868834,0.00062130345,0.00041912877,0.00053591846,0.000625027,0.0014992201,0.0010169259,0.00020001651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042184674,0.000035884394,0.00037554142,0.000019185563,0.000027759315,0.00003732337,0.000047033845,0.9782096,0.0009416517,0.0012692402,0.0002804562,0.018714136],"study_design_scores_gemma":[0.0000064428614,0.000022829972,0.00006354379,0.000001561089,0.0000041184126,0.000003979987,0.000004806732,0.9995003,0.00012686723,0.00017977845,0.000083841805,0.0000018953598],"about_ca_topic_score_codex":0.013361828,"about_ca_topic_score_gemma":0.012174401,"teacher_disagreement_score":0.013361828,"about_ca_system_score_codex":0.00074464676,"about_ca_system_score_gemma":0.0014507849,"threshold_uncertainty_score":0.026568115},"labels":[],"label_agreement":null},{"id":"W4401534062","doi":"10.1016/j.swevo.2024.101699","title":"An improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Drone; Variable (mathematics); Vehicle routing problem; Synchronization (alternating current); Routing (electronic design automation); Variable neighborhood search; Algorithm; Real-time computing; Metaheuristic; Computer network","score_opus":0.005274337485175659,"score_gpt":0.22468573532558977,"score_spread":0.2194113978404141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401534062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037023097,0.00048814266,0.9569588,0.00016312853,0.00014174379,0.00007298384,0.00006934582,0.00021955906,0.004863286],"genre_scores_gemma":[0.65358543,0.00034560155,0.3376727,0.00012621937,0.00007883465,0.00029620357,0.00029274152,0.00009024536,0.007512054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996941,0.000077489865,0.000014196128,0.000079924655,0.00008896907,0.000045282966],"domain_scores_gemma":[0.99966276,0.0001714896,0.00003432744,0.000022217338,0.000083931205,0.000025286132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006419595,0.00065518846,0.0011992353,0.0006258365,0.00046888218,0.00063140196,0.0014793664,0.0011496778,0.0023132272],"category_scores_gemma":[0.0011534953,0.0003361662,0.0005572655,0.00070298323,0.0003546089,0.000816954,0.00085490674,0.0006521098,0.00021800997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103596394,0.0000670577,0.0003964981,0.000051300463,0.00003740744,0.000042135074,0.000037713125,0.9383751,0.0016161255,0.0059448886,0.0015817065,0.051746566],"study_design_scores_gemma":[0.000010138563,0.000023007857,0.000045152054,0.00000169854,0.0000037093014,0.000006301316,0.0000043618893,0.9991596,0.00011104857,0.00042155958,0.000211781,0.0000017575722],"about_ca_topic_score_codex":0.009916417,"about_ca_topic_score_gemma":0.0077958535,"teacher_disagreement_score":0.009916417,"about_ca_system_score_codex":0.00058886263,"about_ca_system_score_gemma":0.0012687426,"threshold_uncertainty_score":0.019717395},"labels":[],"label_agreement":null},{"id":"W4403088095","doi":"10.1016/j.swevo.2024.101742","title":"A comparative study of evolutionary algorithms and particle swarm optimization approaches for constrained multi-objective optimization problems","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metaheuristic; Multi-swarm optimization; Particle swarm optimization; Mathematical optimization; Evolutionary algorithm; Imperialist competitive algorithm; Algorithm; Multi-objective optimization; Meta-optimization; Derivative-free optimization; Optimization algorithm; Parallel metaheuristic; Artificial intelligence; Machine learning; Mathematics","score_opus":0.05117202170374641,"score_gpt":0.2966133150463342,"score_spread":0.2454412933425878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403088095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093242384,0.11694547,0.7309185,0.0022540179,0.0005622612,0.00035400176,0.00012757098,0.0002557116,0.055340074],"genre_scores_gemma":[0.4955162,0.07439662,0.42293924,0.00060408894,0.0003643781,0.0003305064,0.00034170336,0.00009417711,0.0054130303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798334,0.0008034862,0.00015240852,0.00018020612,0.00079925073,0.00008129431],"domain_scores_gemma":[0.99739635,0.0017466103,0.00013878357,0.000098621626,0.00056615414,0.000053489406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029483358,0.0011763957,0.0010072332,0.0020872015,0.00054499344,0.0016318253,0.00091949716,0.0013295448,0.0014073832],"category_scores_gemma":[0.0062215608,0.000326246,0.0011174264,0.0034343419,0.00055588776,0.0023752463,0.00077723636,0.0011625849,0.00020804629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018891858,0.00029208983,0.003819643,0.001502916,0.00045773096,0.00025345857,0.00024189937,0.4712007,0.0016467993,0.04420607,0.003173877,0.47301596],"study_design_scores_gemma":[0.00006305541,0.00053183455,0.004545319,0.000593452,0.00022783439,0.0004287731,0.00035986345,0.94532555,0.0017689803,0.015918195,0.030177511,0.00005961956],"about_ca_topic_score_codex":0.0027142083,"about_ca_topic_score_gemma":0.0023358993,"teacher_disagreement_score":0.0029483358,"about_ca_system_score_codex":0.000764798,"about_ca_system_score_gemma":0.0010686533,"threshold_uncertainty_score":0.015592456},"labels":[],"label_agreement":null},{"id":"W4405929204","doi":"10.1016/j.swevo.2024.101827","title":"Population-level center-based sampling for meta-heuristic algorithms","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"Brock University","keywords":"Computer science; Center (category theory); Heuristic; Sampling (signal processing); Algorithm; Population; Meta heuristic; Artificial intelligence; Telecommunications","score_opus":0.0861505226043258,"score_gpt":0.33446159651186075,"score_spread":0.24831107390753493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405929204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01695291,0.00041486692,0.9801194,0.00012676865,0.00003922371,0.000112191534,0.000027541244,0.00036301004,0.0018439427],"genre_scores_gemma":[0.59924537,0.00048711675,0.3975523,0.00025240684,0.00007155466,0.00053431035,0.00019886665,0.00016086969,0.0014971562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985057,0.0007901206,0.000052740026,0.00017687114,0.00036400545,0.000110643654],"domain_scores_gemma":[0.99722433,0.0016584634,0.0001881264,0.00029407596,0.00055239926,0.00008258894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030132243,0.0008895564,0.0011499631,0.00092093163,0.0007135889,0.0009338684,0.0015901627,0.0009566598,0.0013638929],"category_scores_gemma":[0.0073336805,0.00040562986,0.0007580034,0.0008157841,0.0008701456,0.0010817492,0.0011424534,0.0012348096,0.0003768135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001579362,0.00014778029,0.0020699964,0.00014689956,0.00011438585,0.00008069231,0.00015404694,0.85902923,0.0038670818,0.0292065,0.0020434486,0.10298196],"study_design_scores_gemma":[0.00001862161,0.00006258476,0.00010458988,0.000008852145,0.000014419391,0.000019734889,0.00001748866,0.99214774,0.001180385,0.0054534646,0.0009659893,0.0000062054182],"about_ca_topic_score_codex":0.0025172806,"about_ca_topic_score_gemma":0.0026681356,"teacher_disagreement_score":0.0030132243,"about_ca_system_score_codex":0.0010256486,"about_ca_system_score_gemma":0.0014970361,"threshold_uncertainty_score":0.01593566},"labels":[],"label_agreement":null},{"id":"W4405964481","doi":"10.1016/j.swevo.2024.101830","title":"An evolutionary task scheduling algorithm using fuzzy fitness evaluation method for communication satellite network","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Satellite Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Evolutionary algorithm; Task (project management); Fuzzy logic; Scheduling (production processes); Satellite; Algorithm; Artificial intelligence; Mathematical optimization","score_opus":0.03409292097560975,"score_gpt":0.33857988907856557,"score_spread":0.30448696810295584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405964481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087240286,0.00032907558,0.90690714,0.00016573633,0.00011690258,0.0000714186,0.000030785704,0.00019392512,0.00494477],"genre_scores_gemma":[0.70434827,0.00016086658,0.2906872,0.00007141911,0.000033791508,0.00016370276,0.000067957655,0.000049578633,0.004417303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983156,0.000043467382,0.000009041992,0.000025379848,0.00006330416,0.000027268048],"domain_scores_gemma":[0.9997806,0.000087739616,0.000017952994,0.000012522505,0.00008692574,0.000014324204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056513696,0.00040638572,0.0005268644,0.0005478956,0.0005181402,0.00044264298,0.0006766784,0.0005767599,0.0012491982],"category_scores_gemma":[0.0010297103,0.00019881957,0.000390691,0.0005096435,0.00023231863,0.00037058012,0.0002973713,0.0003826921,0.0000907979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007189296,0.00006584037,0.0006926922,0.000037067435,0.000044154556,0.00005493567,0.000054492986,0.8873796,0.00575303,0.0048039244,0.0009724273,0.10006995],"study_design_scores_gemma":[0.000010169352,0.000027713275,0.00015853143,0.0000029240036,0.000008080898,0.000012239447,0.0000053249228,0.9986085,0.00048224514,0.00038453896,0.00029675107,0.0000029023365],"about_ca_topic_score_codex":0.0072249407,"about_ca_topic_score_gemma":0.005761314,"teacher_disagreement_score":0.0072249407,"about_ca_system_score_codex":0.0006080258,"about_ca_system_score_gemma":0.0008346813,"threshold_uncertainty_score":0.014365792},"labels":[],"label_agreement":null},{"id":"W4407598381","doi":"10.1016/j.swevo.2024.101839","title":"Evolutionary algorithm based on multi-probability distribution model for stochastic optimization","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Estimation of distribution algorithm; Algorithm; Evolutionary algorithm; Stochastic optimization; Probability distribution; Mathematical optimization; Artificial intelligence; Mathematics; Statistics","score_opus":0.029423580042932097,"score_gpt":0.2983921817003596,"score_spread":0.2689686016574275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407598381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045846542,0.00040007482,0.99253243,0.00016476306,0.00007005281,0.000016666414,0.000013188418,0.000039973096,0.0021782506],"genre_scores_gemma":[0.58505934,0.0018863496,0.39949682,0.00021645582,0.00024381524,0.0003320069,0.00018641136,0.00015384621,0.012424897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941933,0.00025301042,0.000023454253,0.000079379075,0.00018393793,0.00004086069],"domain_scores_gemma":[0.99917847,0.0005578071,0.000056891786,0.000043451673,0.0001349137,0.000028390674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011550583,0.0006865037,0.0014836673,0.0007610549,0.00058324845,0.0011453235,0.0015506344,0.0014349814,0.0015663495],"category_scores_gemma":[0.0031737296,0.0004883633,0.001253674,0.0013540927,0.00086255267,0.0018430321,0.00095348543,0.0018590089,0.00025786573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001346208,0.000021075102,0.00022319956,0.00004127816,0.000044502955,0.000039368144,0.000028644074,0.92555094,0.0005511978,0.06063717,0.0005000145,0.012349123],"study_design_scores_gemma":[0.000002573326,0.0000050025674,0.000030168792,0.000002118489,0.000003142686,0.00000768948,0.0000014090742,0.995363,0.000036396134,0.0043550837,0.000191391,0.0000020671496],"about_ca_topic_score_codex":0.004852188,"about_ca_topic_score_gemma":0.0024949436,"teacher_disagreement_score":0.004852188,"about_ca_system_score_codex":0.001090939,"about_ca_system_score_gemma":0.0009601461,"threshold_uncertainty_score":0.009647846},"labels":[],"label_agreement":null},{"id":"W4407980866","doi":"10.1016/j.swevo.2025.101877","title":"Energy-efficient task scheduling with binary random faults in cloud computing environments","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Education of the People's Republic of China; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Binary number; Distributed computing; Scheduling (production processes); Task (project management); Parallel computing; Operating system; Mathematical optimization","score_opus":0.006249074124704185,"score_gpt":0.21968532225862175,"score_spread":0.21343624813391757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407980866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6278526,0.0010413576,0.3617965,0.0009952759,0.00034894983,0.00008840066,0.00014892886,0.00079661957,0.0069314055],"genre_scores_gemma":[0.9900227,0.000044376946,0.009277807,0.000022779659,0.000013056381,0.000009010806,0.000019226572,0.00002317381,0.0005677971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948114,0.00013048164,0.000025851816,0.00006727947,0.0001218375,0.0001733742],"domain_scores_gemma":[0.9988238,0.0005578748,0.00013873748,0.0001465986,0.00022327433,0.00010974577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067675003,0.000389031,0.0006507982,0.00039366639,0.0006859887,0.00086379796,0.0008517754,0.00039429907,0.00085372746],"category_scores_gemma":[0.0031585926,0.0002039395,0.00019884385,0.00062861806,0.00038267585,0.0008628676,0.0005068246,0.0004185209,0.00012364287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082638423,0.00012474852,0.0014109091,0.00006902644,0.000026104733,0.00013800438,0.00005370107,0.9452901,0.006256971,0.007497074,0.001992785,0.03631422],"study_design_scores_gemma":[0.000014821424,0.000029979272,0.0002445415,0.0000016299609,0.000004202993,0.00001784189,0.000020184008,0.9959888,0.0010464622,0.0024744002,0.00015437712,0.000002727277],"about_ca_topic_score_codex":0.003949483,"about_ca_topic_score_gemma":0.0048967754,"teacher_disagreement_score":0.003949483,"about_ca_system_score_codex":0.00079401996,"about_ca_system_score_gemma":0.0012238387,"threshold_uncertainty_score":0.007853031},"labels":[],"label_agreement":null},{"id":"W4410124548","doi":"10.1016/j.swevo.2025.101962","title":"Multimodal multi-objective optimization via multi-operator adaptation and clustering-based environmental selection","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Fredericton","funders":"National Natural Science Foundation of China","keywords":"Computer science; Adaptation (eye); Selection (genetic algorithm); Cluster analysis; Operator (biology); Artificial intelligence; Mathematical optimization","score_opus":0.010375414389769737,"score_gpt":0.2416787254102316,"score_spread":0.23130331102046187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410124548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021111052,0.00016079866,0.97556496,0.00007539126,0.000037038044,0.0000363755,0.000015806634,0.00018824742,0.002810378],"genre_scores_gemma":[0.6585433,0.00017520516,0.3375637,0.00010319759,0.000046517507,0.00021638603,0.000081397746,0.00013537463,0.0031348905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958485,0.00015320016,0.000016135324,0.00007726858,0.00012727147,0.000041352858],"domain_scores_gemma":[0.9995152,0.00021650946,0.000059288697,0.00005739567,0.00012620963,0.0000254077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009656119,0.0009054577,0.00087519374,0.0007147221,0.0005413435,0.00059619726,0.0012265916,0.0010615891,0.0015602324],"category_scores_gemma":[0.002216764,0.0003891445,0.0009158655,0.0010068681,0.0006981758,0.0011111251,0.001322183,0.00077784987,0.00029760896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050516017,0.00006135859,0.00036635372,0.000034555866,0.00006488538,0.000049494272,0.000059524744,0.9384826,0.005187327,0.0052093384,0.00062887534,0.04980529],"study_design_scores_gemma":[0.0000029021894,0.000010743801,0.00008042459,0.0000017520135,0.000004378023,0.00000797325,0.0000042683105,0.9986141,0.0003647756,0.0007944609,0.00011022247,0.0000039969896],"about_ca_topic_score_codex":0.0022840633,"about_ca_topic_score_gemma":0.0027852908,"teacher_disagreement_score":0.0022840633,"about_ca_system_score_codex":0.0004496777,"about_ca_system_score_gemma":0.000492559,"threshold_uncertainty_score":0.005219519},"labels":[],"label_agreement":null},{"id":"W7110041880","doi":"10.1016/j.swevo.2025.102237","title":"A complementary heterogeneity-driven adaptive balance search method for cognitive-only particle swarm optimization family","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Metaheuristic; Balance (ability); Optimization algorithm; Swarm behaviour","score_opus":0.043815077622409535,"score_gpt":0.35555934840301373,"score_spread":0.3117442707806042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110041880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014641769,0.00035754655,0.97441137,0.00018674247,0.00013660845,0.00006197894,0.000031615196,0.00014874371,0.010023578],"genre_scores_gemma":[0.61494,0.0005289985,0.36689004,0.0003176106,0.00027213868,0.00047562053,0.0002000621,0.0002270523,0.016148524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979895,0.00005072303,0.000008935779,0.000029956198,0.000092140566,0.000019310728],"domain_scores_gemma":[0.9997656,0.00008283068,0.000019888235,0.000022883403,0.00008641,0.000022405986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005940084,0.00064417225,0.0007837576,0.0007428366,0.0005347122,0.00080158195,0.0014158012,0.0009875788,0.0034063382],"category_scores_gemma":[0.0013307439,0.00028693397,0.0005655952,0.0006387432,0.00046057865,0.00084302376,0.0013430101,0.0007219377,0.00057470537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022196412,0.00013842434,0.0009644119,0.0001721272,0.00013908453,0.00010200397,0.00012333045,0.70474,0.009583238,0.070947535,0.0055294726,0.2073384],"study_design_scores_gemma":[0.000012003258,0.000023663099,0.00006372606,0.0000039598704,0.000007874646,0.000011622114,0.0000039906695,0.9965379,0.00024607312,0.0022852195,0.00080042944,0.000003470091],"about_ca_topic_score_codex":0.0019530014,"about_ca_topic_score_gemma":0.0016642761,"teacher_disagreement_score":0.0034063382,"about_ca_system_score_codex":0.00041950232,"about_ca_system_score_gemma":0.0006308513,"threshold_uncertainty_score":0.011395335},"labels":[],"label_agreement":null}]}