{"meta":{"query_hash":"c99f131f2551","filters":{"topic":"Evolutionary Algorithms and Applications"},"cohort_total":691,"direct_labels_cover":0,"predictions_cover":691,"exported":691,"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/c99f131f2551","api":"https://metacan.xera.ac/api/v1/cohort?topic=Evolutionary+Algorithms+and+Applications"},"results":[{"id":"W112571559","doi":"10.1007/0-387-29026-5_3","title":"Combinatorial Evolutionary Methods in Wireless Mobile Computing","year":2006,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Evolutionary Algorithms and Applications","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 Ottawa; University of Windsor","funders":"","keywords":"Computer science; Wireless; Distributed computing; Computational biology; Biology; Telecommunications","score_opus":0.01828775062124818,"score_gpt":0.29461983739004444,"score_spread":0.2763320867687963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112571559","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000045347915,0.0010097603,0.19723916,0.00036219353,0.0023327256,0.0009481129,0.000026703732,0.0005257541,0.79751027],"genre_scores_gemma":[0.013802929,0.000057122597,0.23172691,0.001180451,0.0044534346,0.0006543999,0.00049763545,0.00035518725,0.7472719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99567115,0.00015382044,0.0012277457,0.00138054,0.0007846801,0.0007820893],"domain_scores_gemma":[0.99734724,0.00045450692,0.00060420757,0.0010899015,0.0002549478,0.00024917317],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0012458349,0.000659023,0.00074334105,0.00068862457,0.00031884367,0.00034661376,0.0028813279,0.001508016,0.000044382054],"category_scores_gemma":[0.000029081008,0.00073940435,0.00029141805,0.0001978073,0.0003175582,0.0011370246,0.0011989349,0.0031427941,0.000061006704],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041700205,0.00003678619,0.00003658261,0.000026942254,0.00003746827,0.000017925435,0.00014010967,0.00016865521,0.00006620322,0.8396623,0.10820414,0.051598705],"study_design_scores_gemma":[0.0006905195,0.000045163997,0.00015892154,0.00016892298,0.000023488785,0.000049136175,0.00001266413,0.027029417,0.000027499476,0.36214533,0.60880035,0.00084856263],"about_ca_topic_score_codex":0.000105556144,"about_ca_topic_score_gemma":0.0000027030007,"teacher_disagreement_score":0.5005962,"about_ca_system_score_codex":0.0006955766,"about_ca_system_score_gemma":0.0006111612,"threshold_uncertainty_score":0.9997882},"labels":[],"label_agreement":null},{"id":"W113154612","doi":"10.1007/978-3-642-33093-3_22","title":"Adaptation and Genomic Evolution in EcoSim","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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 Windsor","funders":"","keywords":"Computer science; Adaptation (eye); Biology","score_opus":0.017583893813993903,"score_gpt":0.22883524677568073,"score_spread":0.21125135296168684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W113154612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048529432,0.002434208,0.995007,0.00059244095,0.00042601387,0.00030186956,0.0000035771584,0.00006375532,0.0006858721],"genre_scores_gemma":[0.5291963,0.00019589398,0.4698162,0.00027951945,0.00034636277,0.000022019402,0.000006660488,0.000017507105,0.00011948274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980073,0.000021003232,0.00033986982,0.00085136126,0.0003670873,0.00041339168],"domain_scores_gemma":[0.9988671,0.00017461294,0.00015382313,0.0006053769,0.000084394764,0.00011470505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005422321,0.00026341455,0.00023815947,0.00061698566,0.00017904668,0.00015855406,0.0010002,0.00019115902,0.000008207783],"category_scores_gemma":[0.000017483717,0.00026743353,0.000040573374,0.00042688864,0.00030362676,0.00072053925,0.0005974537,0.00041826777,0.000035710324],"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":[0.0000018806679,0.000038306112,0.00036799497,0.0000171852,0.0000033196122,0.0000070526407,0.000747313,0.03704809,0.00019881474,0.17232287,0.000006579908,0.7892406],"study_design_scores_gemma":[0.00015362998,0.000039545972,0.011353917,0.000081309285,0.0000029594808,0.000039668033,2.418394e-7,0.76354945,0.00003094577,0.2236366,0.0007955682,0.00031620104],"about_ca_topic_score_codex":0.00005932042,"about_ca_topic_score_gemma":0.00016642484,"teacher_disagreement_score":0.7889244,"about_ca_system_score_codex":0.00045373102,"about_ca_system_score_gemma":0.00029377552,"threshold_uncertainty_score":0.99997777},"labels":[],"label_agreement":null},{"id":"W1174219372","doi":"10.1007/978-3-319-21500-6_15","title":"Unary Patterns with Permutations","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"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 Winnipeg","funders":"","keywords":"Unary operation; Combinatorics; Sigma; Mathematics; Alphabet; Integer (computer science); Pi; Word (group theory); Function (biology); Discrete mathematics; Physics; Computer science; Geometry; Genetics; Philosophy; Biology","score_opus":0.021343112257833988,"score_gpt":0.25421918387509906,"score_spread":0.2328760716172651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1174219372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054175205,0.00030171685,0.9920061,0.0018086563,0.00037860117,0.0003201643,0.000018241675,0.00017446962,0.004937829],"genre_scores_gemma":[0.15830278,0.00004013257,0.8380771,0.0010881912,0.00062362885,0.00005425356,0.000039761908,0.00004976943,0.0017243286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716353,0.000017043169,0.00031206166,0.0011637015,0.00090117834,0.0004425088],"domain_scores_gemma":[0.99780273,0.00017810547,0.000174729,0.0012320824,0.00039766697,0.000214709],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036427594,0.0003686656,0.00029624003,0.00048244165,0.00030470314,0.0002944892,0.002317348,0.00016472548,0.00002607803],"category_scores_gemma":[0.00001624963,0.00030754454,0.000062806175,0.00058935,0.0004766397,0.0006183034,0.0007165,0.00053997344,0.000075550204],"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":[0.000005436006,0.00012183256,0.0003606403,0.000037662212,0.00002676846,0.00020337396,0.0013906439,0.14058045,0.000028855606,0.30920553,0.00026646635,0.54777235],"study_design_scores_gemma":[0.00034018068,0.0002784208,0.00089348614,0.00027302682,0.000012062755,0.0003132841,5.7016365e-7,0.6263909,0.000061814164,0.3643709,0.006237107,0.0008282544],"about_ca_topic_score_codex":0.000026492184,"about_ca_topic_score_gemma":0.00007306487,"teacher_disagreement_score":0.5469441,"about_ca_system_score_codex":0.00028018563,"about_ca_system_score_gemma":0.00093343435,"threshold_uncertainty_score":0.99993765},"labels":[],"label_agreement":null},{"id":"W118937142","doi":"","title":"Evolutionary Graphs on Two Levels.","year":2008,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics","score_opus":0.02267969934936548,"score_gpt":0.2519833874877529,"score_spread":0.22930368813838742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118937142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91921234,0.0007168579,0.026213747,0.006528725,0.0044566193,0.0006291912,0.000025015224,0.001265993,0.04095149],"genre_scores_gemma":[0.9854784,0.00002672506,0.013438682,0.00034309513,0.000014645535,0.000049274982,0.0000058775086,0.000009032544,0.00063429406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99884105,0.000041267427,0.00017544214,0.00037106028,0.00031831788,0.00025283828],"domain_scores_gemma":[0.99899596,0.00009118641,0.00005700762,0.00064534706,0.00009668679,0.000113825125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009305798,0.00013209163,0.000114552975,0.000102483784,0.0005137955,0.000022006514,0.0007023985,0.0000450942,0.000024738094],"category_scores_gemma":[0.000016189415,0.00013262566,0.000077822195,0.00060388393,0.00009294452,0.0003301474,0.00014616134,0.00015898042,0.0004370208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012835624,0.00019288696,0.0009317731,0.0000010015658,0.0000074514655,0.000015266567,0.000075623524,0.000053404325,0.00007369606,0.98311347,0.014718192,0.0008159797],"study_design_scores_gemma":[0.0006024572,0.00009791553,0.08398783,0.000007655103,0.0000022671957,0.00008214893,0.000004686074,0.008014618,0.00022169267,0.89757943,0.009193721,0.00020558275],"about_ca_topic_score_codex":0.000024706678,"about_ca_topic_score_gemma":4.9563994e-7,"teacher_disagreement_score":0.085534014,"about_ca_system_score_codex":0.00006573668,"about_ca_system_score_gemma":0.00009735019,"threshold_uncertainty_score":0.5617164},"labels":[],"label_agreement":null},{"id":"W12582432","doi":"","title":"Evolution of recurrent neural networks to control Autonomous Life Agents","year":2001,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Waterloo","funders":"","keywords":"Artificial life; Recurrent neural network; Autonomous agent; Computer science; Artificial intelligence; Task (project management); Artificial neural network; Intelligent agent; Multi-agent system; Control (management); Simple (philosophy); Engineering","score_opus":0.02028320403609548,"score_gpt":0.25074937336899034,"score_spread":0.23046616933289485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W12582432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099091284,0.0008442595,0.89734894,0.0017288782,0.00029537964,0.00037220126,0.00001118134,0.00009742607,0.00021042468],"genre_scores_gemma":[0.94765216,0.000080283695,0.051751316,0.00026443537,0.000112449125,0.000059615664,0.000015115705,0.000007139804,0.000057468005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984123,0.000098871504,0.0004488281,0.00047530298,0.0002725514,0.00029213994],"domain_scores_gemma":[0.99883175,0.00009732656,0.00016608533,0.00027386102,0.00035947774,0.00027151915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012624824,0.00017799319,0.00021236425,0.00016013927,0.0002540887,0.00005443156,0.00037814392,0.00007183351,0.00002701711],"category_scores_gemma":[0.00003095244,0.0001898881,0.000054991717,0.0005454213,0.00009499425,0.00023864067,0.00016148352,0.0001090163,0.000021176444],"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":[0.000031632808,0.00020217542,0.012974081,0.00001451934,0.000030207017,0.000004647381,0.00026448158,0.8487435,0.00006931769,0.04113149,0.0026115242,0.09392244],"study_design_scores_gemma":[0.0003235575,0.00011812888,0.32457235,0.000011767546,0.000008471487,0.000034588953,0.000027821641,0.6703406,7.738379e-7,0.0038920706,0.0005358112,0.00013403712],"about_ca_topic_score_codex":0.000066038156,"about_ca_topic_score_gemma":0.00000522945,"teacher_disagreement_score":0.84856087,"about_ca_system_score_codex":0.00009082415,"about_ca_system_score_gemma":0.00019904893,"threshold_uncertainty_score":0.7743412},"labels":[],"label_agreement":null},{"id":"W134905603","doi":"10.1007/978-3-642-37207-0_9","title":"Robustness and Evolvability of Recombination in Linear Genetic Programming","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"","keywords":"Evolvability; Robustness (evolution); Phenotype; Biology; Genetic programming; Recombination; Computer science; Genetics; Evolutionary biology; Computational biology; Artificial intelligence; Gene","score_opus":0.013951434353448783,"score_gpt":0.24116370624880631,"score_spread":0.22721227189535753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W134905603","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029167116,0.0003246991,0.9951596,0.0005434122,0.0002836406,0.00051986653,0.0000011514504,0.000040060513,0.00021080839],"genre_scores_gemma":[0.13922629,0.000039598835,0.86050576,0.000043249078,0.0000649805,0.000029344177,0.0000024083772,0.000009693789,0.00007867267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979673,0.000025773466,0.00044874084,0.0008758806,0.0003974692,0.00028486215],"domain_scores_gemma":[0.9985741,0.00019942639,0.00020542578,0.00068778964,0.00025772201,0.00007553316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051305187,0.00022480472,0.000294755,0.00043081597,0.0000961811,0.0001121481,0.0010956448,0.00017421978,0.000009965806],"category_scores_gemma":[0.000042132895,0.00021549537,0.0000422967,0.00055604224,0.0004988768,0.00041807574,0.00059740635,0.000346863,0.0000032853077],"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":[7.852456e-7,0.000058735957,0.0003284545,0.000048300477,0.0000021314406,0.000003416952,0.00024596814,0.07446441,0.000035000907,0.010516202,0.0000023181274,0.9142943],"study_design_scores_gemma":[0.00013237719,0.000070912305,0.0042485977,0.00013782835,0.0000019739841,0.000015293266,1.6271895e-7,0.9061731,0.00008046228,0.088839464,0.00009041426,0.00020941772],"about_ca_topic_score_codex":0.000056191795,"about_ca_topic_score_gemma":0.000055178487,"teacher_disagreement_score":0.91408485,"about_ca_system_score_codex":0.00014096806,"about_ca_system_score_gemma":0.00021670246,"threshold_uncertainty_score":0.8787646},"labels":[],"label_agreement":null},{"id":"W1480328200","doi":"10.1109/cec.2003.1299599","title":"Evolutionary exploration of dynamic swarm behaviour","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Swarm behaviour; Computer science; Evolutionary computation; Artificial intelligence","score_opus":0.01883730947000606,"score_gpt":0.2590932603582102,"score_spread":0.24025595088820412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480328200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008482179,0.00010899739,0.9833946,0.0006204126,0.00008735469,0.00010151295,0.0000023026748,0.00008500751,0.007117583],"genre_scores_gemma":[0.74637514,0.000015819693,0.25260195,0.0000311433,0.0000052590003,0.00002757448,0.000005070906,0.000002547692,0.0009354799],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99940085,0.000026297404,0.00015895134,0.00017277137,0.00014170508,0.000099443656],"domain_scores_gemma":[0.9994898,0.000022564285,0.00005048538,0.00031157138,0.00008802309,0.00003758493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009289748,0.00005634658,0.000060130915,0.000056373978,0.00008340631,0.000011282132,0.00022971242,0.000028554823,0.000034622357],"category_scores_gemma":[0.000010476364,0.00005377246,0.0000361189,0.00031402946,0.000027507636,0.00067131186,0.00003606428,0.000040859035,0.00004955749],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.651214e-7,0.00012101416,0.0003246556,0.0000017631293,0.000002589846,4.217103e-7,0.000066525405,0.00020036758,0.00037394988,0.99666137,0.0006599939,0.0015870761],"study_design_scores_gemma":[0.0007747136,0.00021885379,0.079176985,0.000020423067,0.000018755813,0.00007060879,0.00041972013,0.3026719,0.008225468,0.59561694,0.012198919,0.00058672007],"about_ca_topic_score_codex":0.000012995386,"about_ca_topic_score_gemma":0.0000033508904,"teacher_disagreement_score":0.737893,"about_ca_system_score_codex":0.000030906966,"about_ca_system_score_gemma":0.000062662104,"threshold_uncertainty_score":0.21927772},"labels":[],"label_agreement":null},{"id":"W1483457136","doi":"10.5772/14688","title":"Evolvable Metaheuristics on Circuit Design","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Metaheuristic; Computer science; Systems engineering; Engineering; Artificial intelligence","score_opus":0.07546975879759946,"score_gpt":0.244431409288355,"score_spread":0.16896165049075554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483457136","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.267693e-8,0.000080628706,0.51066816,0.00002565316,0.00015668942,0.0002086323,0.0000098506425,0.00021157842,0.48863873],"genre_scores_gemma":[0.0030262645,0.000029878716,0.07825757,0.00035434248,0.00021113006,0.00010641888,0.000007514825,0.000057761787,0.91794914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984396,0.000017871944,0.00033556687,0.00061242614,0.00032858766,0.00026591463],"domain_scores_gemma":[0.9980898,0.00014249663,0.00020114606,0.0012386793,0.00020663516,0.000121252364],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020678896,0.000337334,0.00029325928,0.00019482146,0.0001731286,0.00008766026,0.0013775079,0.0002901943,0.00017406407],"category_scores_gemma":[0.000017709674,0.00032589555,0.00016086207,0.00002399071,0.000117554024,0.000069182934,0.00025721075,0.0005194746,0.0015573085],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002007082,0.000014579175,5.13048e-8,0.000006331047,0.000043664837,0.000021347369,0.000053427386,0.000002677715,0.00018531618,0.96849227,0.0039083995,0.027269915],"study_design_scores_gemma":[0.000060038496,0.00012433945,0.0000010128407,0.00007392188,0.00002354873,0.000022434726,5.6108513e-7,0.0005551808,0.0034124667,0.6480166,0.34741792,0.00029200298],"about_ca_topic_score_codex":0.0000140727025,"about_ca_topic_score_gemma":0.0000012171,"teacher_disagreement_score":0.4324106,"about_ca_system_score_codex":0.00014203273,"about_ca_system_score_gemma":0.00022889116,"threshold_uncertainty_score":0.9999193},"labels":[],"label_agreement":null},{"id":"W1484657058","doi":"10.1007/978-3-540-72877-1_16","title":"The Evolution of Artistic Filters","year":2007,"lang":"en","type":"book-chapter","venue":"Natural computing series","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Art; Aesthetics","score_opus":0.013117592724235103,"score_gpt":0.24435328307217408,"score_spread":0.23123569034793898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484657058","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016633203,0.014786818,0.7378487,0.0020678868,0.0030433538,0.0005914783,0.00002842773,0.0005077607,0.24095921],"genre_scores_gemma":[0.34986624,0.00023139891,0.16129668,0.00018130029,0.0012033762,0.0000073730635,0.0000584111,0.000065860506,0.48708934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871093,0.000014360048,0.00038234133,0.0003113128,0.00034738984,0.00023364759],"domain_scores_gemma":[0.99852586,0.00032372292,0.00033514056,0.0005303809,0.00024371181,0.00004117984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002746342,0.00019973291,0.00019532951,0.000085811946,0.00051659957,0.00007391286,0.00084193965,0.00013393753,0.00000415367],"category_scores_gemma":[0.000035222558,0.00015182493,0.00012240169,0.00010713408,0.00026346682,0.00015702586,0.00032634762,0.00036601326,0.000021693671],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003733723,0.0000037074683,0.0000029859727,0.00001269816,0.000019839312,0.0000022287347,0.00004623917,0.000065787506,0.000025425614,0.971278,0.0014830782,0.02705628],"study_design_scores_gemma":[0.00026796822,0.00021942142,0.0023484542,0.00041357081,0.000050493596,0.00021218519,0.00007687244,0.084307216,0.00017116542,0.5109128,0.40010315,0.0009167329],"about_ca_topic_score_codex":0.000017111306,"about_ca_topic_score_gemma":0.00002543352,"teacher_disagreement_score":0.57655203,"about_ca_system_score_codex":0.00016500092,"about_ca_system_score_gemma":0.000108598586,"threshold_uncertainty_score":0.6191241},"labels":[],"label_agreement":null},{"id":"W1486218346","doi":"10.1007/978-3-642-15323-5_7","title":"A New Method to Find Developmental Descriptions for Digital Circuits","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Electronic circuit; Field-programmable gate array; Modular design; Digital electronics; Function (biology); Scalability; Theoretical computer science; Computer engineering; Parallel computing; Computer architecture; Algorithm; Arithmetic; Computer hardware; Mathematics; Programming language; Electrical engineering","score_opus":0.02700422935292478,"score_gpt":0.27897254594089527,"score_spread":0.2519683165879705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486218346","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012505662,0.000055488672,0.9944522,0.0018936753,0.00083990744,0.0007797489,0.000038779148,0.00012999019,0.0017977224],"genre_scores_gemma":[0.0026328603,0.000002043349,0.9932517,0.00096370175,0.000499357,0.000056282803,0.000015070171,0.000024511004,0.0025544846],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99716645,0.000007413617,0.00039857955,0.0013403498,0.0005413533,0.0005458358],"domain_scores_gemma":[0.9981358,0.00040830602,0.00012933413,0.00074068666,0.00023103009,0.00035483725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040555713,0.00037712848,0.0003163034,0.00056011666,0.000468316,0.0007837852,0.0026911455,0.0002529416,0.000022504702],"category_scores_gemma":[0.00007540009,0.00037381888,0.00012512079,0.0006248717,0.00016297272,0.0007163327,0.0009485727,0.0005158115,0.00009483121],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.585209e-7,0.000020710946,0.0000060145385,0.0000064379315,0.000006492056,0.000003645026,0.00041886052,0.00318295,0.0011152264,0.03358719,0.00017397584,0.9614775],"study_design_scores_gemma":[0.00041684872,0.00019881617,0.00042827553,0.00018700905,0.00001050041,0.00025473334,4.3248832e-7,0.26245373,0.0025137379,0.639603,0.09273873,0.0011941948],"about_ca_topic_score_codex":0.000013531267,"about_ca_topic_score_gemma":0.00003923499,"teacher_disagreement_score":0.96028334,"about_ca_system_score_codex":0.0002663061,"about_ca_system_score_gemma":0.0013789919,"threshold_uncertainty_score":0.9998714},"labels":[],"label_agreement":null},{"id":"W1488179309","doi":"10.1109/ahs.2015.7231168","title":"Designing customized microprocessors for fixed-point computation","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Flexibility (engineering); Personalization; Latency (audio); Computation; Word (group theory); Fixed point; Architecture; Word length; Parallel computing; Computer architecture; FLOPS; Computer engineering; Fixed-point arithmetic; Point (geometry); Floating point; Computer hardware; Embedded system; Algorithm","score_opus":0.037433230851078525,"score_gpt":0.28653694125977924,"score_spread":0.24910371040870072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488179309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010406973,0.000051536394,0.99478614,0.0019419161,0.00009720045,0.00026342712,0.0000014290734,0.000198298,0.0016193516],"genre_scores_gemma":[0.1230444,0.0000010751544,0.875945,0.00024134568,0.00005235885,0.00009638038,0.000008650928,0.0000049149917,0.00060591433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937147,0.000016476442,0.00014509061,0.00021328138,0.000113396745,0.00014027595],"domain_scores_gemma":[0.99939555,0.00008903907,0.000052996238,0.00014083541,0.00023286181,0.0000887274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028758467,0.000067661305,0.00008266666,0.00004639804,0.000118002325,0.00007301258,0.0002723297,0.000027955326,0.000002545812],"category_scores_gemma":[0.000034356784,0.000060023933,0.000035473106,0.00020965186,0.000017590513,0.0003216792,0.000059586546,0.000034124765,0.000054659227],"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":[0.00007025562,0.00054286805,0.00023852808,0.00005504032,0.000064672815,0.000003714943,0.0037883655,0.040896837,0.014638458,0.5549185,0.26416978,0.120612934],"study_design_scores_gemma":[0.0012868565,0.00006000215,0.00007455738,0.0000056441345,0.000004695169,0.000011172438,0.00016416716,0.9304944,0.0062049343,0.054381687,0.007154829,0.00015703772],"about_ca_topic_score_codex":0.000014784304,"about_ca_topic_score_gemma":0.0000015072061,"teacher_disagreement_score":0.8895976,"about_ca_system_score_codex":0.00003997166,"about_ca_system_score_gemma":0.000093364855,"threshold_uncertainty_score":0.2447705},"labels":[],"label_agreement":null},{"id":"W1493310923","doi":"10.1007/978-1-4419-7747-2_6","title":"A Survey of Self Modifying Cartesian Genetic Programming","year":2010,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Iterated function; Genetic programming; Cartesian coordinate system; Graph; Scalability; Sequence (biology); Variety (cybernetics); Theoretical computer science; Mathematics; Artificial intelligence; Biology; Genetics","score_opus":0.01964022500149743,"score_gpt":0.2356565986755685,"score_spread":0.2160163736740711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493310923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003337345,0.0058349795,0.98480463,0.00016334582,0.00031492097,0.00070638885,0.00006216964,0.00021449968,0.004561709],"genre_scores_gemma":[0.14131926,0.00043008843,0.85390437,0.000036212274,0.00018682842,0.00005063043,0.00024216887,0.00004597316,0.0037844388],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99812037,0.000049862334,0.00055111625,0.0006396429,0.00038466149,0.0002543647],"domain_scores_gemma":[0.99851155,0.0001446088,0.00036615532,0.00041854437,0.00042018679,0.00013896749],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018444723,0.00030398113,0.0003224995,0.00021710705,0.00031224196,0.000058246384,0.00038908017,0.00030465567,0.000012698061],"category_scores_gemma":[0.000011373546,0.00033944924,0.00008657636,0.00014709702,0.00016296159,0.00012897419,0.00023410663,0.00030681564,0.00002425649],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020552745,0.00042726385,0.0050534564,0.00046887083,0.0004491333,0.00003787151,0.0015251053,0.017653536,0.00028368336,0.11604166,0.0016231906,0.8564157],"study_design_scores_gemma":[0.0003918162,0.00019436287,0.47314432,0.00008696298,0.00007820055,0.00016748374,0.000006398906,0.45192116,0.0000058673786,0.063784175,0.0095750615,0.0006441753],"about_ca_topic_score_codex":0.00013660736,"about_ca_topic_score_gemma":0.000035756126,"teacher_disagreement_score":0.8557715,"about_ca_system_score_codex":0.000059907565,"about_ca_system_score_gemma":0.00029147428,"threshold_uncertainty_score":0.99990577},"labels":[],"label_agreement":null},{"id":"W1493901599","doi":"10.1109/latw.2015.7102506","title":"Optimizing an LDO voltage regulator by evolutionary algorithms considering tolerances of the circuit elements","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Semtech (Canada)","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Evolutionary algorithm; Sorting; Regulator; Voltage regulator; Genetic algorithm; Computer science; Low-dropout regulator; Capacitor; Spice; Chromosome; Voltage; Algorithm; Control theory (sociology); Dropout voltage; Engineering; Electronic engineering; Artificial intelligence; Machine learning; Biology","score_opus":0.03439185068317264,"score_gpt":0.2572711011934019,"score_spread":0.22287925051022928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493901599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017272167,0.00068787613,0.9771052,0.0009130246,0.0003222443,0.00028324223,0.000022787788,0.00015017626,0.0032432873],"genre_scores_gemma":[0.70109284,0.000011034582,0.29725954,0.0002922862,0.00009669096,0.000049992137,0.000009849397,0.000011115668,0.0011766715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986658,0.00004459802,0.0003234551,0.0003340824,0.00040637172,0.00022571102],"domain_scores_gemma":[0.9988482,0.000036527275,0.0001415201,0.00066666614,0.00016865184,0.00013841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002680706,0.000119572134,0.0001333491,0.000038035323,0.00021686894,0.000050457373,0.0008817811,0.00004457348,0.000021007474],"category_scores_gemma":[0.000016208103,0.000092101924,0.0000555496,0.00036166655,0.000111222915,0.0007207771,0.00027447895,0.000088676854,0.000009801297],"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":[0.000011446619,0.0014710542,0.015847692,0.00004988186,0.00016843562,0.000007320032,0.004401665,0.013892422,0.03449311,0.75988287,0.105387636,0.06438647],"study_design_scores_gemma":[0.0009149238,0.00013297344,0.007142017,0.000042461517,0.000014390599,0.00004809502,0.00066773355,0.90627855,0.008505138,0.03909349,0.036704294,0.00045595737],"about_ca_topic_score_codex":0.00008717803,"about_ca_topic_score_gemma":0.0000050983544,"teacher_disagreement_score":0.8923861,"about_ca_system_score_codex":0.00006425835,"about_ca_system_score_gemma":0.00015272705,"threshold_uncertainty_score":0.37558076},"labels":[],"label_agreement":null},{"id":"W1495327238","doi":"10.1109/cec.2015.7257197","title":"Better trade exits for foreign exchange currency trading using FXGP","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Currency; Benchmarking; Foreign exchange market; Profit (economics); Computer science; Fibonacci number; Trading strategy; Volatility (finance); Business; Algorithmic trading; Econometrics; Operations research; Economics; Financial economics; Microeconomics; Monetary economics; Marketing; Mathematics","score_opus":0.12350090877034176,"score_gpt":0.31324263294664495,"score_spread":0.1897417241763032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495327238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003558164,0.0001883576,0.9882477,0.0020933244,0.00016557272,0.0002635468,0.0000065640443,0.00012229243,0.005354485],"genre_scores_gemma":[0.24795637,0.0000033562685,0.7508045,0.0005087888,0.00036264127,0.00009889282,0.000007702141,0.000009799972,0.0002479685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992119,0.000013018641,0.00014532368,0.00025890127,0.00014012532,0.0002307308],"domain_scores_gemma":[0.99950564,0.000041145682,0.000040700404,0.000252249,0.00003658836,0.00012365749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017194643,0.000091102556,0.00008971112,0.000057651436,0.00013945211,0.00006246198,0.00038352393,0.000037204587,0.000013120227],"category_scores_gemma":[0.00000842774,0.000081861675,0.000054903478,0.00022690503,0.000020125184,0.0005026547,0.000058349226,0.000049496048,0.000009587542],"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":[0.0000031575223,0.00021799206,0.0005099904,0.000034847675,0.0000202802,0.0000026519933,0.0014067123,0.00013936758,0.0008852044,0.78779745,0.0662658,0.14271654],"study_design_scores_gemma":[0.00034961684,0.000048392325,0.00023882717,0.000008524903,0.000005631599,0.000019493242,0.00005323621,0.88535506,0.0005018069,0.083992526,0.029261695,0.00016521904],"about_ca_topic_score_codex":0.000010724804,"about_ca_topic_score_gemma":0.0000015461902,"teacher_disagreement_score":0.88521564,"about_ca_system_score_codex":0.00004684433,"about_ca_system_score_gemma":0.000053343174,"threshold_uncertainty_score":0.33382225},"labels":[],"label_agreement":null},{"id":"W1495652757","doi":"10.1109/icosp.2004.1452567","title":"Genetic algorithms for the design of digital filters using canonic signed digit coefficients","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Coding (social sciences); Numerical digit; Computer science; Algorithm; Genetic algorithm; Throughput; Digital filter; Algorithm design; Design methods; Arithmetic; Computer hardware; Mathematics; Filter (signal processing); Engineering; Telecommunications; Computer vision","score_opus":0.04326202874631511,"score_gpt":0.2726552891241682,"score_spread":0.2293932603778531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495652757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018858316,0.000102622434,0.996776,0.00051007903,0.000058497957,0.00045862468,0.000018489856,0.00004709033,0.00014275036],"genre_scores_gemma":[0.52656764,0.000004989586,0.4728212,0.0000887185,0.000067080284,0.000043333665,0.0000020799093,0.000006780978,0.0003981434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919087,0.000010762514,0.0002041339,0.00021900043,0.0001694196,0.00020578763],"domain_scores_gemma":[0.9991513,0.00027563554,0.000069710484,0.00036468217,0.00009070294,0.000048006994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008106073,0.00009048075,0.00008673624,0.000038423284,0.00018899643,0.00011047795,0.0006428909,0.000025369787,0.000009747776],"category_scores_gemma":[0.000013389618,0.00006361801,0.00006177455,0.00026703608,0.00006696712,0.0002924912,0.000104626466,0.000030672843,0.000009468638],"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":[0.0000057920943,0.0002698232,0.00007671015,0.000007006192,0.000046633966,5.5746534e-7,0.00024047744,0.6487096,0.0015909786,0.009569511,0.0016186001,0.3378643],"study_design_scores_gemma":[0.00019937169,0.00004892552,0.00021506299,0.0000034785774,0.000007726345,0.00000820525,0.000018544648,0.9949629,0.0010755392,0.0006695219,0.0026999149,0.000090837195],"about_ca_topic_score_codex":0.00001556904,"about_ca_topic_score_gemma":0.0000012668804,"teacher_disagreement_score":0.5246818,"about_ca_system_score_codex":0.000043232183,"about_ca_system_score_gemma":0.000120501616,"threshold_uncertainty_score":0.2594267},"labels":[],"label_agreement":null},{"id":"W1496791178","doi":"10.1007/978-3-540-25966-4_24","title":"Sharing Training Patterns among Multiple Classifiers","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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 Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Machine learning; Artificial intelligence; Training set; Information sharing; Data mining; Perspective (graphical); World Wide Web","score_opus":0.031691702199515226,"score_gpt":0.24650804907558369,"score_spread":0.21481634687606846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496791178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007677455,0.000105107305,0.9948871,0.00052080647,0.0008823039,0.00034587967,0.000009189895,0.00024133585,0.0022405034],"genre_scores_gemma":[0.66084003,0.000023262472,0.33796442,0.00040583158,0.00043276808,0.00002620212,0.00001033005,0.000032281157,0.00026484206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632955,0.000011105805,0.00048143367,0.0017142018,0.0007593614,0.0007043453],"domain_scores_gemma":[0.99779606,0.0002432904,0.00024851708,0.0013466411,0.00013743117,0.00022803366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004862781,0.0004556396,0.00039102268,0.0005793318,0.00046237666,0.0004979413,0.0035985452,0.0002832739,0.000028165656],"category_scores_gemma":[0.00004218326,0.00045232783,0.00014805449,0.00051815953,0.00058478944,0.0007892289,0.0013011595,0.0008645375,0.00003328155],"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":[0.0000020250582,0.0000575206,0.0032976891,0.000046529563,0.000021696604,0.000120328266,0.0031338343,0.16293505,0.00009309367,0.1546291,0.000008281902,0.6756548],"study_design_scores_gemma":[0.0002799397,0.000055211618,0.005032868,0.00042763745,0.00000515903,0.000039533476,8.076858e-7,0.7535671,0.00014402605,0.23951477,0.00030853727,0.0006244263],"about_ca_topic_score_codex":0.00008325218,"about_ca_topic_score_gemma":0.00015223083,"teacher_disagreement_score":0.6750304,"about_ca_system_score_codex":0.0004906585,"about_ca_system_score_gemma":0.00053804734,"threshold_uncertainty_score":0.9997929},"labels":[],"label_agreement":null},{"id":"W1501199179","doi":"10.1007/978-3-540-31989-4_21","title":"Context-Based Repeated Sequences in Linear Genetic Programming","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Crossover; Computer science; Sequence (biology); Context (archaeology); Representation (politics); Code (set theory); Genetic programming; Phenomenon; Theoretical computer science; Programming language; Artificial intelligence; Genetics; Biology","score_opus":0.017625324479103234,"score_gpt":0.2550294407413274,"score_spread":0.23740411626222416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501199179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020687001,0.0007450969,0.9952147,0.0020558755,0.0005653576,0.00054672634,0.000004049835,0.00018116127,0.00048019012],"genre_scores_gemma":[0.12078488,0.000030442294,0.8774875,0.0009863192,0.00035670734,0.00004628784,0.000008229985,0.000022419825,0.0002772643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657625,0.000034451863,0.00060693297,0.0014545851,0.00071375095,0.0006140561],"domain_scores_gemma":[0.99804497,0.00023943816,0.0002479415,0.0010973407,0.00022235955,0.00014794554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048417816,0.00041330286,0.00039249245,0.0007211018,0.00022310771,0.00029665016,0.0025614465,0.0002609976,0.000018964147],"category_scores_gemma":[0.000039460392,0.0003922771,0.000102462895,0.0009983481,0.0006557196,0.0003945817,0.00047961753,0.00067620573,0.00004129456],"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":[0.000001906196,0.00005022804,0.00015993584,0.000014545873,0.0000032856074,0.00007045011,0.00020938757,0.11403428,0.000048980568,0.007289357,0.000007877106,0.87810975],"study_design_scores_gemma":[0.0002633516,0.00010865636,0.00033328537,0.00028659686,0.0000035465753,0.000045513654,3.174525e-7,0.96520466,0.0002914861,0.027939651,0.005015588,0.0005073396],"about_ca_topic_score_codex":0.00009291685,"about_ca_topic_score_gemma":0.0005054132,"teacher_disagreement_score":0.8776024,"about_ca_system_score_codex":0.00035811547,"about_ca_system_score_gemma":0.00082262256,"threshold_uncertainty_score":0.9998529},"labels":[],"label_agreement":null},{"id":"W1501239440","doi":"10.1007/978-3-642-12239-2_6","title":"Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review","year":2010,"lang":"en","type":"review","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Symbiosis; Mechanism (biology); Mendelian inheritance; Complex adaptive system; Computer science; Operator (biology); Biology; Cognitive science; Evolutionary biology; Computational biology; Artificial intelligence; Epistemology; Genetics; Psychology; Philosophy; Gene","score_opus":0.054019153438797346,"score_gpt":0.3356723315665985,"score_spread":0.2816531781278011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501239440","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.9465616e-8,0.48341113,0.51398194,0.00023293842,0.0006580141,0.0015974094,0.000019046884,0.00009340134,0.000006005775],"genre_scores_gemma":[0.000019989837,0.5352169,0.46327797,0.0005042423,0.0002648604,0.0006853531,0.000009486661,0.000019744608,0.0000014332568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956284,0.00014998473,0.00090943003,0.0018289983,0.0006887433,0.00079441676],"domain_scores_gemma":[0.9959523,0.0010477788,0.0005799739,0.0016987354,0.00049694744,0.0002242285],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013828644,0.0006178555,0.0015683107,0.00056862977,0.0005721807,0.00045893778,0.004909445,0.00031028676,0.0000037117381],"category_scores_gemma":[0.00023283238,0.0005072892,0.00043445674,0.0036097,0.00028200302,0.00047265305,0.0010880879,0.0005954033,0.000037392165],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.8549425e-7,0.00003953492,6.228496e-8,0.0037865567,0.000011434755,0.000005453754,0.000022911296,0.00014677683,0.000008982881,0.08384181,0.000029501247,0.9121067],"study_design_scores_gemma":[0.00017337048,0.00020128097,8.6709457e-7,0.034159042,0.00014448256,0.0006544776,2.455508e-7,0.564541,0.000027594011,0.044686757,0.35427475,0.0011361481],"about_ca_topic_score_codex":0.000053813324,"about_ca_topic_score_gemma":0.000008181866,"teacher_disagreement_score":0.91097057,"about_ca_system_score_codex":0.00030417572,"about_ca_system_score_gemma":0.0012639079,"threshold_uncertainty_score":0.99973786},"labels":[],"label_agreement":null},{"id":"W1506197414","doi":"10.1109/cec.2005.1554749","title":"Boolean Genetic Programming for Promoter Recognition in Eukaryotes","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boolean function; Promoter; Coding (social sciences); Computer science; Genetic programming; Set (abstract data type); Boolean expression; Computational biology; Theoretical computer science; Mathematics; Algorithm; Artificial intelligence; Biology; Genetics; Gene; Programming language; Gene expression","score_opus":0.02311196158695135,"score_gpt":0.2578528916553817,"score_spread":0.2347409300684304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506197414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014170672,0.000055363264,0.9805825,0.0039469316,0.000031906893,0.00047679828,0.0000014114545,0.00010947432,0.0006249845],"genre_scores_gemma":[0.103795566,0.0000047250264,0.8952502,0.00018814167,0.00012809168,0.00029276882,0.0000066706507,0.0000039363968,0.0003299131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945074,0.000008246822,0.00013054213,0.00019868572,0.000059272657,0.00015253804],"domain_scores_gemma":[0.9997346,0.000027657807,0.000022992896,0.00014726736,0.000036197074,0.00003132666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008895144,0.000052271465,0.00004598195,0.00005112215,0.00005980419,0.000047336693,0.00018279736,0.000024468722,0.000012776745],"category_scores_gemma":[0.0000070866754,0.000048366153,0.000025448207,0.0001645566,0.000011225068,0.00025325792,0.000036173144,0.000035434558,0.000053404347],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.8235505e-7,0.00010308516,0.00025415464,0.00000433282,0.0000014892781,2.967406e-7,0.00009886488,0.00005879888,0.00011734076,0.0049149157,0.00040069994,0.9940453],"study_design_scores_gemma":[0.0007533162,0.00016747476,0.028764602,0.000029725625,0.0000047169938,0.000032890868,0.000035861452,0.8257202,0.0022247012,0.042226937,0.099688776,0.0003508316],"about_ca_topic_score_codex":0.000010939622,"about_ca_topic_score_gemma":0.000041449766,"teacher_disagreement_score":0.9936945,"about_ca_system_score_codex":0.000023739787,"about_ca_system_score_gemma":0.000019250348,"threshold_uncertainty_score":0.19723144},"labels":[],"label_agreement":null},{"id":"W1506992350","doi":"","title":"Learning conceptual chess for testing evolutionary programming versus a reasoning-based soft expert system: The KASER","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Expert system; Legal expert system; Knowledge acquisition; Domain (mathematical analysis); Inference; Bottleneck; Machine learning; Genetic programming; Subject-matter expert; Domain knowledge; Knowledge-based systems","score_opus":0.05169874446479046,"score_gpt":0.26896568396851445,"score_spread":0.217266939503724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506992350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00433689,0.00033735237,0.99169,0.0009569064,0.0002705819,0.0005763279,0.000002075733,0.00072579837,0.0011040956],"genre_scores_gemma":[0.56681806,9.587577e-7,0.43166497,0.00009912423,0.00024933234,0.0007093944,0.000011377879,0.0000120875,0.0004346937],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986974,0.00006243747,0.00022505259,0.00039474957,0.0002637986,0.00035655033],"domain_scores_gemma":[0.99819374,0.0009995135,0.000112523805,0.00036015655,0.00025131964,0.000082774306],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00026097114,0.00014571013,0.00012473483,0.00003776589,0.0016486542,0.000064304026,0.00058518996,0.000055972236,0.0000044063904],"category_scores_gemma":[0.00022128611,0.00010931248,0.00008620697,0.00046844815,0.00020403227,0.0002754826,0.00011255125,0.00015414934,0.00001943576],"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":[0.00019961981,0.0009233518,0.0070161107,0.00015098113,0.00020707706,0.000058479072,0.008666035,0.1099901,0.0010522616,0.7358907,0.02763265,0.10821265],"study_design_scores_gemma":[0.00074856007,0.00019454444,0.0007247487,0.000039021386,0.00000637461,0.000038804417,0.0012119731,0.9600896,0.00009094085,0.000033072192,0.036630154,0.00019215434],"about_ca_topic_score_codex":0.00008754442,"about_ca_topic_score_gemma":0.0000032706773,"teacher_disagreement_score":0.85009956,"about_ca_system_score_codex":0.00012356535,"about_ca_system_score_gemma":0.0002483206,"threshold_uncertainty_score":0.9996511},"labels":[],"label_agreement":null},{"id":"W1508212832","doi":"10.1007/978-3-540-24840-8_12","title":"Towards Efficient Training on Large Datasets for Genetic Programming","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Genetic programming; Machine learning; Artificial intelligence; Selection (genetic algorithm); Matching (statistics); Hierarchy; Focus (optics); Overhead (engineering); Set (abstract data type); Process (computing); Training set; Data mining","score_opus":0.0262054816399336,"score_gpt":0.27646971505370405,"score_spread":0.25026423341377046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508212832","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034340523,0.00018786758,0.9959584,0.0014898764,0.00071566436,0.000916597,0.00012105557,0.00015365669,0.00042255572],"genre_scores_gemma":[0.033235595,0.000010938321,0.9651578,0.0009052617,0.00044617977,0.00009513536,0.000070749615,0.000028854214,0.00004947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655086,0.000012401927,0.0004134761,0.0015036968,0.00074686506,0.00077272166],"domain_scores_gemma":[0.99808013,0.00019910146,0.00019821103,0.001197167,0.00014958819,0.00017578319],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006149756,0.00041324025,0.0003530574,0.00044950328,0.00051556947,0.00039474026,0.0025185656,0.0002652579,0.000009743839],"category_scores_gemma":[0.000045434437,0.0003834282,0.00013294433,0.0004766406,0.00030479167,0.00017672259,0.00067551696,0.0005731179,0.000022973496],"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":[0.0000021112542,0.00007411382,7.59426e-7,0.000022895836,0.0000057417565,0.000017299646,0.00059133366,0.119513966,0.000013872548,0.1798567,0.000019841973,0.6998814],"study_design_scores_gemma":[0.0004573411,0.0002498179,0.00011592563,0.00031730699,0.000008326517,0.00004437094,4.115514e-7,0.8331727,0.00014806916,0.14880905,0.016097164,0.00057956495],"about_ca_topic_score_codex":0.0000074249015,"about_ca_topic_score_gemma":0.000015188954,"teacher_disagreement_score":0.7136587,"about_ca_system_score_codex":0.00034974204,"about_ca_system_score_gemma":0.00085159723,"threshold_uncertainty_score":0.9998618},"labels":[],"label_agreement":null},{"id":"W1509316422","doi":"10.1007/springerreference_301754","title":"Evolutionary Learning and Stochastic Process Algebra","year":2012,"lang":"en","type":"reference-entry","venue":"SpringerReference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic programming; Stochastic process; Process (computing); Computer science; Stochastic optimization; Stochastic programming; Process calculus; Theoretical computer science; Algebra over a field; Mathematical optimization; Artificial intelligence; Mathematics; Programming language; Pure mathematics; Statistics","score_opus":0.02201518513033344,"score_gpt":0.27079794469293544,"score_spread":0.248782759562602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509316422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002030604,0.07396884,0.78050476,0.0007994458,0.0024986109,0.0010865254,0.00008103616,0.0013742318,0.13765593],"genre_scores_gemma":[0.8115123,0.03930528,0.052872922,0.00019659588,0.0032856914,0.0010940473,0.00047958634,0.00017382414,0.09107976],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973207,0.00007836902,0.00039072701,0.0009511083,0.0005765614,0.0006825424],"domain_scores_gemma":[0.9983862,0.00017671192,0.00031344005,0.0005971279,0.00020505444,0.000321438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020039186,0.0004627388,0.0004196344,0.00025622087,0.00051563926,0.00024871668,0.0014308636,0.00035588638,0.00010170315],"category_scores_gemma":[0.000086074746,0.0004529524,0.000083029496,0.0005206142,0.0001485729,0.0009165602,0.0010837734,0.0012006722,0.00027747513],"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":[0.000015396148,0.0006321176,0.003449332,0.001213378,0.00023376053,0.000018611025,0.0013836628,0.00045621395,0.000010946186,0.2933558,0.036144767,0.663086],"study_design_scores_gemma":[0.0010685853,0.00032754376,0.053098448,0.002806524,0.00026777416,0.00041148794,0.00026798033,0.080429435,0.00002098147,0.079978816,0.776913,0.0044094273],"about_ca_topic_score_codex":0.000033804434,"about_ca_topic_score_gemma":0.0000034417797,"teacher_disagreement_score":0.8094817,"about_ca_system_score_codex":0.00012984306,"about_ca_system_score_gemma":0.00048036242,"threshold_uncertainty_score":0.9997922},"labels":[],"label_agreement":null},{"id":"W1513009135","doi":"10.1007/978-3-540-71605-1_13","title":"GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Bidding; Genetic programming; Common value auction; Computer science; Decomposition; Genetic algorithm; Population; Classifier (UML); Mathematical optimization; Artificial intelligence; Combinatorial auction; Machine learning; Microeconomics; Economics; Mathematics","score_opus":0.025938851104528166,"score_gpt":0.2750009925150746,"score_spread":0.24906214141054642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1513009135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023145867,0.00047921014,0.99117446,0.00097283314,0.00065289903,0.00046548454,0.000010122931,0.00015969892,0.0058538322],"genre_scores_gemma":[0.018123087,0.000065399516,0.9799063,0.00080931623,0.0005112895,0.000016987093,0.000008683598,0.000030188345,0.0005287502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678826,0.000021255339,0.0005200925,0.0014292862,0.0006266103,0.0006144739],"domain_scores_gemma":[0.99787074,0.00037835835,0.00029668218,0.0009322178,0.00028944915,0.00023254075],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065204466,0.00043520477,0.0003481912,0.00077810546,0.00092703104,0.00046289366,0.0014596131,0.00032868038,0.00004561379],"category_scores_gemma":[0.000016226144,0.00043850506,0.00009284864,0.00089808676,0.000620681,0.00092663843,0.00096242706,0.0007386722,0.000027105334],"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":[0.000014184146,0.0002985008,0.00028743435,0.0002647946,0.0000754433,0.00013375771,0.0024428586,0.14694998,0.0015358365,0.33660755,0.0003085485,0.5110811],"study_design_scores_gemma":[0.00021635462,0.0000845463,0.00073680596,0.00023769194,0.000010268889,0.00042810012,4.56621e-7,0.8440224,0.00024082762,0.14275415,0.010609304,0.0006591249],"about_ca_topic_score_codex":0.000031795633,"about_ca_topic_score_gemma":0.000117408315,"teacher_disagreement_score":0.6970724,"about_ca_system_score_codex":0.00045106548,"about_ca_system_score_gemma":0.00037322674,"threshold_uncertainty_score":0.9998067},"labels":[],"label_agreement":null},{"id":"W1515636825","doi":"10.1007/978-3-540-39432-7_24","title":"On the Dynamics of an Artificial Regulatory Network","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Simple (philosophy); Artificial neural network; Heterochrony; Genetic algorithm; Artificial intelligence; Artificial life; Genetic network; Mutation; Machine learning; Biology; Genetics; Gene","score_opus":0.013833763981386795,"score_gpt":0.2293129863989791,"score_spread":0.2154792224175923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515636825","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002846159,0.000090026835,0.9942371,0.0020422405,0.000766398,0.0002726482,0.0000059209337,0.000050613973,0.0022504088],"genre_scores_gemma":[0.15236332,0.000024322742,0.84070915,0.005091024,0.001104653,0.00003144842,0.000019192934,0.000053075528,0.0006038394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977164,0.0000443629,0.000379047,0.0008213316,0.0006734453,0.0003654048],"domain_scores_gemma":[0.9974132,0.0004718773,0.0002478437,0.0016247863,0.00015560605,0.000086693915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086521945,0.00026904762,0.00025816038,0.00019068414,0.00035931356,0.00014074534,0.002510359,0.00018109461,0.000016495642],"category_scores_gemma":[0.00003168421,0.00020333134,0.00008764539,0.00056660944,0.0007434285,0.00022578398,0.00037282947,0.0005111654,0.00001281557],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011119238,0.00002487305,0.0000020572213,0.000002719679,0.0000028604506,0.0000031390666,0.00005869592,0.10779331,0.000008753225,0.7805993,0.000053677326,0.11144953],"study_design_scores_gemma":[0.000022406812,0.00006742535,0.000046558405,0.00004544757,0.0000018349007,0.000009299545,7.4216565e-8,0.5017374,0.00008937717,0.49762782,0.00021453366,0.0001378238],"about_ca_topic_score_codex":0.0000062660483,"about_ca_topic_score_gemma":0.00005896629,"teacher_disagreement_score":0.39394408,"about_ca_system_score_codex":0.00017193981,"about_ca_system_score_gemma":0.00028074716,"threshold_uncertainty_score":0.8291611},"labels":[],"label_agreement":null},{"id":"W1521598381","doi":"10.1109/cec.2000.870298","title":"Reconstructing the shifting balance theory in a GA: taking Sewall Wright seriously","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wright; Balance (ability); Computer science; Mathematical economics; Mathematics; Psychology; Programming language","score_opus":0.018730675815470978,"score_gpt":0.22468097648385713,"score_spread":0.20595030066838615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521598381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.121131785,0.00086645136,0.74837935,0.010974009,0.0002946865,0.00038951082,0.0000015582323,0.0004244465,0.11753823],"genre_scores_gemma":[0.90823346,0.000024277111,0.090061724,0.0005318363,0.000084339954,0.000032139502,3.1396794e-7,0.0000054634465,0.0010264679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990922,0.000073887706,0.0002055206,0.00027221942,0.00011548137,0.00024067987],"domain_scores_gemma":[0.9992025,0.00025475048,0.000101350444,0.00038471606,0.000025268737,0.000031434258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004514454,0.00008647633,0.0000816507,0.000044011147,0.00030336983,0.00010501287,0.00064155157,0.000030980576,0.00016611395],"category_scores_gemma":[0.000041093575,0.000060116665,0.000032577653,0.00048440098,0.000050716728,0.00035214555,0.00015172895,0.0001754808,0.000077394645],"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":[5.790214e-7,0.000044733788,0.010235194,0.0000046955174,0.000007124273,0.000008707562,0.0024479176,0.00026909143,0.00018688403,0.8465548,0.0003999125,0.13984032],"study_design_scores_gemma":[0.0001757023,0.000010289238,0.0078027523,0.00003188749,0.0000017593514,0.000116727155,0.00032158845,0.9478306,0.00010506107,0.04000423,0.003419328,0.00018008093],"about_ca_topic_score_codex":0.00003998232,"about_ca_topic_score_gemma":0.000026476157,"teacher_disagreement_score":0.9475615,"about_ca_system_score_codex":0.000033873428,"about_ca_system_score_gemma":0.000013588017,"threshold_uncertainty_score":0.24514864},"labels":[],"label_agreement":null},{"id":"W1527227975","doi":"10.1007/978-3-642-12148-7_5","title":"Novelty-Based Fitness: An Evaluation under the Santa Fe Trail","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Novelty; Computer science; Artificial intelligence; Psychology","score_opus":0.03461303463023035,"score_gpt":0.28894693843921615,"score_spread":0.2543339038089858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527227975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003269599,0.00020899525,0.992474,0.0041195625,0.0011365998,0.0006423694,0.000011144557,0.000133552,0.00094677287],"genre_scores_gemma":[0.2720101,0.000016501113,0.7213228,0.005176082,0.0010912705,0.00010452673,0.000048488728,0.00004479137,0.00018546658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625236,0.000062982035,0.00041191137,0.0013534416,0.0014430498,0.0004762324],"domain_scores_gemma":[0.9965544,0.00047620657,0.00025807586,0.0020868285,0.00047251474,0.00015197026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017816445,0.00040805343,0.00027890372,0.0003580325,0.0007744157,0.0005687305,0.004078169,0.00032726716,0.0000622834],"category_scores_gemma":[0.000038291317,0.00030344454,0.00011359173,0.0006678208,0.0009646817,0.00066186406,0.00048046073,0.0010244487,0.00004438173],"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":[0.0000037006612,0.00013205367,0.000016970238,0.000014030809,0.000010625645,0.0000066837983,0.0005225622,0.13368222,0.0013734646,0.09814116,0.000042712814,0.7660538],"study_design_scores_gemma":[0.00021399207,0.0000695677,0.0006555868,0.000050466664,0.000011093231,0.000025986565,3.2639267e-7,0.7836522,0.00066867156,0.21237302,0.0019248028,0.00035427522],"about_ca_topic_score_codex":0.000035266166,"about_ca_topic_score_gemma":0.00028265265,"teacher_disagreement_score":0.7656995,"about_ca_system_score_codex":0.00022779408,"about_ca_system_score_gemma":0.0014311767,"threshold_uncertainty_score":0.99994177},"labels":[],"label_agreement":null},{"id":"W1528288700","doi":"10.1007/978-3-540-24855-2_133","title":"Multiple Species Weighted Voting – A Genetics-Based Machine Learning System","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Voting; Classifier (UML); Machine learning; Majority rule; Pruning; Weighted voting; Population; Class (philosophy); Pattern recognition (psychology); Biology","score_opus":0.013872420682726427,"score_gpt":0.21942773845483557,"score_spread":0.20555531777210914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528288700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000098005905,0.00080605,0.9955174,0.0006039181,0.00066396425,0.00041399268,0.000011326859,0.00042249737,0.0014629014],"genre_scores_gemma":[0.43779793,0.000014241589,0.5612987,0.0002097925,0.00036567976,0.000016209737,0.000017715896,0.000030150044,0.00024953438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964893,0.00003682833,0.00053859473,0.0014273886,0.0008895164,0.0006183872],"domain_scores_gemma":[0.9978258,0.00044314365,0.0003440389,0.0009454586,0.00026477737,0.00017679772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005301248,0.000496308,0.0004419954,0.00063204335,0.00069700956,0.00044854474,0.002537288,0.0002430811,0.00001775329],"category_scores_gemma":[0.000042023457,0.0004686135,0.00014899693,0.00073380186,0.00047840772,0.00027395829,0.0008351684,0.0008905502,0.00005768943],"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":[0.0000040424957,0.000062473475,0.0005709056,0.00012272663,0.000015599666,0.000097979704,0.00034478298,0.7093677,0.00034490382,0.14815754,0.000005294307,0.14090605],"study_design_scores_gemma":[0.0003521542,0.00009203978,0.0002545614,0.00045269768,0.0000067674855,0.000045358818,2.218871e-7,0.98585516,0.0009223134,0.009847143,0.0016522026,0.00051936996],"about_ca_topic_score_codex":0.000048932772,"about_ca_topic_score_gemma":0.00006367575,"teacher_disagreement_score":0.4376999,"about_ca_system_score_codex":0.00068269816,"about_ca_system_score_gemma":0.0006793426,"threshold_uncertainty_score":0.99977654},"labels":[],"label_agreement":null},{"id":"W1528603915","doi":"10.1007/11844297_76","title":"Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP): A New Developmental Approach","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Computer science; Probabilistic logic; Genetic programming; Artificial intelligence","score_opus":0.024729963209635122,"score_gpt":0.21980290430136165,"score_spread":0.19507294109172651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528603915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008896692,0.00038319957,0.9912217,0.00020058361,0.0003403158,0.0011563589,0.000005309281,0.00025273822,0.0063508004],"genre_scores_gemma":[0.014693953,0.000006868767,0.9835643,0.0002436992,0.0004081768,0.00008223048,0.000025503716,0.000037644677,0.000937618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99528897,0.000025821673,0.00073022646,0.002020654,0.0010508246,0.0008835346],"domain_scores_gemma":[0.9984669,0.00015558087,0.00029901238,0.00062221487,0.0001881407,0.00026812876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000394387,0.00066589384,0.00048232105,0.0006613096,0.0005869258,0.0005340971,0.0028279095,0.00027727848,0.00001332006],"category_scores_gemma":[0.000024545274,0.00065518945,0.00012356708,0.0011031779,0.0006243828,0.00050217554,0.0015245698,0.00068461266,0.00005818104],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029246028,0.000125441,0.00006768966,0.00003823237,0.000023777526,0.000048980073,0.0011543814,0.036854833,0.000052899617,0.016740615,0.0002045748,0.94468564],"study_design_scores_gemma":[0.00052910764,0.00014456606,0.0022547415,0.0003704787,0.00001427203,0.0007865428,0.0000030696003,0.8757426,0.00010947134,0.10524211,0.013049479,0.0017535334],"about_ca_topic_score_codex":0.000083744744,"about_ca_topic_score_gemma":0.00004743192,"teacher_disagreement_score":0.9429321,"about_ca_system_score_codex":0.0011282332,"about_ca_system_score_gemma":0.0025643408,"threshold_uncertainty_score":0.9995899},"labels":[],"label_agreement":null},{"id":"W1529894968","doi":"10.1109/cec.2015.7257022","title":"Flow of control in linear genetic programming","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Alternator; Computer science; Control flow; Flow (mathematics); Genetic programming; Flow control (data); Domain (mathematical analysis); Task (project management); Linear programming; String (physics); Control engineering; Artificial intelligence; Algorithm; Programming language; Mathematics; Engineering","score_opus":0.018494501965047337,"score_gpt":0.2539878208490026,"score_spread":0.23549331888395528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529894968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053630685,0.00008520495,0.99298954,0.0006394458,0.00003925161,0.00011042733,4.947565e-7,0.00003268713,0.0007398536],"genre_scores_gemma":[0.43655685,9.797609e-7,0.5632639,0.000046636273,0.00001695583,0.000017439952,3.285049e-7,0.0000010738378,0.000095863004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996153,0.000011640159,0.00010993829,0.00009620634,0.00008617214,0.00008072069],"domain_scores_gemma":[0.9997012,0.000016375805,0.000020467742,0.00016589799,0.000053159332,0.00004292243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009996609,0.000030019954,0.000053490883,0.00003149293,0.000010599713,0.000007515437,0.00020945769,0.000015018804,0.0000026359407],"category_scores_gemma":[0.000008929438,0.000025819625,0.000013585001,0.00020822654,0.000015302263,0.000075462274,0.000035583715,0.000026974883,0.000016665856],"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":[0.000008345705,0.00088542013,0.031672772,0.000019309839,0.000019641448,0.000014247942,0.0015024323,0.067853674,0.0006707825,0.20289622,0.0029836395,0.69147354],"study_design_scores_gemma":[0.00032489625,0.00003396695,0.0050010397,0.0000023117295,6.7163717e-7,0.0000030372998,0.000017321496,0.9877085,0.000052058138,0.0026063824,0.004212998,0.000036801677],"about_ca_topic_score_codex":0.000050140203,"about_ca_topic_score_gemma":0.000011095856,"teacher_disagreement_score":0.9198548,"about_ca_system_score_codex":0.000010288928,"about_ca_system_score_gemma":0.000047972706,"threshold_uncertainty_score":0.10528938},"labels":[],"label_agreement":null},{"id":"W1531850458","doi":"10.1109/cec.1999.781916","title":"Rule acquisition with a genetic algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Crossover; Computer science; Generalization; Genetic algorithm; Genetic representation; Association rule learning; Artificial intelligence; Population-based incremental learning; Algorithm; Knowledge acquisition; Data mining; Hierarchy; Variable (mathematics); Quality control and genetic algorithms; Machine learning; Theoretical computer science; Mathematics; Meta-optimization","score_opus":0.006259371295358735,"score_gpt":0.2094776923583368,"score_spread":0.20321832106297807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531850458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015930934,0.00007719036,0.9855138,0.00046522883,0.00003094232,0.000084522406,8.90953e-7,0.00012534569,0.012108974],"genre_scores_gemma":[0.03299134,0.0000071998625,0.9654605,0.00027296811,0.000023573519,0.000040534673,0.0000015775694,0.000003824434,0.0011985085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944746,0.000017843267,0.0000745053,0.00020298357,0.00012363803,0.00013358743],"domain_scores_gemma":[0.99956745,0.000013972226,0.000020847105,0.00029294268,0.00004812927,0.00005664818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004952778,0.000061099396,0.0000464234,0.000030786356,0.00011794525,0.000048814705,0.00019329986,0.00001944568,0.00008166146],"category_scores_gemma":[0.000001287909,0.000047256406,0.000016825301,0.000280629,0.000021389322,0.00018349872,0.00002284588,0.000034813635,0.00015100389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.773537e-7,0.00021381803,0.00044799398,0.0000030321632,0.000015751222,0.000014084154,0.00010434427,0.00048672812,0.00029690814,0.8690372,0.0024762969,0.12690315],"study_design_scores_gemma":[0.0015809761,0.0004268496,0.0753497,0.000021319029,0.000020216004,0.00091061374,0.00011801304,0.6123176,0.007212035,0.17518446,0.12588422,0.0009739958],"about_ca_topic_score_codex":0.000008908403,"about_ca_topic_score_gemma":7.3970915e-7,"teacher_disagreement_score":0.6938528,"about_ca_system_score_codex":0.000015977676,"about_ca_system_score_gemma":0.000042431395,"threshold_uncertainty_score":0.19409},"labels":[],"label_agreement":null},{"id":"W1533373774","doi":"10.1007/978-3-540-24855-2_119","title":"CellNet Co-Ev: Evolving Better Pattern Recognizers Using Competitive Co-evolution","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.01845423380202252,"score_gpt":0.2564964090425729,"score_spread":0.2380421752405504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533373774","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022356265,0.0003521653,0.99147534,0.00093327346,0.0010119985,0.0005795106,0.00004583757,0.00021826253,0.005160049],"genre_scores_gemma":[0.36569577,0.000044244294,0.6304979,0.0022303404,0.0011659781,0.000027091268,0.00006723441,0.00007712221,0.0001943653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953126,0.000056811677,0.0006802865,0.0019326921,0.0011296725,0.00088795775],"domain_scores_gemma":[0.99725324,0.0003639478,0.00046009105,0.0013039634,0.00037292327,0.0002458558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006808939,0.00067042594,0.0005689495,0.0008499719,0.00074958656,0.000551602,0.0026608382,0.00039661748,0.00011081744],"category_scores_gemma":[0.00003161331,0.000690197,0.00019818963,0.0006947201,0.0009790742,0.0010142414,0.00057636946,0.0010618647,0.00016989827],"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":[0.00001065411,0.00029752826,0.0015464732,0.00017843115,0.00009629627,0.0003113078,0.0023711775,0.14344618,0.0027920888,0.0960974,0.00033349768,0.75251895],"study_design_scores_gemma":[0.0006001872,0.0001627624,0.0011437727,0.00084887195,0.000022988606,0.00018210888,0.0000014106053,0.79002357,0.0016401653,0.20238385,0.0016000465,0.0013902978],"about_ca_topic_score_codex":0.00014856466,"about_ca_topic_score_gemma":0.000043058975,"teacher_disagreement_score":0.7511287,"about_ca_system_score_codex":0.0015602541,"about_ca_system_score_gemma":0.001058756,"threshold_uncertainty_score":0.99955493},"labels":[],"label_agreement":null},{"id":"W1533873536","doi":"10.5772/36817","title":"New Approaches to Designing Genes by Evolution in the Computer","year":2012,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Computational biology; Computer science; Biology; Evolutionary biology; Theoretical computer science","score_opus":0.07683591453166555,"score_gpt":0.2369365098042942,"score_spread":0.16010059527262865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533873536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000039057886,0.0010445752,0.9163825,0.0010274627,0.00014180726,0.0004997972,0.000006074634,0.000105540756,0.08078837],"genre_scores_gemma":[0.02893545,0.00002303149,0.7534075,0.0012033432,0.0016497159,0.00032001888,0.00003387081,0.00007502675,0.21435206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998655,0.000031429692,0.00028812455,0.00044266903,0.0002960484,0.00028670335],"domain_scores_gemma":[0.99898124,0.00009489567,0.00010668703,0.00068155304,0.000027285923,0.000108367865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031336682,0.000257662,0.0001933669,0.00016135008,0.00013328914,0.000113384696,0.0013262385,0.00019783006,0.000011903744],"category_scores_gemma":[0.0000021025269,0.00020649389,0.0000909021,0.000051451672,0.000044863948,0.00011188629,0.00032515128,0.00036974042,0.00019330648],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014761342,0.000016044381,0.00000488171,0.00000635187,0.00001487472,0.0000022549088,0.0006329775,0.000011967137,0.00064232777,0.7185076,0.0067927707,0.2733665],"study_design_scores_gemma":[0.00020467618,0.0001288189,0.00010234111,0.00018578599,0.000025588408,0.000087749184,0.000027543318,0.0033895883,0.0024871246,0.2349322,0.75765085,0.00077771896],"about_ca_topic_score_codex":0.000072129296,"about_ca_topic_score_gemma":0.000017170803,"teacher_disagreement_score":0.75085807,"about_ca_system_score_codex":0.00013373591,"about_ca_system_score_gemma":0.00010695473,"threshold_uncertainty_score":0.84205765},"labels":[],"label_agreement":null},{"id":"W1534539971","doi":"10.1007/978-3-642-01181-8_1","title":"One-Class Genetic Programming","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Novelty detection; Class (philosophy); Computer science; Genetic programming; Artificial intelligence; One-class classification; Fitness function; Novelty; Machine learning; Scalability; Function (biology); Construct (python library); Genetic algorithm; Pattern recognition (psychology); Data mining; Support vector machine; Programming language; Database","score_opus":0.01691058351238581,"score_gpt":0.24296360388241453,"score_spread":0.22605302037002872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534539971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017781224,0.0005646866,0.99128824,0.0023836638,0.00045064138,0.00044583698,0.0000021920318,0.00023384804,0.004613087],"genre_scores_gemma":[0.012994084,0.000055860404,0.984558,0.0010320299,0.0005463358,0.000022057782,0.000004568851,0.000021538106,0.0007655125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662036,0.000017326609,0.00044038508,0.0014224726,0.00086513883,0.00063431397],"domain_scores_gemma":[0.9978398,0.00013841089,0.00021053776,0.0014005984,0.00022267835,0.00018796709],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035162785,0.0003849854,0.000355513,0.0005028183,0.00034989085,0.000530902,0.0032003075,0.00024821685,0.00001483468],"category_scores_gemma":[0.00001993818,0.00040157276,0.00011860979,0.0006917557,0.0004679027,0.0004295866,0.000791096,0.0006302917,0.00008926681],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.169669e-7,0.000039675313,0.0000057513093,0.000008191987,0.0000040285677,0.000018625322,0.00012755422,0.007151819,0.00003731435,0.037652653,0.000014008542,0.95493984],"study_design_scores_gemma":[0.00016282513,0.00017520667,0.0006954516,0.0002088743,0.000008087817,0.00007837483,5.661279e-8,0.56100357,0.00017432764,0.41850686,0.018302364,0.0006840273],"about_ca_topic_score_codex":0.000010450689,"about_ca_topic_score_gemma":0.00002541779,"teacher_disagreement_score":0.9542558,"about_ca_system_score_codex":0.0002548987,"about_ca_system_score_gemma":0.0004692801,"threshold_uncertainty_score":0.9998436},"labels":[],"label_agreement":null},{"id":"W1541677343","doi":"10.1007/3-540-45984-7_18","title":"An Analysis of Koza’s Computational Effort Statistic for Genetic Programming","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":135,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic programming; Computer science; Statistic; Mathematics; Statistics; Artificial intelligence","score_opus":0.01695518108309615,"score_gpt":0.2714305306652515,"score_spread":0.2544753495821554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541677343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011088669,0.00012320653,0.99855185,0.0002027888,0.00020409805,0.0005680175,0.00006314381,0.00007566473,0.00010032552],"genre_scores_gemma":[0.07869748,0.000009360954,0.9208274,0.00013104665,0.00011417741,0.00004445072,0.00007214943,0.00001647401,0.000087472814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971181,0.00001539432,0.000595566,0.0011342489,0.00072987145,0.00040677693],"domain_scores_gemma":[0.99768984,0.00040311296,0.00035960152,0.00091797125,0.0004840963,0.00014539652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038194167,0.00031419328,0.0005134848,0.0011305676,0.00024894203,0.00022350982,0.0019282765,0.00014970468,0.00002265291],"category_scores_gemma":[0.000018971588,0.00028205445,0.00018983128,0.0012237638,0.00047732092,0.0003197029,0.00024776603,0.00021845372,0.000004371911],"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":[0.0000011496509,0.000060610997,0.0001421824,0.000020206842,0.00005250684,0.000003901912,0.00022441143,0.58483416,0.0000059882022,0.042927697,0.0000053637696,0.37172183],"study_design_scores_gemma":[0.00012914532,0.00017639334,0.003808701,0.00003841691,0.00008871368,0.000009404724,1.4392951e-7,0.88931596,0.000013299585,0.10576044,0.00036469058,0.00029469383],"about_ca_topic_score_codex":0.000021361675,"about_ca_topic_score_gemma":0.000042720636,"teacher_disagreement_score":0.37142715,"about_ca_system_score_codex":0.00015821426,"about_ca_system_score_gemma":0.00028138512,"threshold_uncertainty_score":0.99996316},"labels":[],"label_agreement":null},{"id":"W1541688028","doi":"10.1007/978-1-4419-1626-6_3","title":"Evolving Coevolutionary Classifiers Under Large Attribute Spaces","year":2009,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Evolutionary biology; Biology","score_opus":0.018683929692077546,"score_gpt":0.23739483029965763,"score_spread":0.21871090060758008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541688028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012730955,0.018383056,0.9334397,0.003540352,0.00045602824,0.0005733794,0.00012042664,0.00041940997,0.042940374],"genre_scores_gemma":[0.14709328,0.005145748,0.5258928,0.0023661419,0.0020280017,0.00013677735,0.0022444015,0.00019363646,0.31489924],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99699044,0.00005633588,0.00061617745,0.0010748233,0.00072207814,0.000540139],"domain_scores_gemma":[0.99828124,0.00015793847,0.00037744653,0.00052954425,0.0003825617,0.00027128565],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021931554,0.0005364725,0.00042325648,0.00035771652,0.000909655,0.00019935622,0.00058021746,0.00045274044,0.00008565951],"category_scores_gemma":[0.00001120346,0.0006008471,0.00018418605,0.00019441302,0.00021044811,0.0004993273,0.000397972,0.00044936032,0.00021259744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011007407,0.0001629256,0.00022076278,0.000053926666,0.00021270166,0.000034098888,0.00015480346,0.016824866,0.000024134852,0.8464618,0.09174815,0.044090852],"study_design_scores_gemma":[0.0005898994,0.00018747034,0.057530116,0.00012340183,0.00009951855,0.00027068797,0.00004279134,0.35941792,7.068772e-7,0.48095492,0.09990401,0.0008785535],"about_ca_topic_score_codex":0.000012868722,"about_ca_topic_score_gemma":0.0000054947764,"teacher_disagreement_score":0.40754688,"about_ca_system_score_codex":0.00034953296,"about_ca_system_score_gemma":0.00038159857,"threshold_uncertainty_score":0.9996443},"labels":[],"label_agreement":null},{"id":"W1547470232","doi":"10.1007/978-3-642-01181-8_8","title":"The Role of Population Size in Rate of Evolution in Genetic Programming","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Evolutionary computation; Population; Population size; Rate of evolution; Evolutionary programming; Computer science; Genetic representation; Evolutionary algorithm; Genetic algorithm; Artificial intelligence; Machine learning; Biology; Genetics; Demography","score_opus":0.0068051540346034545,"score_gpt":0.22680964543433252,"score_spread":0.22000449139972905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547470232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032181637,0.0011240289,0.9943811,0.00038540212,0.0001479672,0.00046576216,0.0000014404329,0.000021960264,0.00025414565],"genre_scores_gemma":[0.72065103,0.00003856503,0.27918163,0.000029657862,0.000050633527,0.000011779463,0.0000016375573,0.0000060487387,0.000029009063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813366,0.000044690732,0.00059144717,0.00055808364,0.00036997025,0.00030212803],"domain_scores_gemma":[0.9984617,0.00039780096,0.00032725107,0.00063965545,0.00013699938,0.000036548165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076138345,0.00018638275,0.00026072864,0.0003709284,0.00010228275,0.00006688019,0.0013305348,0.00013524992,0.0000010778989],"category_scores_gemma":[0.00006885283,0.00015608667,0.00005765741,0.00091628986,0.00027816294,0.0002454254,0.0002794923,0.00030623795,0.0000011827995],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003005657,0.00004033143,0.00127225,0.000009612371,0.0000014439098,0.0000022555619,0.0002457544,0.07085001,0.00035156327,0.049824137,3.0231976e-7,0.8773993],"study_design_scores_gemma":[0.000111252855,0.000064882275,0.067782536,0.00016395583,0.0000015081251,0.000005436357,4.313836e-7,0.49932715,0.0002123592,0.43205473,0.00013701756,0.00013874964],"about_ca_topic_score_codex":0.0002651213,"about_ca_topic_score_gemma":0.0005722908,"teacher_disagreement_score":0.87726057,"about_ca_system_score_codex":0.00022763312,"about_ca_system_score_gemma":0.00020235869,"threshold_uncertainty_score":0.636503},"labels":[],"label_agreement":null},{"id":"W1548616591","doi":"10.1109/icsmc.1999.825348","title":"Approaching evolutionary robotics through population-based incremental learning","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Evolutionary robotics; Computer science; Task (project management); Evolutionary algorithm; Competitive learning; Artificial neural network; Genetic algorithm; Population; Robotics; Evolutionary computation; Machine learning; Robot; Engineering","score_opus":0.021770418615981398,"score_gpt":0.25169934184081605,"score_spread":0.22992892322483466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548616591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030866894,0.000078933175,0.9818568,0.00066839927,0.000089916044,0.00013053857,6.99241e-7,0.0002626677,0.013825332],"genre_scores_gemma":[0.5078217,0.0000015630669,0.4916744,0.00015713587,0.000021163016,0.0000151612885,0.000019716197,0.0000045755905,0.00028460452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998974,0.00009064933,0.00019017704,0.00029660016,0.00023719892,0.00021132299],"domain_scores_gemma":[0.999485,0.00008301383,0.000060375445,0.00027516778,0.000042883832,0.000053513533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017580776,0.000105142695,0.000083190134,0.0000462713,0.0005307684,0.000062808795,0.0002796522,0.000043210595,0.000048328293],"category_scores_gemma":[0.000036951387,0.00010317917,0.000052564756,0.0003648229,0.000019504814,0.0005145527,0.00005651161,0.000156455,0.000054488206],"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":[3.4203356e-7,0.000085614,0.015343221,0.0000029016044,0.0000048106167,7.0746233e-7,0.000047920686,0.16807063,0.00005260425,0.81571543,0.0002095423,0.00046626574],"study_design_scores_gemma":[0.00026570132,0.000032376673,0.021109035,0.0000068819004,0.000004661198,0.000017849852,0.000058753056,0.9463723,0.00012531865,0.02565819,0.0061160885,0.00023288853],"about_ca_topic_score_codex":0.00012194206,"about_ca_topic_score_gemma":0.0000030171807,"teacher_disagreement_score":0.79005724,"about_ca_system_score_codex":0.00010154933,"about_ca_system_score_gemma":0.000075397504,"threshold_uncertainty_score":0.42075244},"labels":[],"label_agreement":null},{"id":"W1549140495","doi":"10.1007/978-3-540-87700-4_23","title":"Dynamic Cooperative Coevolutionary Sensor Deployment Via Localized Fitness Evaluation","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Software deployment; Coevolution; Evolutionary computation; Computation; Evolutionary algorithm; Distributed computing; Range (aeronautics); Wireless sensor network; Artificial intelligence; Algorithm; Computer network; Ecology; Engineering","score_opus":0.018647123594120355,"score_gpt":0.2710565834751944,"score_spread":0.25240945988107405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549140495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010253446,0.001447936,0.99371445,0.0014782075,0.00094782823,0.0011120131,0.000017591236,0.00020796587,0.0009714804],"genre_scores_gemma":[0.12481952,0.00041523538,0.8716527,0.0014004575,0.0003702605,0.00021215924,0.000110289475,0.000065305496,0.0009540406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952867,0.0000925627,0.00062326464,0.0017215608,0.001688347,0.0005875368],"domain_scores_gemma":[0.9969278,0.0003535084,0.0003026778,0.0013366604,0.0008862346,0.00019310549],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008029921,0.0005492238,0.00048214057,0.0005889818,0.0007349138,0.00018449893,0.0020766263,0.00030397638,0.00006149103],"category_scores_gemma":[0.000053493863,0.0005239428,0.00013975492,0.00085812306,0.0009776069,0.0006497219,0.0008611261,0.0006429884,0.00012711344],"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":[0.000009888441,0.00015798271,0.000028865245,0.000021890759,0.00003314741,0.000069712274,0.00068400736,0.45639926,0.0002527206,0.009146867,0.00022873643,0.5329669],"study_design_scores_gemma":[0.00045233677,0.00011653889,0.0003783406,0.00012304077,0.000013835508,0.00024428585,2.2100981e-7,0.9567673,0.00013926506,0.038998965,0.002177443,0.0005883911],"about_ca_topic_score_codex":0.000030461264,"about_ca_topic_score_gemma":0.000056098837,"teacher_disagreement_score":0.5323785,"about_ca_system_score_codex":0.0011512849,"about_ca_system_score_gemma":0.0013075265,"threshold_uncertainty_score":0.9997212},"labels":[],"label_agreement":null},{"id":"W1553241761","doi":"10.1007/978-0-387-30440-3_373","title":"Numerical Bifurcation Analysis","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Complexity and Systems Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Ode; Bifurcation; Parameter space; Bifurcation theory; Nonlinear system; Qualitative analysis; Computer science; Ordinary differential equation; Dynamical systems theory; Software; Applied mathematics; Bifurcation diagram; Numerical analysis; Differential equation; Mathematics; Theoretical computer science; Mathematical analysis; Qualitative research; Physics; Programming language","score_opus":0.030961590261434867,"score_gpt":0.26155499623646294,"score_spread":0.2305934059750281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553241761","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010988883,0.0012540177,0.28901225,0.0004460354,0.00026168604,0.00031695294,0.000023617627,0.000090366375,0.7084852],"genre_scores_gemma":[0.40057087,0.0034290156,0.14163332,0.00026595415,0.00091594795,0.0000805035,0.000113381306,0.000045014564,0.452946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976825,0.000025281688,0.0005421984,0.0007075679,0.00079726917,0.0002451483],"domain_scores_gemma":[0.9981233,0.00009301292,0.0004355733,0.0008369207,0.00032339164,0.00018782805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006316321,0.0002227818,0.0005044807,0.0005662851,0.0003756262,0.00013787475,0.0012445563,0.000114185044,0.00001778956],"category_scores_gemma":[0.000023999763,0.00020866029,0.00013252799,0.0008615244,0.0007565032,0.0004774923,0.00019453962,0.00018360345,0.000023597548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.323617e-7,0.000021531305,0.00004333157,0.000016983642,0.000027787808,0.0000011310436,0.00011816464,0.00019673258,0.0000074715276,0.99261814,0.00017218795,0.00677589],"study_design_scores_gemma":[0.00021216154,0.0002235622,0.023573996,0.00014444806,0.00020141949,0.000056789253,0.000025625981,0.36966166,0.0000119735505,0.29960868,0.30533174,0.0009479454],"about_ca_topic_score_codex":0.00011032684,"about_ca_topic_score_gemma":0.000005984706,"teacher_disagreement_score":0.6930095,"about_ca_system_score_codex":0.00006556273,"about_ca_system_score_gemma":0.0002731873,"threshold_uncertainty_score":0.85089195},"labels":[],"label_agreement":null},{"id":"W1553628792","doi":"","title":"Open BEAGLE: A New Versatile C++ Framework for Evolutionary Computation.","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Beagle; Symbolic regression; Computer science; Genetic programming; Computation; Simple (philosophy); Evolutionary computation; Programming language; Theoretical computer science; Evolutionary programming; Genetic algorithm; Artificial intelligence; Machine learning; Biology","score_opus":0.052142576250536206,"score_gpt":0.30393285971668194,"score_spread":0.25179028346614574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553628792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000031595024,0.000217164,0.97448164,0.014636279,0.00016798177,0.000551502,0.000009656888,0.0001752297,0.009728954],"genre_scores_gemma":[0.020580318,0.000012459786,0.9706594,0.0010116845,0.00017195736,0.00008967585,0.000014773454,0.0000076204374,0.0074521154],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990865,0.000015127169,0.00018466728,0.00035871303,0.0001512722,0.00020369775],"domain_scores_gemma":[0.99907637,0.00027269268,0.00006120452,0.00036903314,0.0000898214,0.00013089803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078241064,0.00009630286,0.000105246974,0.000048511654,0.0003312397,0.00020240223,0.0012359873,0.000059042355,0.00038298042],"category_scores_gemma":[0.0000408148,0.00009434445,0.00005552663,0.00041669136,0.000026723377,0.0007356678,0.00039343076,0.0000762649,0.0003145143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.0643636e-7,0.000082683306,0.000024757823,0.0000017527974,0.000007797925,3.0335977e-7,0.00012239005,0.0006859358,0.000003971154,0.6924919,0.28167164,0.024905961],"study_design_scores_gemma":[0.00027581624,0.000048211004,0.0007233725,0.0000070559495,0.0000030562292,0.0000066831503,0.000015567728,0.5553364,0.000013497431,0.34961605,0.09383639,0.000117920405],"about_ca_topic_score_codex":0.00006956427,"about_ca_topic_score_gemma":0.0000025815114,"teacher_disagreement_score":0.5546504,"about_ca_system_score_codex":0.000049689064,"about_ca_system_score_gemma":0.00006857038,"threshold_uncertainty_score":0.4193367},"labels":[],"label_agreement":null},{"id":"W1553704381","doi":"10.1007/3-540-45110-2_33","title":"The Master-Slave Architecture for Evolutionary Computations Revisited","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"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 Laval","funders":"","keywords":"Computer science; Workstation; Exploit; Computation; Architecture; Parallel computing; Distributed computing; Evolutionary computation; Idle; Operating system; Artificial intelligence; Algorithm; Computer security","score_opus":0.01817391371438399,"score_gpt":0.24979223902858258,"score_spread":0.2316183253141986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553704381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000029999592,0.0018063465,0.9842749,0.010094017,0.0008814951,0.0011008867,0.00004182671,0.00014591218,0.0016515757],"genre_scores_gemma":[0.0023132302,0.000117725984,0.9926786,0.0022676694,0.00057833776,0.00014143926,0.000045285735,0.000044100212,0.0018136523],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965335,0.0000511314,0.00060452434,0.0013725148,0.00073717575,0.00070114963],"domain_scores_gemma":[0.9959771,0.0015203266,0.00032702164,0.0015168693,0.00049037277,0.00016829603],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00073024567,0.00048549162,0.00037771804,0.00043394289,0.001539243,0.0005438857,0.003010825,0.0002475864,0.0000073362557],"category_scores_gemma":[0.000098378914,0.0003766654,0.00022379302,0.0007940324,0.0007454149,0.00031830705,0.00065557007,0.0006904046,0.000029759409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062579484,0.000050185496,0.0000134853435,0.00003811268,0.000028873412,0.000010068447,0.00029115906,0.08500205,0.00003156962,0.38217407,0.001595753,0.53075844],"study_design_scores_gemma":[0.00018878856,0.00008770215,0.00007170541,0.00011313803,0.000008824763,0.000082663784,2.0467246e-7,0.40642434,0.000027329994,0.53071064,0.061907787,0.00037690732],"about_ca_topic_score_codex":0.0000055645755,"about_ca_topic_score_gemma":0.000025434338,"teacher_disagreement_score":0.5303815,"about_ca_system_score_codex":0.00039045865,"about_ca_system_score_gemma":0.00057052623,"threshold_uncertainty_score":0.9998685},"labels":[],"label_agreement":null},{"id":"W1560677248","doi":"10.1007/978-3-642-15384-6_57","title":"Prognosis of Breast Cancer Using Genetic Programming","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"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 Saskatchewan","funders":"","keywords":"Breast cancer; Genetic programming; Computer science; Cancer; Lung cancer; Machine learning; Artificial intelligence; Medicine; Oncology; Internal medicine","score_opus":0.017524650858758692,"score_gpt":0.26155643782471866,"score_spread":0.24403178696595995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560677248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063198275,0.0003836202,0.9970066,0.0006957177,0.00061888195,0.00044303728,0.000015833866,0.00007613648,0.00012820344],"genre_scores_gemma":[0.0655682,0.00003575223,0.93380135,0.0001227106,0.0003727568,0.00003049871,0.000001564511,0.00002103238,0.000046139794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974608,0.000012206761,0.00043708095,0.000992245,0.0006526741,0.00044502915],"domain_scores_gemma":[0.9981849,0.00009208621,0.00033164833,0.00087148737,0.00039478415,0.00012508918],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027738366,0.00031893668,0.00034097437,0.0004307851,0.0002516003,0.00019753884,0.002201529,0.00024547236,0.000023983408],"category_scores_gemma":[0.000008974738,0.00029561433,0.00010702868,0.000678408,0.000761949,0.0003419713,0.00082861487,0.00057816424,0.000004519875],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011775558,0.000057289846,0.00038973324,0.000042409098,0.000010336082,0.0000098116225,0.00026330343,0.01693748,0.0019201294,0.0060971207,0.0000017158974,0.9742695],"study_design_scores_gemma":[0.00015807187,0.000060847018,0.0028662456,0.00044379753,0.00001905835,0.00021605154,1.2943879e-7,0.93961906,0.0022475163,0.05285815,0.00090940436,0.0006016677],"about_ca_topic_score_codex":0.00010927211,"about_ca_topic_score_gemma":0.00006456618,"teacher_disagreement_score":0.9736678,"about_ca_system_score_codex":0.00014405843,"about_ca_system_score_gemma":0.00071092846,"threshold_uncertainty_score":0.9999496},"labels":[],"label_agreement":null},{"id":"W1560681326","doi":"10.1109/icec.1995.487447","title":"Hybridized crossover-based search techniques for program discovery","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Santa Fe Institute","keywords":"Crossover; Hill climbing; Genetic programming; Computer science; Iterated function; Simulated annealing; Iterated local search; Genetic representation; Genetic algorithm; Operator (biology); Mathematical optimization; Theoretical computer science; Algorithm; Artificial intelligence; Local search (optimization); Mathematics; Machine learning; Biology","score_opus":0.034033856403859634,"score_gpt":0.3069457912597012,"score_spread":0.27291193485584153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560681326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010181216,0.000048279555,0.98895705,0.0043677795,0.000031634245,0.00082474377,0.000011253863,0.0007291906,0.0040119523],"genre_scores_gemma":[0.14197525,0.0000100775,0.8520889,0.00034377634,0.00007225267,0.0009918053,0.000008399801,0.000007652986,0.0045018326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991851,0.000012492373,0.00012809159,0.0002849449,0.00015722537,0.00023214213],"domain_scores_gemma":[0.9993523,0.00008823341,0.000024191437,0.00039867492,0.00008249321,0.00005415447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011592515,0.000080986865,0.00008005163,0.00004713214,0.00020965666,0.00027283377,0.0004811801,0.00003015841,0.000031431417],"category_scores_gemma":[0.000009635953,0.000068135756,0.000079481215,0.0002610028,0.00005466142,0.00049090007,0.0000767407,0.00005529844,0.000030017218],"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":[0.000008964684,0.0013631476,0.00010674284,0.00002961729,0.000014121184,0.0000025045633,0.000054370128,0.000082071936,0.0019434759,0.4298347,0.039047305,0.52751297],"study_design_scores_gemma":[0.00046314424,0.00022065615,0.00021330953,0.0000076109804,0.0000026619539,0.0000045005363,0.0000046983946,0.83827263,0.029074194,0.0062937145,0.12525108,0.00019177956],"about_ca_topic_score_codex":0.0000155374,"about_ca_topic_score_gemma":0.0000015343434,"teacher_disagreement_score":0.83819056,"about_ca_system_score_codex":0.00002902264,"about_ca_system_score_gemma":0.000030266545,"threshold_uncertainty_score":0.27784956},"labels":[],"label_agreement":null},{"id":"W1565486756","doi":"","title":"Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers","year":2009,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Publishing; Computer science; Evolutionary computation; Track (disk drive); Operations research; Political science; Artificial intelligence; Law; Mathematics","score_opus":0.014811961576018752,"score_gpt":0.2366278524168018,"score_spread":0.22181589084078304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565486756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6831859,0.0006399578,0.30766937,0.004562094,0.00026101837,0.0008619779,0.00006556758,0.00019479287,0.0025593387],"genre_scores_gemma":[0.941892,0.00022967163,0.057254724,0.00031458595,0.00008195055,0.000032084205,0.00002954237,0.00001237054,0.00015305108],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9973012,0.00010422549,0.00066436024,0.0008575765,0.00065375847,0.00041888235],"domain_scores_gemma":[0.9979195,0.00015397537,0.00045384988,0.0002558587,0.0010162507,0.00020056941],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017835105,0.00038235003,0.00036413304,0.00019809583,0.0007542897,0.00019332522,0.0005890073,0.00016258337,0.000019349613],"category_scores_gemma":[0.000031884298,0.0003431331,0.00008403676,0.00061499904,0.00046362297,0.00049996906,0.0002744832,0.00029600132,0.000010941675],"study_design_candidate":"observational","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":[0.00015201862,0.0012187886,0.025752807,0.00028900916,0.00017096913,0.000012119822,0.00899677,0.09862134,0.00854966,0.47260335,0.0060685915,0.37756455],"study_design_scores_gemma":[0.00046018793,0.00022803652,0.5574127,0.00011785955,0.000020854084,0.00009407972,0.00023447741,0.39524448,0.000033326833,0.04564288,0.00024754956,0.00026362325],"about_ca_topic_score_codex":0.000043716827,"about_ca_topic_score_gemma":0.0000035089831,"teacher_disagreement_score":0.53165984,"about_ca_system_score_codex":0.000092284434,"about_ca_system_score_gemma":0.00033656618,"threshold_uncertainty_score":0.99990207},"labels":[],"label_agreement":null},{"id":"W1565737841","doi":"10.1007/11788911_21","title":"Analyzing the Genetic Operations of an Evolutionary Query Optimizer","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Crossover; Computer science; Genetic programming; Genetic algorithm; Plan (archaeology); Mutation; Operations research; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.011676907347618563,"score_gpt":0.23831243456395643,"score_spread":0.22663552721633787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565737841","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019598089,0.0010271146,0.99572927,0.0010517668,0.00036135837,0.0003374576,0.000013857067,0.00007160879,0.001211612],"genre_scores_gemma":[0.057102002,0.000047563826,0.9417717,0.00025682946,0.0004076546,0.00002578066,0.000017886785,0.000018545381,0.00035206752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757266,0.00004447739,0.0005300485,0.0009297708,0.0005662521,0.00035681773],"domain_scores_gemma":[0.9976622,0.00024294204,0.00017345254,0.0015114973,0.00031996568,0.00008994281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042390972,0.00030249203,0.00029312048,0.0004290763,0.00054681115,0.00021668436,0.0028551356,0.00016655104,0.000017749659],"category_scores_gemma":[0.000020868549,0.00023537458,0.00011814768,0.00071589154,0.0008348866,0.00057285867,0.00069182954,0.00044220005,0.000012069682],"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":[7.682953e-7,0.000046075962,0.000031943622,0.0000059842187,0.000006606158,0.0000067352257,0.000106280306,0.89575595,0.00009281229,0.02668343,0.00006156894,0.07720187],"study_design_scores_gemma":[0.00009119692,0.000060244878,0.0016814842,0.000059162106,0.000009110682,0.000045229343,1.5055906e-7,0.93398416,0.000118752265,0.06320289,0.00047524294,0.00027236433],"about_ca_topic_score_codex":0.00013577676,"about_ca_topic_score_gemma":0.00007334453,"teacher_disagreement_score":0.07692951,"about_ca_system_score_codex":0.00015994335,"about_ca_system_score_gemma":0.00060939323,"threshold_uncertainty_score":0.9598297},"labels":[],"label_agreement":null},{"id":"W1568544595","doi":"10.1109/cec.2005.1554981","title":"The Estimation of Evolvability Genetic Algorithm","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Evolvability; Genetic algorithm; Computer science; Fitness function; Estimation; Function (biology); Algorithm; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Evolutionary biology; Biology; Engineering","score_opus":0.007716369297148556,"score_gpt":0.24330377170589493,"score_spread":0.23558740240874637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1568544595","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019561562,0.00014731043,0.9929245,0.0033435102,0.000031896834,0.000091783026,9.312326e-7,0.000050378138,0.0014535222],"genre_scores_gemma":[0.18537405,0.000013733716,0.8142069,0.000040696625,0.000031118954,0.000018191744,5.545804e-7,0.0000012691196,0.00031346403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949783,0.00001726336,0.00015343048,0.00011939017,0.00012580254,0.000086304164],"domain_scores_gemma":[0.99937207,0.00009283925,0.000040718805,0.00040686762,0.00006181127,0.000025688114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014553101,0.000038699232,0.000039039605,0.000012684,0.00015043802,0.000025214382,0.0003908961,0.000015395757,0.000013122379],"category_scores_gemma":[0.000012755569,0.00002548996,0.00002823927,0.00017232432,0.00005367125,0.00015845087,0.00007084912,0.000031238575,0.00004355933],"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":[1.1928877e-7,0.00003495341,0.000027420596,7.0088674e-7,0.0000019703716,2.6907305e-8,0.00003098158,0.005822514,0.000019886635,0.06883397,0.00058285217,0.9246446],"study_design_scores_gemma":[0.000041703825,0.000011077062,0.01663443,7.6458565e-7,0.0000010450063,0.0000027933913,0.0000046499,0.9610749,0.00044898217,0.015999466,0.005747869,0.000032345855],"about_ca_topic_score_codex":0.00001668811,"about_ca_topic_score_gemma":0.0000037131158,"teacher_disagreement_score":0.95525235,"about_ca_system_score_codex":0.000019560986,"about_ca_system_score_gemma":0.00003245483,"threshold_uncertainty_score":0.11570628},"labels":[],"label_agreement":null},{"id":"W1569529403","doi":"10.1109/qest.2004.11","title":"Genetic Instruction Scheduling and Register Allocation","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Register allocation; Computer science; Instruction scheduling; Compiler; Parallel computing; Scheduling (production processes); Processor register; Processor scheduling; Optimizing compiler; Program optimization; Dynamic priority scheduling; Programming language; Two-level scheduling; Schedule; Mathematical optimization; Operating system","score_opus":0.012838746915617328,"score_gpt":0.23379018534964016,"score_spread":0.22095143843402285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569529403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12789947,0.00010529281,0.86572057,0.004552364,0.000030775715,0.00004779663,8.821674e-8,0.00007314499,0.0015705224],"genre_scores_gemma":[0.48931143,0.000021188158,0.51012474,0.00016090239,0.00007098948,0.000007304006,3.8893353e-7,0.0000012023764,0.000301873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964696,0.000006115652,0.000079065125,0.00014657415,0.00005871779,0.00006257617],"domain_scores_gemma":[0.9997541,0.0000073482147,0.000021230764,0.00016618572,0.000027267748,0.000023920386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038982835,0.0000353704,0.00002703477,0.000028392114,0.00009055948,0.000046695535,0.00010480062,0.000018780594,0.000007650566],"category_scores_gemma":[0.0000026653781,0.00003307527,0.000008316368,0.00010215726,0.00001747729,0.00031346676,0.00004448992,0.000028961522,0.000033915167],"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":[4.2461343e-7,0.00002554171,0.0006453677,0.0000028224692,0.0000031831773,1.7369646e-7,0.000131721,0.0017208555,0.0010600089,0.47214383,0.0002663517,0.5239997],"study_design_scores_gemma":[0.00017724535,0.00001281437,0.043341193,0.0000033906501,0.000001897852,0.00005513551,0.00001727995,0.9281794,0.00046852417,0.008598406,0.019051822,0.0000928841],"about_ca_topic_score_codex":0.000009138812,"about_ca_topic_score_gemma":0.00000653445,"teacher_disagreement_score":0.92645854,"about_ca_system_score_codex":0.000014204204,"about_ca_system_score_gemma":0.000011532141,"threshold_uncertainty_score":0.13487704},"labels":[],"label_agreement":null},{"id":"W1572763892","doi":"10.1017/cbo9780511808241.021","title":"Examples of Dynamic Programs","year":2008,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Programming language","score_opus":0.025049093011836946,"score_gpt":0.19995062083591608,"score_spread":0.17490152782407914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572763892","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000051613086,0.00024381863,0.06564743,0.000020009853,0.000077429926,0.0003407422,0.00008103553,0.00017232275,0.9333656],"genre_scores_gemma":[0.0011626214,0.00029957108,0.013303902,0.000011461087,0.000029100158,0.0000015579194,0.0000505236,0.000016787451,0.98512447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99893934,0.000015224261,0.0001720453,0.00044600642,0.00024282931,0.00018455021],"domain_scores_gemma":[0.998684,0.000037758164,0.00022529378,0.00078052026,0.00017094944,0.000101479774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000042045245,0.00021655436,0.00026177848,0.00012494781,0.00017017698,0.00001915906,0.0011105692,0.00018409987,0.000001029392],"category_scores_gemma":[0.0000015880918,0.00025638705,0.00017411506,0.000016092614,0.00027736824,0.00011523751,0.0005227948,0.00021746976,0.000008592365],"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":[0.0000032983764,0.00002069603,6.7997286e-7,0.000026803249,0.00004518206,0.00004717355,0.00003764522,0.0000020849063,0.000021802485,0.9750879,0.0057733473,0.018933436],"study_design_scores_gemma":[0.00020943649,0.00005333055,0.00006717781,0.00007609927,0.000033445667,0.00004052108,0.000006091239,0.0025388033,0.000042925243,0.00006469112,0.9965793,0.00028821142],"about_ca_topic_score_codex":0.000075548465,"about_ca_topic_score_gemma":0.0000013412588,"teacher_disagreement_score":0.9908059,"about_ca_system_score_codex":0.00009557312,"about_ca_system_score_gemma":0.00013400208,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W1574428622","doi":"10.1007/3-540-48035-8_76","title":"Collective Intelligence and Priority Routing in Networks","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Carleton University","funders":"","keywords":"Computer science; Swarm intelligence; Routing (electronic design automation); Ant colony; Distributed computing; Foraging; Swarm behaviour; Simplicity; Action (physics); Adaptation (eye); Artificial intelligence; Ant colony optimization algorithms; Computer network; Machine learning; Ecology; Particle swarm optimization","score_opus":0.02006697533973757,"score_gpt":0.2434654478876814,"score_spread":0.22339847254794384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574428622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000046349818,0.0006594723,0.99525374,0.0005457399,0.00041073395,0.00039562437,0.000001930447,0.00007864585,0.0026077458],"genre_scores_gemma":[0.47746405,0.00034771554,0.52025783,0.0006918431,0.00044546058,0.000032135773,0.0000026779585,0.000028619454,0.00072967156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972352,0.000030345964,0.00044409314,0.0013218571,0.0004423305,0.0005261997],"domain_scores_gemma":[0.99830794,0.00051625335,0.00018424146,0.0007238355,0.00013993356,0.00012780742],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006455762,0.00034856045,0.00036374564,0.00050614314,0.00031707567,0.0003147717,0.0016091388,0.0002516155,0.000009905431],"category_scores_gemma":[0.00005726497,0.00034851846,0.000053447715,0.0010809447,0.00056946033,0.0004638754,0.0011515825,0.0008903239,0.000009959008],"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":[0.0000019789468,0.00005071499,0.00046542144,0.000011925265,0.000005275396,0.000046301913,0.001560351,0.10879175,0.0000044313883,0.112575985,0.000008726298,0.77647716],"study_design_scores_gemma":[0.00007403099,0.000053878008,0.0012244273,0.00015321525,0.000001871249,0.00004354146,2.1075526e-7,0.8249727,0.00002245522,0.17298311,0.00015291366,0.00031766185],"about_ca_topic_score_codex":0.000038647107,"about_ca_topic_score_gemma":0.00008491703,"teacher_disagreement_score":0.77615947,"about_ca_system_score_codex":0.000562573,"about_ca_system_score_gemma":0.0004197554,"threshold_uncertainty_score":0.9998967},"labels":[],"label_agreement":null},{"id":"W1576324336","doi":"10.1109/cec.2005.1554824","title":"CasGP: Building Cascaded Hierarchical Models Using Niching","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Scalability; Layer (electronics); Metric (unit); Cascade; Genetic programming; Function (biology); Selection (genetic algorithm); Feature (linguistics); Genetic algorithm; Dynamic programming; Mathematical optimization; Algorithm; Artificial intelligence; Data mining; Machine learning; Mathematics; Engineering","score_opus":0.03786847997479447,"score_gpt":0.2912821835145228,"score_spread":0.2534137035397283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576324336","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038732234,0.000089846944,0.95458555,0.0030829187,0.000046935653,0.000077918034,8.504701e-7,0.00019132312,0.003192429],"genre_scores_gemma":[0.4659397,0.0000032558921,0.5334659,0.00027050517,0.00012164277,0.000004491505,4.099385e-7,0.0000036953495,0.00019039631],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990955,0.000023105169,0.00017238384,0.00029357514,0.00017444597,0.00024098119],"domain_scores_gemma":[0.99944484,0.00005344406,0.0000326863,0.00033600407,0.000032960634,0.00010003789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014885128,0.000087602195,0.000083696024,0.0000688171,0.0002946267,0.00009185928,0.00048405366,0.000043010194,0.000014838309],"category_scores_gemma":[0.0000056851495,0.0000802558,0.000048274465,0.00027363744,0.00002542055,0.00087741273,0.00022422957,0.00014804749,0.000017388424],"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":[3.877965e-7,0.00004021014,0.000011099132,0.0000012510186,0.000004854394,0.0000020266955,0.00018731432,0.10151413,0.0033319758,0.8739593,0.00022563136,0.020721832],"study_design_scores_gemma":[0.000091007285,0.000005131145,0.000045904784,0.000004843262,0.0000020547438,0.00006866832,0.000007387209,0.9614338,0.00074335356,0.03523822,0.0022473803,0.00011223869],"about_ca_topic_score_codex":0.000084133324,"about_ca_topic_score_gemma":0.000007596627,"teacher_disagreement_score":0.85991967,"about_ca_system_score_codex":0.00006801152,"about_ca_system_score_gemma":0.000057588204,"threshold_uncertainty_score":0.32727367},"labels":[],"label_agreement":null},{"id":"W1578966950","doi":"10.1007/978-1-4419-1626-6_10","title":"Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes","year":2009,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","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":"Brock University","funders":"","keywords":"Genetic programming; Computer science; Set (abstract data type); Construct (python library); Process (computing); Feature (linguistics); Process calculus; Stochastic process; Selection (genetic algorithm); Feature selection; Machine learning; Artificial intelligence; Mathematical optimization; Theoretical computer science; Programming language; Mathematics","score_opus":0.028463512170990832,"score_gpt":0.2645522546837153,"score_spread":0.23608874251272446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1578966950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034023172,0.004075922,0.992589,0.00025501187,0.00014521407,0.0010713158,0.000022154592,0.00021329503,0.0012878617],"genre_scores_gemma":[0.027583405,0.00009374412,0.96556884,0.0001526563,0.00030615838,0.00008189928,0.000038724957,0.000046385394,0.0061282003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776155,0.00003185271,0.000476288,0.00095067977,0.0004122474,0.00036738126],"domain_scores_gemma":[0.9985925,0.00013799129,0.0002525894,0.00033326584,0.00045518842,0.00022845883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008225774,0.00042757014,0.0003343701,0.00031791508,0.0005298089,0.00013256323,0.00038803482,0.0002145054,0.000008222325],"category_scores_gemma":[0.000034798548,0.000474634,0.00007959262,0.00025192063,0.00010977057,0.00019504169,0.0002233175,0.00020685715,0.000061670806],"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":[0.000019831774,0.00022481347,0.000034574176,0.00016943399,0.00013981345,0.00003151893,0.00081796123,0.35804963,0.00006740869,0.016817799,0.00041835377,0.6232089],"study_design_scores_gemma":[0.00047615558,0.00038850337,0.017541539,0.0004355013,0.0001581154,0.0005158278,0.000036776823,0.873738,0.0000045134307,0.100793846,0.0046856897,0.0012255204],"about_ca_topic_score_codex":0.000021498674,"about_ca_topic_score_gemma":0.000006672009,"teacher_disagreement_score":0.62198335,"about_ca_system_score_codex":0.00021926862,"about_ca_system_score_gemma":0.00045594157,"threshold_uncertainty_score":0.9997705},"labels":[],"label_agreement":null},{"id":"W1579385695","doi":"10.1002/9780470973134.ch9","title":"Genetic Programming for Exploring Medical Data Using Visual Spaces","year":2010,"lang":"en","type":"other","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Artificial intelligence; Data science; Human–computer interaction","score_opus":0.05950827733421983,"score_gpt":0.3158990664913205,"score_spread":0.2563907891571007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579385695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010478217,0.0042319787,0.9921206,0.00034966602,0.0007068644,0.0007038779,0.000054101172,0.00029179378,0.0004932714],"genre_scores_gemma":[0.0022310335,0.0004435339,0.99283576,0.000054683383,0.001433619,0.00018108998,0.00034155892,0.00013063509,0.0023481024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978967,0.00004677228,0.00032840073,0.00087054743,0.00051822595,0.00033937686],"domain_scores_gemma":[0.99887323,0.00010562389,0.0002154938,0.00052577065,0.00008230602,0.00019758017],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019238594,0.00026663375,0.00023906495,0.00023296374,0.00032791193,0.0001443361,0.0008742596,0.00028998713,0.000044345372],"category_scores_gemma":[0.000037415393,0.0002821272,0.000047847945,0.00026006092,0.0001470729,0.000222514,0.00068051205,0.00021423373,0.000013827308],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071267873,0.00028981848,0.00061567046,0.00025360336,0.00015124706,0.000015891865,0.00015382777,0.003055983,0.000057474692,0.003383718,0.07427702,0.9177386],"study_design_scores_gemma":[0.00028226012,0.00005260987,0.0030206644,0.00008218738,0.00003767776,0.00011523702,0.000023107887,0.81542295,7.35983e-7,0.0018352644,0.1788214,0.00030591988],"about_ca_topic_score_codex":0.00014872292,"about_ca_topic_score_gemma":0.000040470528,"teacher_disagreement_score":0.91743267,"about_ca_system_score_codex":0.00003553998,"about_ca_system_score_gemma":0.00034104905,"threshold_uncertainty_score":0.9999631},"labels":[],"label_agreement":null},{"id":"W1581567161","doi":"","title":"EVALUATION OF FORECASTS PRODUCED BY GENETICALLY EVOLVED MODELS","year":2000,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Genetic programming; Heuristics; Computer science; Series (stratigraphy); Simple (philosophy); Process (computing); Machine learning; Programming language","score_opus":0.06948301755472099,"score_gpt":0.3364684688345633,"score_spread":0.2669854512798423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581567161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81770414,0.0027663005,0.018609282,0.0030292724,0.0005301718,0.0068788654,0.00028057385,0.00019030692,0.1500111],"genre_scores_gemma":[0.9607088,0.004244787,0.03327931,0.000025469315,0.00011667021,0.001093475,0.000081900595,0.000033085278,0.00041647878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966022,0.00040554695,0.000625192,0.0010522977,0.00083735643,0.0004774287],"domain_scores_gemma":[0.99727297,0.00017735548,0.0001678617,0.0016174576,0.0006298611,0.0001345011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040516355,0.00020260534,0.00032034426,0.0002856679,0.00010799239,0.000092530456,0.0015034521,0.00026358804,0.000052812637],"category_scores_gemma":[0.00013758984,0.0002272712,0.00011167249,0.00021833007,0.00017821198,0.00020325772,0.0009429265,0.00068139395,0.000007302072],"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":[0.000008400403,0.00024641727,0.0000373755,0.000028276567,0.000039977116,5.706742e-7,0.00015456558,0.25933832,0.0004452906,0.0026677616,0.00017004789,0.736863],"study_design_scores_gemma":[0.00030334634,0.000044474687,0.00051009393,0.000050196297,0.000009131914,0.0000018119291,0.000012748492,0.93320453,0.00060339866,0.06432474,0.00074189797,0.0001936507],"about_ca_topic_score_codex":0.00004568332,"about_ca_topic_score_gemma":0.000022079586,"teacher_disagreement_score":0.73666936,"about_ca_system_score_codex":0.0006766278,"about_ca_system_score_gemma":0.0015460553,"threshold_uncertainty_score":0.92678505},"labels":[],"label_agreement":null},{"id":"W1584211151","doi":"10.1109/cec.2003.1299584","title":"An evolutionary approach to behavioural-level synthesis","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; High-level synthesis; Scheduling (production processes); Processor scheduling; Parallel computing; Theoretical computer science; Distributed computing; Mathematical optimization; Field-programmable gate array; Mathematics; Schedule; Embedded system","score_opus":0.04960530375862269,"score_gpt":0.25995738193954476,"score_spread":0.21035207818092208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584211151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027169639,0.00002689041,0.95689017,0.0006202631,0.000057867892,0.00016974148,0.000008472672,0.00020783136,0.039301783],"genre_scores_gemma":[0.3964594,0.000001249973,0.6022815,0.00020669117,0.000016117898,0.00014142907,0.000002881438,0.000004518495,0.0008861815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998973,0.000055396973,0.00014415076,0.0004015341,0.00019975711,0.00022613781],"domain_scores_gemma":[0.9989933,0.00004121604,0.000022856766,0.00066794426,0.000073751726,0.00020097908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016355967,0.00010202513,0.00008510134,0.00008158546,0.00023867628,0.00006157388,0.0006456101,0.000042481952,0.000054016746],"category_scores_gemma":[0.000023949766,0.00009311418,0.000041700074,0.00047816377,0.000023064087,0.0004621692,0.000058288686,0.00006004628,0.00019575095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.2166823e-7,0.00046870494,0.0010564749,0.0000013229461,0.000004025804,6.74633e-7,0.00007871967,0.00042870612,0.00031569166,0.987564,0.0054327818,0.0046484196],"study_design_scores_gemma":[0.00046007335,0.00023104812,0.49897513,0.000016630158,0.000034140267,0.0003567182,0.0005053618,0.38876814,0.006118451,0.044827525,0.058020253,0.0016865463],"about_ca_topic_score_codex":0.00005239227,"about_ca_topic_score_gemma":0.0000012347856,"teacher_disagreement_score":0.9427365,"about_ca_system_score_codex":0.000048616293,"about_ca_system_score_gemma":0.00006709521,"threshold_uncertainty_score":0.37970862},"labels":[],"label_agreement":null},{"id":"W1584380365","doi":"10.1115/1.859599.paper24","title":"Using Evolvable Regressors to Partition Data","year":2010,"lang":"en","type":"book-chapter","venue":"ASME Press eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Partition (number theory); Computer science; Data set; Data mining; Process (computing); Set (abstract data type); Artificial intelligence; Mathematics; Combinatorics","score_opus":0.1772480937848931,"score_gpt":0.3245944538905887,"score_spread":0.14734636010569563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584380365","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008028347,0.00019519244,0.4180333,0.00022190336,0.00039573645,0.0004744421,0.00017726692,0.0002080492,0.5802861],"genre_scores_gemma":[0.00034789718,0.000021608243,0.41612765,0.00027498187,0.00049988244,0.00006526364,0.00014425162,0.00005593136,0.58246255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982347,0.000013799926,0.00029337645,0.0008382977,0.00034582627,0.00027402467],"domain_scores_gemma":[0.9965906,0.000046582638,0.00018229497,0.0028736587,0.00012947057,0.00017739064],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018677938,0.00025701558,0.00022588218,0.00009104759,0.00026904844,0.0001526176,0.0020672297,0.00026202423,0.000035494366],"category_scores_gemma":[0.000011991888,0.0002647528,0.00005853044,0.000013865018,0.000074623495,0.00029400093,0.0017475513,0.000430082,0.00011509795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020623652,0.000013507026,4.1949778e-7,0.000014623136,0.00002825834,0.000008596516,0.00006872508,0.00007487797,0.0005904252,0.97901374,0.0045554168,0.01562936],"study_design_scores_gemma":[0.00007967139,0.000018398228,0.0000041619155,0.000097463424,0.000030730593,0.000021402884,5.5514766e-7,0.04289866,0.0005470861,0.04162639,0.9143292,0.00034628212],"about_ca_topic_score_codex":0.00015546012,"about_ca_topic_score_gemma":0.000026076594,"teacher_disagreement_score":0.93738735,"about_ca_system_score_codex":0.000038925824,"about_ca_system_score_gemma":0.00011485389,"threshold_uncertainty_score":0.99998045},"labels":[],"label_agreement":null},{"id":"W1586558052","doi":"10.1007/978-3-642-01088-0_6","title":"Inducing Relational Fuzzy Classification Rules by Means of Cooperative Coevolution","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Fuzzy classification; Fuzzy logic; Genetic programming; Data mining; Population; Neuro-fuzzy; Evolutionary algorithm; Membership function; Fuzzy control system","score_opus":0.10576768326572608,"score_gpt":0.3404296944026688,"score_spread":0.2346620111369427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586558052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000037523056,0.0070467805,0.92518294,0.001895414,0.00029628692,0.0004905181,0.00015367947,0.000084666455,0.06481219],"genre_scores_gemma":[0.2142976,0.011171878,0.6890546,0.0006787405,0.00071777677,0.0002474713,0.0020228743,0.000107729225,0.081701346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973599,0.000056261557,0.0009886983,0.00071946013,0.0006615427,0.00021418424],"domain_scores_gemma":[0.9971079,0.00082713,0.00057282497,0.0003594663,0.0010772209,0.000055447217],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037878242,0.00034051965,0.00044654062,0.00031186856,0.00025645344,0.000027936998,0.0006235209,0.00021341373,0.000023092687],"category_scores_gemma":[0.0000989232,0.00036129195,0.000114271395,0.00026736606,0.0004945392,0.00038209124,0.00021190927,0.00044656266,0.00007912096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000445852,0.00005345751,0.000013851462,0.000022699864,0.00007075459,0.0000017706927,0.00054156996,0.0785982,0.000011861422,0.8957688,0.0026415046,0.022271032],"study_design_scores_gemma":[0.00009881157,0.000106653984,0.000777587,0.00036996612,0.000017879125,0.0000130892995,0.00015603831,0.19379207,0.000029643546,0.79659456,0.0076558143,0.00038789795],"about_ca_topic_score_codex":0.000010325019,"about_ca_topic_score_gemma":0.000011413408,"teacher_disagreement_score":0.23612835,"about_ca_system_score_codex":0.0004358315,"about_ca_system_score_gemma":0.0002641125,"threshold_uncertainty_score":0.9998839},"labels":[],"label_agreement":null},{"id":"W1588477125","doi":"10.1007/978-3-642-17298-4_31","title":"Supplanting Neural Networks with ODEs in Evolutionary Robotics","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Genetic programming; Computer science; Ode; Artificial intelligence; Symbolic regression; Artificial neural network; Evolutionary robotics; Robotics; Robot; Ordinary differential equation; Evolutionary algorithm; Genetic algorithm; Controller (irrigation); Differential equation; Machine learning; Mathematics; Applied mathematics","score_opus":0.010432609770204194,"score_gpt":0.22117974172815705,"score_spread":0.21074713195795286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588477125","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013082383,0.00036619673,0.9961856,0.0014302589,0.00069238845,0.00033239755,0.000004522624,0.00012466637,0.00073316635],"genre_scores_gemma":[0.18025418,0.00003089192,0.8185587,0.00046688336,0.0005100338,0.000016182254,0.000016276805,0.000026427915,0.00012042035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697435,0.00002247606,0.00043473722,0.0012439458,0.0006508301,0.00067367655],"domain_scores_gemma":[0.99803895,0.000395817,0.0002135258,0.0010404512,0.00017261188,0.000138654],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042347948,0.00042329094,0.00037551468,0.00056990504,0.00035907846,0.00028382297,0.0023857546,0.00032995475,0.000009298966],"category_scores_gemma":[0.000020598278,0.0003699187,0.00006837192,0.0008142044,0.00072121975,0.0006121692,0.0008706424,0.0015079603,0.000009571269],"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":[0.0000031836175,0.000035316836,0.00074217794,0.000009623441,0.0000037367688,0.000072704985,0.000107850145,0.89415485,0.000027700848,0.031619765,0.000008336863,0.07321478],"study_design_scores_gemma":[0.000174473,0.000075278054,0.0018614425,0.00014546583,0.0000036701172,0.00022036476,1.2032974e-7,0.9593043,0.000027728505,0.03743412,0.00029890632,0.00045416385],"about_ca_topic_score_codex":0.000043235257,"about_ca_topic_score_gemma":0.0002731307,"teacher_disagreement_score":0.18012336,"about_ca_system_score_codex":0.00016801126,"about_ca_system_score_gemma":0.00035752813,"threshold_uncertainty_score":0.99987525},"labels":[],"label_agreement":null},{"id":"W1590984588","doi":"10.1007/3-540-44938-8_1","title":"Data Dependence in Combining Classifiers","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Categorization; Classifier (UML); Artificial intelligence; Variety (cybernetics); Machine learning; Data mining; Text categorization; Pattern recognition (psychology)","score_opus":0.042709517163155734,"score_gpt":0.27603447508627005,"score_spread":0.2333249579231143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590984588","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015084959,0.00060504,0.9920515,0.0013535183,0.00089705247,0.00030938408,0.000018180895,0.00009334267,0.0046569225],"genre_scores_gemma":[0.023723586,0.00012147573,0.97306657,0.002226714,0.00019383192,0.00001889941,0.000034594144,0.000032296426,0.0005820282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99592716,0.000050119754,0.000537036,0.0020111084,0.0008377563,0.0006367997],"domain_scores_gemma":[0.9960083,0.000512688,0.00022327586,0.002976995,0.00011851923,0.00016024327],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0014630429,0.00039850138,0.00039175065,0.000860636,0.0002639619,0.00035807787,0.0071062776,0.00027299108,0.00001868929],"category_scores_gemma":[0.00010880719,0.00040239538,0.000053396143,0.001243388,0.0005866443,0.0011011689,0.0024821379,0.0009997068,0.000048771537],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027915864,0.000084592146,0.00024238549,0.000023159067,0.000009907824,0.00014289738,0.00035032677,0.019209052,0.000044260418,0.3454045,0.00021107277,0.634275],"study_design_scores_gemma":[0.00021627119,0.000043985077,0.00032090346,0.00019813735,0.0000037010848,0.00008239589,3.1082416e-7,0.6307813,0.00004274982,0.3600271,0.007766411,0.000516721],"about_ca_topic_score_codex":0.000023319693,"about_ca_topic_score_gemma":0.0001745475,"teacher_disagreement_score":0.6337583,"about_ca_system_score_codex":0.0003008123,"about_ca_system_score_gemma":0.00065961474,"threshold_uncertainty_score":0.99984276},"labels":[],"label_agreement":null},{"id":"W1592012372","doi":"10.1109/reldis.2002.1180207","title":"A search for routing strategies in a peer-to-peer network using genetic programming","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Protocol (science); Routing protocol; Distributed computing; Peer-to-peer; Routing (electronic design automation); Genetic programming; Resource (disambiguation); Computer network; Enhanced Interior Gateway Routing Protocol; Wireless Routing Protocol; Artificial intelligence","score_opus":0.043790998470581564,"score_gpt":0.3189134581869949,"score_spread":0.27512245971641336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592012372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034055874,0.00004127418,0.9634131,0.0008962972,0.000054988737,0.0004925384,5.274118e-7,0.000065743705,0.000979654],"genre_scores_gemma":[0.34430265,3.6533476e-7,0.65522206,0.000058345777,0.00005173372,0.00007721015,7.89845e-7,0.0000049101786,0.0002819506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988473,0.000033564753,0.00017835727,0.00029273555,0.00023428447,0.00041377888],"domain_scores_gemma":[0.99941814,0.00007250192,0.00002245911,0.0002243724,0.00019155044,0.00007095943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058779557,0.000081666294,0.00008544619,0.00005902069,0.00020686311,0.00024441275,0.00028192476,0.000032656782,0.0000045802753],"category_scores_gemma":[0.000027020304,0.00007898875,0.000032596563,0.0007324145,0.000015604543,0.00021272418,0.00007309304,0.00007396708,0.000006491726],"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":[0.0000012924676,0.00007136767,0.0031405182,0.000009197449,0.000005875181,0.0000025250931,0.00072914857,0.24882264,0.00018880283,0.7213968,0.00027093122,0.025360899],"study_design_scores_gemma":[0.00021697317,0.00005418148,0.003406236,0.000019705547,0.0000024502635,0.000017290744,0.00060590333,0.9728632,0.00016203825,0.010297465,0.012167045,0.00018749383],"about_ca_topic_score_codex":0.00010393386,"about_ca_topic_score_gemma":0.000033214827,"teacher_disagreement_score":0.72404057,"about_ca_system_score_codex":0.000049126607,"about_ca_system_score_gemma":0.0001756846,"threshold_uncertainty_score":0.32210675},"labels":[],"label_agreement":null},{"id":"W1594809898","doi":"10.1109/cec.2005.1554958","title":"Toward Co-Evolutionary Training of a Multi-Class Classifier","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Genetic programming; Computer science; Classifier (UML); Artificial intelligence; Machine learning; Population; Evolutionary algorithm; Class (philosophy); Boosting (machine learning)","score_opus":0.07967135720985735,"score_gpt":0.29777225797278983,"score_spread":0.21810090076293248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594809898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017582802,0.00015169074,0.96975845,0.005826665,0.00005830405,0.00011719272,0.00000804442,0.00016245536,0.022158919],"genre_scores_gemma":[0.5647665,0.000010770657,0.43279904,0.00025788738,0.000081890495,0.00002382895,0.00000446648,0.000004072048,0.0020515162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991194,0.000021128704,0.00023039614,0.00024804118,0.0001892869,0.0001917528],"domain_scores_gemma":[0.99940205,0.000056535217,0.000065215994,0.00032192594,0.00007584179,0.000078419675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011772702,0.000086905,0.000113717884,0.00007251481,0.00009779068,0.000017268441,0.0004820853,0.000050927312,0.00009541619],"category_scores_gemma":[0.000010339182,0.00007944357,0.00006872407,0.00026266955,0.00007464137,0.0003792567,0.00008643856,0.0000865398,0.00012505216],"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":[0.0000029311943,0.00052236475,0.00031230727,0.000011087375,0.00003102138,0.0000026406778,0.0020323046,0.002014433,0.0033150231,0.84142345,0.02052113,0.12981132],"study_design_scores_gemma":[0.00039550872,0.000031264553,0.0076853074,0.0000072524,0.0000031229017,0.000028829834,0.00017935706,0.8706563,0.00092423474,0.0015735392,0.11836177,0.00015347487],"about_ca_topic_score_codex":0.000012401404,"about_ca_topic_score_gemma":0.000004198493,"teacher_disagreement_score":0.8686419,"about_ca_system_score_codex":0.000042683256,"about_ca_system_score_gemma":0.00011110054,"threshold_uncertainty_score":0.32396147},"labels":[],"label_agreement":null},{"id":"W1594829400","doi":"10.1007/978-3-540-87527-7_17","title":"What Hides in Dimension X? A Quest for Visualizing Particle Swarms","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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 Calgary","funders":"","keywords":"Computer science; Visualization; Particle swarm optimization; Cluster analysis; Representation (politics); Data visualization; Dimension (graph theory); Population; Range (aeronautics); Theoretical computer science; Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.026032629025926197,"score_gpt":0.28392369610236484,"score_spread":0.25789106707643866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594829400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008090095,0.0016113503,0.9939574,0.001943165,0.00093489955,0.0005341993,0.000002435272,0.00010525791,0.00010228986],"genre_scores_gemma":[0.2450108,0.0009212601,0.7511968,0.0017888105,0.0005295378,0.00011682136,0.000013435254,0.00004235536,0.0003801825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737483,0.000019726358,0.00043785773,0.0011598165,0.00048328904,0.00052445347],"domain_scores_gemma":[0.9983132,0.00046444588,0.00016199153,0.0007823027,0.00015983469,0.00011826271],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050887774,0.000306225,0.00032665688,0.00034787692,0.00030761285,0.00039447861,0.0014205846,0.00017324185,0.0000030093229],"category_scores_gemma":[0.00004287305,0.00028667218,0.00009277802,0.00055503636,0.00037956407,0.0012968883,0.00062155153,0.00032978604,0.000019432548],"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":[0.000010494855,0.00019414298,0.00016981941,0.00005504745,0.000009910757,0.00008413913,0.003899669,0.059567638,0.0009971586,0.11664362,0.00015997882,0.8182084],"study_design_scores_gemma":[0.0002940862,0.00012804782,0.00031123194,0.00042925755,0.0000027305052,0.000059613816,6.7005305e-7,0.8937381,0.0018429809,0.09882387,0.0039219144,0.00044751467],"about_ca_topic_score_codex":0.000032531785,"about_ca_topic_score_gemma":0.00009423905,"teacher_disagreement_score":0.83417046,"about_ca_system_score_codex":0.00024228699,"about_ca_system_score_gemma":0.00027389554,"threshold_uncertainty_score":0.9999585},"labels":[],"label_agreement":null},{"id":"W1595833556","doi":"10.1007/978-3-540-71605-1_21","title":"Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Pareto principle; Artificial intelligence; Computer science; Coevolution; Machine learning; Genetic programming; Random subspace method; Multi-objective optimization; Binary classification; Scheme (mathematics); Mathematical optimization; Support vector machine; Mathematics","score_opus":0.04805478791578602,"score_gpt":0.27049104154648856,"score_spread":0.22243625363070255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595833556","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056997927,0.0009197747,0.9728694,0.0006132379,0.0011329389,0.00055340887,0.000014092154,0.0003372461,0.023502924],"genre_scores_gemma":[0.08083237,0.000055928296,0.9160231,0.0013069096,0.0008292401,0.000046075238,0.00004434493,0.00005450901,0.0008075618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99454504,0.000038602015,0.00071782194,0.0021990591,0.0014117155,0.0010877332],"domain_scores_gemma":[0.9969315,0.00045659873,0.00033963486,0.0016521991,0.0002782858,0.00034181352],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013435165,0.0006549745,0.00056651497,0.0012806308,0.00077568897,0.00032541956,0.0037896296,0.0004926431,0.000017709348],"category_scores_gemma":[0.000049853872,0.00063916587,0.00022762924,0.0015559546,0.0013652112,0.0006447368,0.0013133984,0.0012354222,0.0000661381],"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":[0.000008314829,0.00018347063,0.0000421085,0.000046778674,0.000025410804,0.00011084145,0.0017014695,0.12718822,0.00010962473,0.19035387,0.0001921435,0.68003774],"study_design_scores_gemma":[0.00025397816,0.00013281657,0.00047132073,0.00017081293,0.0000098343335,0.00021044324,0.0000022536417,0.91518897,0.000038933573,0.07547929,0.0072318255,0.0008095464],"about_ca_topic_score_codex":0.000015212712,"about_ca_topic_score_gemma":0.0000100092375,"teacher_disagreement_score":0.7880007,"about_ca_system_score_codex":0.0006337527,"about_ca_system_score_gemma":0.0011287059,"threshold_uncertainty_score":0.99960595},"labels":[],"label_agreement":null},{"id":"W1595949339","doi":"","title":"Lens system design and re-engineering with Evolutionary Algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Lens (geology); Genetic programming; Computer science; Evolutionary programming; Evolutionary algorithm; Through-the-lens metering; Genetic algorithm; Evolutionary computation; Artificial intelligence; Machine learning; Engineering","score_opus":0.021412946513290464,"score_gpt":0.19418177051033084,"score_spread":0.17276882399704038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595949339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023680534,0.00048552823,0.99513185,0.0010152721,0.000044003944,0.00017400377,0.0000011483154,0.00042005398,0.002491318],"genre_scores_gemma":[0.18577194,0.000032691725,0.8134474,0.000050135422,0.000052043666,0.00005750986,7.8158354e-7,0.000007708297,0.0005797494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923617,0.000017136308,0.00011640536,0.00028640399,0.00015798035,0.00018592199],"domain_scores_gemma":[0.99947983,0.000069047885,0.00002697981,0.00030037487,0.000048976173,0.000074793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000881625,0.00010437622,0.00008782734,0.00006211589,0.0001710579,0.00005556842,0.00023176368,0.000033743087,0.000015626323],"category_scores_gemma":[0.0000029508037,0.00008427678,0.000016697575,0.00030063395,0.000026968406,0.00046536306,0.00007315085,0.00007224925,0.000051813306],"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":[0.0000030042468,0.00018247054,0.00028287643,0.00006797386,0.0000659552,0.000057600464,0.00045031914,0.04240074,0.0005273715,0.92348826,0.018734012,0.013739418],"study_design_scores_gemma":[0.00014328954,0.000056296394,0.0010637594,0.00001998083,0.0000037843035,0.00019114201,0.000030584553,0.99488354,0.00005935464,0.00008887973,0.003323652,0.0001357456],"about_ca_topic_score_codex":0.000011056436,"about_ca_topic_score_gemma":3.110179e-7,"teacher_disagreement_score":0.9524828,"about_ca_system_score_codex":0.00004773348,"about_ca_system_score_gemma":0.000011521808,"threshold_uncertainty_score":0.34367073},"labels":[],"label_agreement":null},{"id":"W1599040639","doi":"10.1109/iscas.2001.921288","title":"Design of piece-wise maps for spread spectrum communication using genetic programming","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Genetic programming; Spread spectrum; Computer science; Construct (python library); Code (set theory); Sequence (biology); Genetic algorithm; Spectrum (functional analysis); Interval (graph theory); Algorithm; Theoretical computer science; Artificial intelligence; Programming language; Mathematics; Machine learning; Code division multiple access; Telecommunications","score_opus":0.06026838104876835,"score_gpt":0.2691165363102545,"score_spread":0.20884815526148615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599040639","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086735236,0.00043398835,0.9969794,0.00086720666,0.000022788014,0.0004984917,0.0000016429257,0.00007356286,0.0002555769],"genre_scores_gemma":[0.12820354,0.000042794643,0.8714531,0.000029111323,0.000018997947,0.000064304324,0.000002160115,0.0000052964288,0.00018067748],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993513,0.00003159084,0.00019391306,0.00017028328,0.00009795617,0.0001549247],"domain_scores_gemma":[0.99916226,0.00010040203,0.00008828181,0.0005520663,0.00005967298,0.00003734261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012841614,0.00006843588,0.00008340707,0.000050534672,0.00016207555,0.0000395306,0.00052612246,0.000029202902,0.000017258719],"category_scores_gemma":[0.000007816659,0.00006651712,0.000036940728,0.0002550661,0.000043096687,0.0001811721,0.000095112875,0.000037782887,0.000009278043],"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":[0.000009434325,0.0015181415,0.00072926143,0.00009848917,0.00007996371,0.0000018040663,0.0018709316,0.06245314,0.010306617,0.45824638,0.00536602,0.4593198],"study_design_scores_gemma":[0.00016119567,0.000054916913,0.0002520062,0.0000111510835,0.0000068529494,0.000009465249,0.000015714213,0.98329437,0.0014835435,0.012439333,0.0021819621,0.00008952014],"about_ca_topic_score_codex":0.000036217465,"about_ca_topic_score_gemma":0.0000021160174,"teacher_disagreement_score":0.9208412,"about_ca_system_score_codex":0.000026845626,"about_ca_system_score_gemma":0.000016112532,"threshold_uncertainty_score":0.27124894},"labels":[],"label_agreement":null},{"id":"W1600178424","doi":"10.1109/cec.2015.7257148","title":"Evolving fractal art with a directed acyclic graph genetic programming representation","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Fractal; Fitness function; Genetic programming; Representation (politics); Computer science; Digraph; Graph; Genetic representation; Mathematical optimization; Theoretical computer science; Mathematics; Genetic algorithm; Artificial intelligence; Discrete mathematics","score_opus":0.02072204219500991,"score_gpt":0.2642176212185772,"score_spread":0.2434955790235673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600178424","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027333325,0.000099362915,0.965332,0.00090314815,0.000053875243,0.00026615214,4.1588171e-7,0.0005831277,0.0054285955],"genre_scores_gemma":[0.39158332,0.0000031262102,0.6073556,0.00005895936,0.000049170845,0.00010204922,0.000007720586,0.0000061804385,0.0008338468],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906564,0.000028751236,0.0001383014,0.00031716027,0.00026403944,0.00018608652],"domain_scores_gemma":[0.9992253,0.00003622472,0.000059623206,0.0003828337,0.0001632354,0.00013276491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008505499,0.00008703858,0.0000771097,0.00007441557,0.00013182432,0.00014543628,0.0002952203,0.000026799895,0.000011021658],"category_scores_gemma":[0.000023943687,0.00006987603,0.000026580701,0.00076390407,0.000036188114,0.0005361872,0.00008539105,0.000068563386,0.00007653898],"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":[0.000048476544,0.0019999165,0.15884444,0.000036976075,0.00024776574,0.00014273496,0.005933256,0.006539853,0.0032828364,0.09276922,0.08933586,0.64081866],"study_design_scores_gemma":[0.0011295541,0.00032263924,0.22215709,0.000032246346,0.000023527791,0.00022508713,0.00035305106,0.73780453,0.0007332466,0.013020715,0.023617452,0.0005808736],"about_ca_topic_score_codex":0.00008832029,"about_ca_topic_score_gemma":0.000027620377,"teacher_disagreement_score":0.73126465,"about_ca_system_score_codex":0.00002813869,"about_ca_system_score_gemma":0.000072952884,"threshold_uncertainty_score":0.28494617},"labels":[],"label_agreement":null},{"id":"W1600825218","doi":"10.1155/2015/873794","title":"Compromise Rank Genetic Programming for Automated Nonlinear Design of Disaster Management","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Rank (graph theory); Compromise; Mathematical optimization; Computer science; Genetic algorithm; Nonlinear programming; Nonlinear system; Genetic programming; Multi-objective optimization; Process (computing); Artificial intelligence; Mathematics","score_opus":0.030851559950880823,"score_gpt":0.253591673032192,"score_spread":0.22274011308131117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600825218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072732056,0.000058880818,0.99777484,0.000065876295,0.000034248253,0.0010269608,0.0000011418923,0.00025482813,0.000055927914],"genre_scores_gemma":[0.08771248,0.0000016036281,0.91177016,0.000004217913,0.0000136660165,0.00046027423,0.0000017350724,0.000011797923,0.000024048695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991103,0.000010955301,0.00033491934,0.0001844202,0.00014979028,0.00020960947],"domain_scores_gemma":[0.9995007,0.00009059077,0.00004478524,0.00025502295,0.000039048067,0.00006988601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003291515,0.00010274503,0.00016260473,0.000081868966,0.00001757309,0.000028871125,0.00032719027,0.000033697168,0.0000010372773],"category_scores_gemma":[0.000027631762,0.00009396407,0.000032078857,0.00025124685,0.00001708832,0.00010089673,0.000106207255,0.00005216837,0.000006648498],"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":[0.0000015729431,0.00020805854,0.000007588426,0.00053628645,0.000014438756,0.0000017124034,0.00051070424,0.97570753,0.00019014503,0.019446058,0.00005754466,0.0033183726],"study_design_scores_gemma":[0.0004827372,0.00004586621,0.000028146043,0.00013144691,0.000005792517,0.000005965129,0.000019209547,0.9889143,0.00006404378,0.00998648,0.00021135854,0.00010462707],"about_ca_topic_score_codex":8.022074e-7,"about_ca_topic_score_gemma":8.3824524e-8,"teacher_disagreement_score":0.08698516,"about_ca_system_score_codex":0.000035569617,"about_ca_system_score_gemma":0.000011376477,"threshold_uncertainty_score":0.38317436},"labels":[],"label_agreement":null},{"id":"W1603156618","doi":"10.1007/978-3-642-01181-8_26","title":"Beneficial Preadaptation in the Evolution of a 2D Agent Control System with Genetic Programming","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic programming; Computer science; Task (project management); Genetic algorithm; Control (management); Artificial intelligence; Simple (philosophy); Machine learning; Engineering","score_opus":0.009963257023095237,"score_gpt":0.21687443676763277,"score_spread":0.20691117974453754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603156618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041732125,0.00046428147,0.9970541,0.0004678355,0.00014730869,0.0009486667,0.0000037802577,0.000055811368,0.00044090018],"genre_scores_gemma":[0.7004236,0.00000460392,0.2992926,0.000077420234,0.00013686626,0.000041227348,0.0000022383776,0.0000073225074,0.000014155756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757844,0.000050641436,0.00045832075,0.00073596434,0.00083407975,0.00034252755],"domain_scores_gemma":[0.9984698,0.00018119613,0.0003248507,0.00075096154,0.00022443217,0.00004874922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006199106,0.0002634016,0.00029648916,0.00041938605,0.00018515933,0.00016890821,0.0016745724,0.00013493247,7.265357e-7],"category_scores_gemma":[0.000011288984,0.00018845923,0.000063907944,0.0007747173,0.00027457078,0.00025736645,0.00011775806,0.00033236615,0.0000030087413],"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":[0.000007743924,0.00006915059,0.00008358797,0.00004267977,0.000005995536,0.000022175127,0.001096541,0.27702975,0.00003775882,0.10927462,0.0000013103172,0.6123287],"study_design_scores_gemma":[0.00034078874,0.00033614077,0.005290859,0.00041202182,0.000012863213,0.00010048231,0.000004143538,0.97350353,0.000018908695,0.019577362,0.00013969623,0.00026322922],"about_ca_topic_score_codex":0.00013339584,"about_ca_topic_score_gemma":0.00027601875,"teacher_disagreement_score":0.70000625,"about_ca_system_score_codex":0.0004967979,"about_ca_system_score_gemma":0.00048175664,"threshold_uncertainty_score":0.76851445},"labels":[],"label_agreement":null},{"id":"W1603781187","doi":"10.1007/978-3-642-12242-2_11","title":"Using Code Bloat to Obfuscate Evolved Network Traffic","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Computer science; Genetic programming; Intrusion detection system; Code (set theory); Process (computing); Network packet; Port (circuit theory); Intrusion; Real-time computing; Computer security; Artificial intelligence; Programming language","score_opus":0.028522077278252726,"score_gpt":0.26617275926615164,"score_spread":0.2376506819878989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603781187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003964522,0.00026640168,0.9942177,0.0015819025,0.0020080744,0.0005540037,0.000009695918,0.00023174217,0.00073405233],"genre_scores_gemma":[0.028168455,0.000016534124,0.9685874,0.0015479667,0.0013271779,0.000018741677,0.0000058731866,0.00003799174,0.00028988827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960948,0.000023373581,0.0005114926,0.0016942649,0.0008144679,0.00086160353],"domain_scores_gemma":[0.9971027,0.0002809396,0.0002306076,0.0017433321,0.00030814734,0.00033431343],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069804816,0.0005143023,0.00046571429,0.00048522526,0.0006712179,0.0004893166,0.0038339982,0.00036765388,0.00002700073],"category_scores_gemma":[0.000036780446,0.0004994784,0.00014437154,0.0011263033,0.0005101418,0.00044291822,0.0013916765,0.0010372215,0.00010218255],"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":[0.0000025390195,0.000037167527,0.000014652625,0.00001099861,0.00000895367,0.000025145924,0.0003272094,0.75283355,0.00078277144,0.021609925,0.0000769538,0.22427012],"study_design_scores_gemma":[0.0001322061,0.00007008234,0.000107987544,0.00016731523,0.000008659204,0.00007388336,6.135231e-8,0.9069772,0.0002988242,0.08250093,0.009018408,0.0006444793],"about_ca_topic_score_codex":0.000016594848,"about_ca_topic_score_gemma":0.00015135639,"teacher_disagreement_score":0.22362565,"about_ca_system_score_codex":0.00025923538,"about_ca_system_score_gemma":0.0006485583,"threshold_uncertainty_score":0.99974567},"labels":[],"label_agreement":null},{"id":"W161833311","doi":"10.1142/9789812792464_0016","title":"NEW PROBLEMS OF PATTERN AVOIDANCE","year":2000,"lang":"en","type":"article","venue":"Developments in Language Theory","topic":"Evolutionary Algorithms and Applications","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":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.006026744003686442,"score_gpt":0.22835435823147676,"score_spread":0.22232761422779032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W161833311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15524326,0.00054416736,0.814861,0.00021983177,0.00007740153,0.0002894603,0.000004572711,0.00010811703,0.028652193],"genre_scores_gemma":[0.84257174,0.000028451483,0.15045641,0.0002474618,0.000027557862,0.000031725805,0.000006243576,0.0000072158086,0.0066231764],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99932647,0.000040740175,0.0001849453,0.0001788454,0.00012404213,0.00014493997],"domain_scores_gemma":[0.99960876,0.00004116597,0.00003831009,0.00026180674,0.000011780648,0.000038154707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023585543,0.00007064471,0.00008361094,0.000055615783,0.000031240546,0.000011657659,0.0005010807,0.000026789363,0.00036026794],"category_scores_gemma":[0.0000061933815,0.00006566061,0.000016265554,0.00028945995,0.000020800724,0.0001562184,0.00006406577,0.00006707394,0.00011357048],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018518938,0.000053649117,0.00088235753,0.000009787716,0.0000061649193,0.000006005291,0.006426719,0.00010216152,0.0003254375,0.032549433,0.00018766982,0.95944875],"study_design_scores_gemma":[0.0041455925,0.00013471067,0.61724156,0.0010907988,0.000010651965,0.000106301304,0.0012521574,0.009231119,0.019713791,0.29901552,0.04616449,0.0018932994],"about_ca_topic_score_codex":0.00005594644,"about_ca_topic_score_gemma":0.000012307337,"teacher_disagreement_score":0.9575555,"about_ca_system_score_codex":0.000023910483,"about_ca_system_score_gemma":0.00007368659,"threshold_uncertainty_score":0.39446813},"labels":[],"label_agreement":null},{"id":"W1630579982","doi":"10.1109/cec.2004.1331178","title":"Cascaded GP models for data mining","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; RSS; Genetic programming; Data mining; Context (archaeology); Machine learning; Cascade; Architecture; Sampling (signal processing); Basis (linear algebra); Scheme (mathematics); Artificial intelligence; Genetic algorithm; Mathematics; Engineering","score_opus":0.09785359205741352,"score_gpt":0.3123116548357792,"score_spread":0.2144580627783657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1630579982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019594729,0.00007377467,0.9868952,0.007408811,0.000030330228,0.00010974406,0.000011417554,0.000106731015,0.005168026],"genre_scores_gemma":[0.07110435,0.000005341209,0.9259903,0.00048507436,0.00012884825,0.000031801275,0.00002359319,0.0000028528189,0.002227833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946606,0.0000037743832,0.00009198902,0.00024870585,0.000070292794,0.000119189586],"domain_scores_gemma":[0.99917054,0.00005260466,0.000019134035,0.0006893368,0.000029189478,0.000039212246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010806515,0.00004228086,0.000042899093,0.00001989099,0.00011182112,0.00004173564,0.00094886834,0.000019085606,0.000009428603],"category_scores_gemma":[0.0000053661865,0.000037457525,0.00001489179,0.00009946922,0.000009578454,0.00084208185,0.0002749424,0.00002156482,0.000024009802],"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":[5.808917e-7,0.00005756561,0.0000055459527,0.0000016833662,0.0000063086427,1.8435134e-7,0.00015223838,0.0032017406,0.00009573142,0.66529584,0.07955793,0.25162464],"study_design_scores_gemma":[0.00009377408,0.0000059072977,0.000021963295,0.0000011357153,0.0000013705175,0.000004418942,0.000009884445,0.9296962,0.00013689717,0.0066867387,0.06328693,0.00005477974],"about_ca_topic_score_codex":0.00001190432,"about_ca_topic_score_gemma":0.00001201612,"teacher_disagreement_score":0.9264945,"about_ca_system_score_codex":0.000010444695,"about_ca_system_score_gemma":0.00003054091,"threshold_uncertainty_score":0.17632492},"labels":[],"label_agreement":null},{"id":"W1635873948","doi":"10.1007/978-3-540-74111-4_1","title":"Evolutionary Design in Biology","year":2008,"lang":"en","type":"book-chapter","venue":"Natural computing series","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Scope (computer science); Computer science; Data science; Modelling biological systems; Systems biology; Function (biology); Biology; Computational biology; Management science; Evolutionary biology; Engineering","score_opus":0.021813327264720194,"score_gpt":0.25701447610256356,"score_spread":0.23520114883784338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1635873948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022553069,0.04601665,0.7463095,0.0060473336,0.0038652113,0.0016132287,0.00004794017,0.0018245437,0.19405004],"genre_scores_gemma":[0.064258836,0.0013121776,0.75414014,0.00052408065,0.0009592345,0.000022100554,0.00017045405,0.00006904655,0.17854391],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842405,0.000047789974,0.00039020713,0.0006227621,0.00019187431,0.00032329513],"domain_scores_gemma":[0.99891984,0.00022420124,0.00019021261,0.00047661012,0.0001348366,0.000054301527],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014407795,0.00031261903,0.00033344398,0.00024163359,0.00030858067,0.000037306814,0.0009051001,0.00030647352,0.000013780659],"category_scores_gemma":[0.000023225128,0.0003092068,0.00010962717,0.00014718292,0.00020471055,0.00028910796,0.00046638073,0.0007127036,0.00009734387],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007444693,0.000020651534,0.000027402859,0.000011756144,0.000023163344,0.000043683944,0.0001766923,0.0007084749,0.000032851687,0.96884924,0.011312628,0.01878602],"study_design_scores_gemma":[0.0005170948,0.0002639253,0.003473737,0.00028956844,0.000012534996,0.0010342058,0.000009451284,0.13527486,0.000046775924,0.37143844,0.48614314,0.0014962781],"about_ca_topic_score_codex":0.000015196051,"about_ca_topic_score_gemma":0.0000046869127,"teacher_disagreement_score":0.5974108,"about_ca_system_score_codex":0.00017993558,"about_ca_system_score_gemma":0.00019628069,"threshold_uncertainty_score":0.999936},"labels":[],"label_agreement":null},{"id":"W1646992054","doi":"10.1109/cec.2004.1330834","title":"Evolution to the Xtreme: evolving evolutionary strategies using a meta-level approach","year":2004,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Metamodeling; Set (abstract data type); Evolutionary algorithm; Meta-analysis; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.09573218805477317,"score_gpt":0.28344053660922797,"score_spread":0.18770834855445478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1646992054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012435451,0.0008510006,0.9865684,0.004349497,0.00012453213,0.0003106417,0.0000099517865,0.00024926994,0.006293172],"genre_scores_gemma":[0.4594726,0.0000027547794,0.5397411,0.00022516951,0.0001166537,0.000110242734,0.000003886462,0.000007436415,0.00032012584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831676,0.000057447887,0.0002830673,0.00052535185,0.00044280328,0.00037454703],"domain_scores_gemma":[0.99874717,0.000047020996,0.00007422913,0.0008001366,0.00021029332,0.00012113497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034086476,0.00020091093,0.0001819091,0.00012915966,0.0008059769,0.00025262486,0.0010775966,0.000060930637,0.000030394916],"category_scores_gemma":[0.000024248377,0.0001349172,0.00016657902,0.0011756484,0.000070407885,0.001286924,0.00036066183,0.00015816519,0.00009649243],"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":[9.776268e-7,0.0001180358,0.00001918249,0.000004033839,0.00008093189,8.6797615e-7,0.0002637018,0.19147384,0.00068585726,0.80581063,0.0011901492,0.0003518102],"study_design_scores_gemma":[0.0003514268,0.00006267951,0.017320566,0.000012361828,0.00017487934,0.00018053199,0.0010893192,0.85754764,0.00010700174,0.11830442,0.004340484,0.0005086789],"about_ca_topic_score_codex":0.0007500677,"about_ca_topic_score_gemma":0.00004356886,"teacher_disagreement_score":0.6875062,"about_ca_system_score_codex":0.00032799988,"about_ca_system_score_gemma":0.0004580824,"threshold_uncertainty_score":0.6199004},"labels":[],"label_agreement":null},{"id":"W1648737833","doi":"","title":"The effects of randomly sampled training data on program evolution","year":2000,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Sampling (signal processing); Computer science; Machine learning; Resampling; Evolutionary algorithm; Sample (material); Training (meteorology); Population; Artificial intelligence; Simple random sample; Sampling bias; Genetic algorithm; Statistics; Sample size determination; Mathematical optimization; Mathematics","score_opus":0.02617905632834657,"score_gpt":0.2858960940716449,"score_spread":0.25971703774329835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1648737833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018354194,0.0004814837,0.9574553,0.0040692375,0.0002325226,0.0015311718,0.000010021107,0.000490048,0.017376028],"genre_scores_gemma":[0.87406284,0.00010123847,0.123105206,0.00013174038,0.00011546194,0.00021606935,0.000026249181,0.0000071279483,0.0022340815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992723,0.000045009056,0.000139862,0.00022279442,0.00017286523,0.0001471933],"domain_scores_gemma":[0.99848026,0.00053966785,0.00003556619,0.0008850312,0.00002593245,0.000033536693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026371647,0.000059600734,0.00007456491,0.000017601917,0.00024040324,0.000044835346,0.0010007363,0.000020725454,0.000014389732],"category_scores_gemma":[0.00005295449,0.000037327907,0.000027693743,0.00025639017,0.000053655658,0.00020394106,0.0000830895,0.000057605714,0.000029887078],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009384502,0.00011360549,0.00001000662,0.0000044765775,0.000009845269,3.134493e-7,0.000094535295,0.00004529333,0.00009530668,0.16580099,0.0021928342,0.83162344],"study_design_scores_gemma":[0.0024589866,0.0005364729,0.016049815,0.000051140036,0.000016509613,0.000012161515,0.000046849807,0.79457915,0.00035413032,0.02912257,0.15654217,0.00023006917],"about_ca_topic_score_codex":0.000033912573,"about_ca_topic_score_gemma":0.000008487789,"teacher_disagreement_score":0.8557086,"about_ca_system_score_codex":0.000012566938,"about_ca_system_score_gemma":0.00004564523,"threshold_uncertainty_score":0.18596336},"labels":[],"label_agreement":null},{"id":"W1653679933","doi":"10.1007/978-3-642-01129-0_50","title":"Fractal Evolver: Interactive Evolutionary Design of Fractals with Grid Computing","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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 Calgary","funders":"","keywords":"Fractal; Computer science; Grid; Theoretical computer science; Computer graphics (images); Artificial intelligence; Mathematics; Geometry","score_opus":0.013385776957813351,"score_gpt":0.24353608157132556,"score_spread":0.2301503046135122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1653679933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060449256,0.0005142695,0.9953711,0.0005148935,0.00048832194,0.00062032015,0.000014272587,0.00014530386,0.0022710687],"genre_scores_gemma":[0.17262179,0.000035848923,0.82629555,0.00043122593,0.00044089544,0.0000087616545,0.000014024241,0.00002627725,0.00012563421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961322,0.000055768192,0.00066940964,0.0014390295,0.0011196883,0.0005838818],"domain_scores_gemma":[0.9964279,0.00096655666,0.0006690873,0.0011959305,0.00057131913,0.00016920616],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005998346,0.00055269274,0.0006232618,0.00075704284,0.00035324285,0.00018329016,0.0026692776,0.0002638585,0.000018599432],"category_scores_gemma":[0.000051116906,0.00048614657,0.00013324546,0.00086141273,0.0007967227,0.0010764772,0.00078561634,0.0009220071,0.00002543395],"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":[0.000033928354,0.00021666849,0.00009410652,0.00002853833,0.00004783689,0.000066879715,0.00080872304,0.37975138,0.00031329773,0.014285463,0.0002076914,0.60414547],"study_design_scores_gemma":[0.00032322627,0.00077806093,0.0011838798,0.00059454306,0.000014926664,0.00021642902,6.3290474e-7,0.9170326,0.0004914907,0.077958174,0.0007276647,0.0006783476],"about_ca_topic_score_codex":0.00003011238,"about_ca_topic_score_gemma":0.0000064389246,"teacher_disagreement_score":0.6034671,"about_ca_system_score_codex":0.00039037832,"about_ca_system_score_gemma":0.0009111917,"threshold_uncertainty_score":0.999759},"labels":[],"label_agreement":null},{"id":"W1669835938","doi":"10.1109/ccece.2001.933572","title":"Circuit synthesis evolution using a hardware-based genetic algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Minification; Algorithm; Genetic algorithm; Logic synthesis; Logic gate; Function (biology); Scheme (mathematics); Circuit design; High-level synthesis; Computer architecture; Computer hardware; Computer engineering; Field-programmable gate array; Embedded system; Machine learning; Mathematics; Programming language","score_opus":0.03473544719703539,"score_gpt":0.22753398650064705,"score_spread":0.19279853930361165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1669835938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086400786,0.000288265,0.99542093,0.00061498233,0.00010100752,0.00014861888,0.0000062633794,0.00028168218,0.0022742536],"genre_scores_gemma":[0.26428333,0.000007233228,0.7349257,0.00018056434,0.00010000932,0.00006204775,9.0332486e-7,0.0000097217335,0.000430489],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988285,0.000042696487,0.00020297494,0.00039139163,0.0002598145,0.00027465256],"domain_scores_gemma":[0.99908996,0.000086687265,0.000060102702,0.0005685054,0.00008854208,0.00010623585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008895909,0.00012615697,0.00011222325,0.00012188642,0.00029145915,0.000092414455,0.0005331547,0.00005766519,0.00030791748],"category_scores_gemma":[0.000019032666,0.00012490668,0.000080605816,0.00057483587,0.000045339493,0.00028204953,0.000072141025,0.00007312975,0.00028877365],"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":[9.1521184e-7,0.000925127,0.0009306201,0.000027130083,0.000055400164,0.000035058474,0.00018593368,0.019777397,0.003155726,0.12744261,0.005665801,0.8417983],"study_design_scores_gemma":[0.00010559409,0.000014714037,0.0028609086,0.0000101274545,0.000009437143,0.000029412255,0.000006402946,0.9913338,0.00044972033,0.0030562931,0.0019480172,0.00017558016],"about_ca_topic_score_codex":0.000090061905,"about_ca_topic_score_gemma":0.0000014735441,"teacher_disagreement_score":0.9715564,"about_ca_system_score_codex":0.00014735256,"about_ca_system_score_gemma":0.000048383517,"threshold_uncertainty_score":0.50935465},"labels":[],"label_agreement":null},{"id":"W168149749","doi":"10.1007/978-0-387-71921-4_11","title":"Adaptive Control of Genetic Parameters for Dynamic Combinatorial Problems","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","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 Waterloo","funders":"","keywords":"Travelling salesman problem; Mathematical optimization; Population; Scheme (mathematics); Selection (genetic algorithm); Genetic algorithm; Computer science; Diversity (politics); Quality control and genetic algorithms; Adaptive mutation; Evolutionary algorithm; Space (punctuation); Mathematics; Algorithm; Artificial intelligence; Meta-optimization","score_opus":0.02221182163259981,"score_gpt":0.2421803554486267,"score_spread":0.2199685338160269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W168149749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003329019,0.0002985384,0.94446,0.00012383632,0.00038786692,0.0011671899,0.00007241945,0.00006679954,0.05342007],"genre_scores_gemma":[0.03912446,0.00009394767,0.84415907,0.00021410706,0.0001832736,0.0003199959,0.00004903473,0.000064197426,0.11579192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871725,0.000006468205,0.00041608038,0.0004142786,0.00024051659,0.00020538592],"domain_scores_gemma":[0.9986837,0.0002407933,0.00028135162,0.00047625753,0.0002434267,0.000074498916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001462684,0.00021204022,0.00031744147,0.000119733115,0.00007374087,0.000020457988,0.0006070135,0.0002064788,0.00001548913],"category_scores_gemma":[0.000005310769,0.00020038823,0.00019218412,0.000043410477,0.00010977194,0.0000723111,0.00007334299,0.00013391646,0.000018486027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007936949,0.00003399258,8.6469663e-7,0.000016539561,0.000062348256,7.1761764e-7,0.000020962609,0.00036325149,0.000008848592,0.988132,0.00025250253,0.011100017],"study_design_scores_gemma":[0.0011006654,0.00046011602,0.00005264546,0.000049900424,0.00004988716,0.0000068897425,0.0000032524847,0.30449134,0.0000130399185,0.67327386,0.020167643,0.00033076396],"about_ca_topic_score_codex":0.000016047263,"about_ca_topic_score_gemma":0.000008512268,"teacher_disagreement_score":0.31485817,"about_ca_system_score_codex":0.00006897362,"about_ca_system_score_gemma":0.00011179246,"threshold_uncertainty_score":0.8171594},"labels":[],"label_agreement":null},{"id":"W1683124040","doi":"10.1109/iscas.1999.780157","title":"Constrained circuit optimization via library table genetic algorithms","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Genetic algorithm; Table (database); Set (abstract data type); Algorithm; Construct (python library); Electronic circuit; Power (physics); Function (biology); Mathematical optimization; Engineering; Mathematics; Electrical engineering; Data mining","score_opus":0.011407921226530247,"score_gpt":0.2040170388630176,"score_spread":0.19260911763648736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1683124040","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000592009,0.00013533067,0.9469753,0.00047500615,0.0001101616,0.0001649025,0.0000032258013,0.00031235404,0.051764525],"genre_scores_gemma":[0.03269201,0.000036265006,0.96365595,0.00046385385,0.00004505676,0.00004645902,0.000013215235,0.000010551812,0.0030366166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909914,0.00003656496,0.00018569866,0.00031564888,0.00014636663,0.0002165601],"domain_scores_gemma":[0.9993609,0.000038072023,0.000046441095,0.00040738488,0.000041607567,0.00010559542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066138535,0.00010195079,0.00008667973,0.000060730235,0.00017833718,0.00012411117,0.00038059457,0.000049718154,0.0007052346],"category_scores_gemma":[0.000008250286,0.00009883631,0.000032971027,0.00060979405,0.00004544078,0.0006890152,0.000057757294,0.000062355415,0.00009702821],"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":[3.4970728e-7,0.00017567333,0.0003548604,0.000005536652,0.000016183576,0.000008012337,0.00006280002,0.07118736,0.00043396346,0.8971256,0.004563039,0.026066627],"study_design_scores_gemma":[0.0002236468,0.000026942522,0.00037818658,0.0000028266218,0.0000034817865,0.00007790607,0.000011993448,0.9597953,0.0010101027,0.023881018,0.01440291,0.00018571097],"about_ca_topic_score_codex":0.000006666687,"about_ca_topic_score_gemma":2.0194649e-7,"teacher_disagreement_score":0.8886079,"about_ca_system_score_codex":0.000013633941,"about_ca_system_score_gemma":0.00012717287,"threshold_uncertainty_score":0.77218246},"labels":[],"label_agreement":null},{"id":"W168342931","doi":"10.1142/9781860949838_0005","title":"MOBILE GENETIC ELEMENTS AND THEIR PREDICTION","year":2008,"lang":"en","type":"book-chapter","venue":"Series on advances in bioinformatics and computational biology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC; Georgia Cancer Coalition","keywords":"Computer science","score_opus":0.007581123519058414,"score_gpt":0.23083640102546074,"score_spread":0.22325527750640234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W168342931","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014570117,0.032193128,0.8109182,0.0005821156,0.0008406346,0.0017011134,0.0010857559,0.00025958166,0.15096246],"genre_scores_gemma":[0.015093791,0.15140098,0.8206547,0.0012298705,0.00025301537,0.00028752134,0.0012125521,0.00003937885,0.009828194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991418,0.000008356396,0.00040203743,0.0002323167,0.00008376857,0.0001317163],"domain_scores_gemma":[0.9994878,0.00007679003,0.0001839992,0.00014811613,0.000056775698,0.0000465156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050516694,0.00019193784,0.00019259924,0.00014042422,0.00015330287,0.000026456939,0.00017378059,0.00011647761,0.0000057740535],"category_scores_gemma":[0.0000031115774,0.00016168982,0.00002554588,0.000043940672,0.00022408404,0.0003151147,0.00014503472,0.00013944348,0.000009330816],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000927808,0.000024570358,0.00013417355,0.000042724485,0.000018350627,0.0000014208404,0.00048453966,0.00448982,0.0000010742737,0.6372311,0.00018772086,0.35737523],"study_design_scores_gemma":[0.0002913511,0.0006501356,0.0007255286,0.000052010637,0.0000023969444,0.000112949456,0.00004014727,0.1926742,0.0000016373567,0.36495593,0.44025272,0.00024097822],"about_ca_topic_score_codex":5.946275e-7,"about_ca_topic_score_gemma":0.000002128285,"teacher_disagreement_score":0.44006503,"about_ca_system_score_codex":0.00002709085,"about_ca_system_score_gemma":0.00004124805,"threshold_uncertainty_score":0.65935194},"labels":[],"label_agreement":null},{"id":"W1695361088","doi":"10.1002/cpe.2976","title":"Special issue on parallel architectures and bioinspired algorithm: guest editors message","year":2012,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Dependability; Heuristics; Architecture; Fault tolerance; Set (abstract data type); Distributed computing; Parallel computing; Software engineering; Programming language","score_opus":0.017421624073587008,"score_gpt":0.3081765115453007,"score_spread":0.2907548874717137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1695361088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057851322,0.0046955906,0.9087139,0.00830436,0.010307758,0.0006574463,0.000021126376,0.000259766,0.009188733],"genre_scores_gemma":[0.8001873,0.0013059862,0.16113682,0.0017916372,0.035197128,0.00017322275,0.000023227489,0.000016864138,0.00016784068],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895394,0.000080578946,0.00019027207,0.00035164965,0.00019707473,0.00022647822],"domain_scores_gemma":[0.9991277,0.00036265844,0.00012000587,0.00014395693,0.000063946914,0.00018172039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001773846,0.00015146252,0.00012370618,0.00006505494,0.00043236287,0.00016512995,0.00014590033,0.00005377805,0.000008791895],"category_scores_gemma":[0.00008361877,0.00013822418,0.000016468324,0.00018897586,0.00018390638,0.0010776395,0.00012520266,0.00014810158,0.000014544109],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013182918,0.00031931332,0.0005708365,0.000016809385,0.000014411389,0.00000542535,0.019890616,0.000058357575,0.000037253376,0.0223723,0.010177358,0.94652414],"study_design_scores_gemma":[0.0008245286,0.00033973335,0.016437644,0.000044826553,0.000024869276,0.00023659412,0.0027783765,0.05352275,0.00016067325,0.002174252,0.9228872,0.0005685709],"about_ca_topic_score_codex":0.000016689926,"about_ca_topic_score_gemma":4.042493e-7,"teacher_disagreement_score":0.9459556,"about_ca_system_score_codex":0.0000093553,"about_ca_system_score_gemma":0.000025141035,"threshold_uncertainty_score":0.5636618},"labels":[],"label_agreement":null},{"id":"W1707777926","doi":"10.1007/978-3-540-37256-1_107","title":"Benchmarking a Recurrent Linear GP Model on Prediction and Control Problems","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in control and information sciences","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Chaotic; Linear programming; Genetic programming; Computer science; Benchmarking; Series (stratigraphy); Algorithm; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.012484282819386164,"score_gpt":0.22421690544209125,"score_spread":0.2117326226227051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1707777926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047747057,0.0006948344,0.9748159,0.0021755565,0.0001472197,0.0005630833,0.00008574774,0.000061628954,0.021408265],"genre_scores_gemma":[0.9677998,0.0008918501,0.026265668,0.003773631,0.00044010408,0.00019073872,0.00009368894,0.000013399177,0.0005311124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985447,0.000016344638,0.00047854928,0.00033728848,0.00041279025,0.00021034962],"domain_scores_gemma":[0.9991209,0.0002403942,0.00028819308,0.00018048726,0.000108370696,0.00006166141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004862173,0.00022917613,0.00024241571,0.00033745353,0.0003611126,0.000261157,0.00027906397,0.00018852766,0.000003652742],"category_scores_gemma":[0.000038132883,0.00018053455,0.000044267625,0.00013317027,0.0002032455,0.001543695,0.000054772292,0.00030004076,0.0000052844844],"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":[0.000019069705,0.000026587271,0.00014583122,0.000056318764,0.000016348313,4.914251e-7,0.00049630174,0.44907692,0.000016624841,0.24257594,0.00022976163,0.3073398],"study_design_scores_gemma":[0.00062397926,0.0002002702,0.00032978304,0.00011721182,0.000008942944,0.000008088896,9.749969e-7,0.93431175,0.0000022809754,0.054175615,0.010044464,0.0001766626],"about_ca_topic_score_codex":0.000023619443,"about_ca_topic_score_gemma":0.000019649777,"teacher_disagreement_score":0.96775204,"about_ca_system_score_codex":0.00004619863,"about_ca_system_score_gemma":0.000103979895,"threshold_uncertainty_score":0.73619854},"labels":[],"label_agreement":null},{"id":"W173198799","doi":"10.20381/ruor-13147","title":"Abstraction-based genetic programming","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Programming language; Genetic programming; Theoretical computer science; Recursion (computer science); Abstraction; Set (abstract data type); Context (archaeology); Answer set programming; Isomorphism (crystallography); Representation (politics); Artificial intelligence","score_opus":0.009099976824973684,"score_gpt":0.26304351049099634,"score_spread":0.2539435336660226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W173198799","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00254465,0.00046879626,0.9513944,0.00089219253,0.0006127068,0.0006020111,0.0000024787098,0.0007713212,0.042711493],"genre_scores_gemma":[0.046646487,0.00002407759,0.9312456,0.00026524274,0.00024261704,0.0002367235,0.00047382744,0.000020032421,0.020845355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898124,0.0000097268085,0.0002173456,0.0003773004,0.00023242677,0.00018196153],"domain_scores_gemma":[0.99923974,0.000030374786,0.00012299432,0.00042716492,0.000111859146,0.00006788503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000046883473,0.00015357387,0.0001112483,0.00010658989,0.00017485672,0.00014238154,0.0005165281,0.00013791892,0.00004243502],"category_scores_gemma":[0.0000052707937,0.0001510199,0.00008647992,0.00028612668,0.000007439742,0.00012787562,0.0000078058065,0.00016971164,0.00016455447],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017174641,0.00019489281,0.0000179449,0.00002596153,0.000010196817,0.0000058650126,0.00004603414,0.0006465968,0.000108333916,0.017697418,0.0023672094,0.97887784],"study_design_scores_gemma":[0.00070390967,0.00031146992,0.32177964,0.00018378711,0.00007702334,0.000029493134,0.00020187884,0.33051202,0.0033083688,0.024545392,0.3164242,0.0019228237],"about_ca_topic_score_codex":0.00005870906,"about_ca_topic_score_gemma":0.000095680734,"teacher_disagreement_score":0.976955,"about_ca_system_score_codex":0.00003982253,"about_ca_system_score_gemma":0.00021141216,"threshold_uncertainty_score":0.61584127},"labels":[],"label_agreement":null},{"id":"W1809875586","doi":"10.1109/cec.2016.7744088","title":"Feature selection and classification using age layered population structure genetic programming","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Feature selection; Artificial intelligence; Genetic programming; Computer science; Pattern recognition (psychology); Feature (linguistics); Benchmark (surveying); Population; Evolutionary algorithm; Heuristic; Machine learning; Selection (genetic algorithm); Curse of dimensionality; Dimensionality reduction; Evolutionary computation","score_opus":0.020938800777026895,"score_gpt":0.259406753321989,"score_spread":0.2384679525449621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1809875586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22986352,0.000035353212,0.76849747,0.0012638193,0.000041414336,0.00015469211,0.0000015050157,0.000116787094,0.00002541323],"genre_scores_gemma":[0.6431012,0.0000046182568,0.3566503,0.000018009408,0.00005229377,0.000006785681,0.0000033320534,0.0000033821582,0.00016009231],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99939543,0.000023225906,0.00008598903,0.0002657552,0.00010715408,0.00012245431],"domain_scores_gemma":[0.9996661,0.000017075072,0.00005861889,0.00016351715,0.00004974261,0.00004493905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039575614,0.00007440378,0.000055236673,0.000054630953,0.00019195204,0.000086254426,0.0001142193,0.00006540539,0.0000053582316],"category_scores_gemma":[0.000007132594,0.000051409515,0.000015221235,0.00025954455,0.000015193186,0.00035367985,0.000038587834,0.00004366453,0.000002426202],"study_design_candidate":"observational","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":[0.000002691021,0.000035185098,0.031207552,0.000011504598,0.00001267622,0.0000012362905,0.000094083734,0.00023120639,0.23125309,0.09337754,0.0002889448,0.6434843],"study_design_scores_gemma":[0.00020256912,0.000026280752,0.7144278,0.000014409748,0.0000063231864,0.000062260275,0.000008992461,0.27285516,0.0006542861,0.008474973,0.0031086414,0.0001582947],"about_ca_topic_score_codex":0.000028228731,"about_ca_topic_score_gemma":0.00004611815,"teacher_disagreement_score":0.68322027,"about_ca_system_score_codex":0.000054281794,"about_ca_system_score_gemma":0.000015235392,"threshold_uncertainty_score":0.20964192},"labels":[],"label_agreement":null},{"id":"W181710077","doi":"","title":"Distributed Beagle: An Environment For Parallel And Distributed Evolutionary Computations","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Scalability; Workstation; Parallel computing; Distributed computing; Evolutionary computation; Speedup; Computation; Evolutionary algorithm; Human-based evolutionary computation; Interactive evolutionary computation; Evolutionary programming; Artificial intelligence; Algorithm; Operating system","score_opus":0.01591409546536706,"score_gpt":0.2416505097667037,"score_spread":0.22573641430133665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181710077","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001132624,0.0001230814,0.995628,0.0020845602,0.000053025087,0.00040213723,0.00032528286,0.00013816945,0.000113117756],"genre_scores_gemma":[0.29167995,0.000029356603,0.7068464,0.00012746507,0.000028073286,0.00025386363,0.00084307085,0.000006910206,0.00018489681],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989974,0.0000431234,0.00020268405,0.00039784235,0.00013158882,0.00022738543],"domain_scores_gemma":[0.99926436,0.00011862603,0.00005272743,0.0003503489,0.000052417316,0.00016152396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117137635,0.000121423625,0.0001059854,0.000033491804,0.00045714297,0.000064699045,0.00024165296,0.000048714825,0.000023820534],"category_scores_gemma":[0.00001901617,0.0001039829,0.00003971723,0.00017750794,0.00007930442,0.00039438801,0.00006546256,0.000055624918,0.000014744489],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025813358,0.00038960055,0.00079703867,0.0000057934944,0.000018718856,8.727911e-7,0.000048351565,0.025860885,0.00004864907,0.9649951,0.0053658336,0.0024665801],"study_design_scores_gemma":[0.0007123384,0.00013392612,0.045326166,0.000003251138,0.000012780596,0.000027790154,0.00006409522,0.7189486,0.000022619952,0.082019724,0.15245084,0.00027785957],"about_ca_topic_score_codex":0.0000097689735,"about_ca_topic_score_gemma":0.0000020093185,"teacher_disagreement_score":0.8829754,"about_ca_system_score_codex":0.000055767454,"about_ca_system_score_gemma":0.00004812703,"threshold_uncertainty_score":0.42402998},"labels":[],"label_agreement":null},{"id":"W1829038701","doi":"10.3233/kes-2005-9302","title":"Teaching while selecting images for satellite-based forest mapping","year":2005,"lang":"en","type":"article","venue":"International Journal of Knowledge-based and Intelligent Engineering Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Infectious Diseases Society of America","keywords":"Computer science; Process (computing); Satellite; Remote sensing; Task (project management); Artificial intelligence; Computer vision; Satellite imagery; Geography; Systems engineering; Engineering","score_opus":0.016572973756417253,"score_gpt":0.25750030465232676,"score_spread":0.2409273308959095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1829038701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005311919,0.0029104075,0.98890144,0.0012603357,0.0011445585,0.00017593433,0.00000746851,0.0000686316,0.00021929387],"genre_scores_gemma":[0.9009316,0.00002742714,0.09750956,0.000049698512,0.0013013852,0.000028975615,0.0000055384676,0.000017053875,0.00012873161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986768,0.000033181772,0.000614861,0.00020317161,0.00026343396,0.00020858276],"domain_scores_gemma":[0.99839723,0.00040783346,0.0002768893,0.00013993825,0.0006599657,0.00011813877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007369973,0.00016769729,0.00020771599,0.00042074954,0.00011720128,0.0002458869,0.0006398946,0.000054932367,0.0000019682277],"category_scores_gemma":[0.00011930442,0.000155477,0.00014200008,0.00014086506,0.000018053375,0.0003972244,0.000044714503,0.00022372065,0.0000058367386],"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":[0.00002967496,0.00041495688,0.0023769797,0.00026207961,0.00025681814,0.000010648708,0.0009756112,0.7886317,0.005942391,0.05931552,0.001308264,0.14047539],"study_design_scores_gemma":[0.0003794823,0.00007922133,0.0003435589,0.00037995013,0.000008417035,0.000066265435,0.000049384136,0.8328543,0.0032221323,0.000054292686,0.16240478,0.00015828318],"about_ca_topic_score_codex":0.000013125225,"about_ca_topic_score_gemma":0.000001865815,"teacher_disagreement_score":0.8956197,"about_ca_system_score_codex":0.00020686796,"about_ca_system_score_gemma":0.00013247144,"threshold_uncertainty_score":0.63401675},"labels":[],"label_agreement":null},{"id":"W1838202668","doi":"10.1109/cec.2001.934267","title":"Supervised and unsupervised data mining with an evolutionary algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Evolutionary algorithm; Artificial intelligence; Machine learning; Data mining","score_opus":0.04660514755467084,"score_gpt":0.24783260409642197,"score_spread":0.20122745654175112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1838202668","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011215775,0.0007160035,0.981092,0.0031046285,0.000046904395,0.00021217056,0.000040741557,0.00037754004,0.0031942336],"genre_scores_gemma":[0.035294298,0.00007649675,0.96340656,0.00034638093,0.000088027475,0.00002313983,0.000074652424,0.000010365291,0.0006801043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986885,0.00003660593,0.00015413233,0.0006452399,0.00023578723,0.00023971534],"domain_scores_gemma":[0.9983288,0.00005731054,0.000028332683,0.0013389755,0.00006959155,0.00017701411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001170969,0.00013659184,0.000112146,0.00006986002,0.0003465252,0.000118548705,0.0011106023,0.00004268737,0.00015335203],"category_scores_gemma":[0.0000053096774,0.00011132623,0.000011700821,0.00039878362,0.00009523808,0.00182592,0.00044846593,0.000078197,0.00003678679],"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":[0.000004762474,0.0006914204,0.0023027556,0.00001301662,0.00005023821,0.000035359488,0.0009904462,0.000065670865,0.00014445196,0.037250992,0.015123461,0.9433274],"study_design_scores_gemma":[0.00040300319,0.00012594822,0.006274029,0.0000064764913,0.0000062688896,0.00011633451,0.00013157472,0.98689055,0.000008046068,0.0005309568,0.005310472,0.00019634077],"about_ca_topic_score_codex":0.00004712988,"about_ca_topic_score_gemma":0.000008262189,"teacher_disagreement_score":0.98682487,"about_ca_system_score_codex":0.0000151623,"about_ca_system_score_gemma":0.00002780202,"threshold_uncertainty_score":0.4539752},"labels":[],"label_agreement":null},{"id":"W184907052","doi":"10.1007/978-3-642-13950-5_11","title":"Interday and Intraday Stock Trading Using Probabilistic Adaptive Mapping Developmental Genetic Programming and Linear Genetic Programming","year":2010,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Converse; Implementation; Profit (economics); Probabilistic logic; Computer science; Linear programming; Stock market; Trading strategy; Econometrics; Mathematical optimization; Context (archaeology); Economics; Artificial intelligence; Microeconomics; Mathematics; Algorithm; Biology; Programming language","score_opus":0.1039516950835222,"score_gpt":0.32443604244877194,"score_spread":0.22048434736524974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W184907052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003626849,0.005637794,0.98843676,0.00013227906,0.00031371057,0.0012357739,0.000010074476,0.00009896564,0.000507779],"genre_scores_gemma":[0.09711997,0.00030085628,0.9018427,0.000038575057,0.00015581926,0.00012888452,0.000008250618,0.00003437071,0.00037058064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737215,0.00003658964,0.0008034011,0.0010064049,0.00038865075,0.00039281687],"domain_scores_gemma":[0.99850386,0.0005548383,0.00030722332,0.00020069108,0.00031239598,0.00012098038],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027692682,0.00047385818,0.00046738758,0.00032851935,0.00050491886,0.00013513338,0.00046759823,0.00017735345,0.000005115508],"category_scores_gemma":[0.00006516646,0.0004951806,0.0000619123,0.00022174782,0.0008680799,0.00020992379,0.000798019,0.00065090717,0.0000044596486],"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":[0.000011170595,0.000114369526,0.00037313567,0.00041744727,0.0003374685,0.00011471628,0.011344905,0.017301975,0.00003066435,0.1757929,0.000011918944,0.79414934],"study_design_scores_gemma":[0.00013626619,0.00015397744,0.0008637872,0.001031638,0.000039088784,0.0006204224,0.0007253451,0.7791611,0.000013582485,0.21430427,0.0021279224,0.0008226066],"about_ca_topic_score_codex":0.000015471795,"about_ca_topic_score_gemma":0.000035978777,"teacher_disagreement_score":0.79332674,"about_ca_system_score_codex":0.0002950233,"about_ca_system_score_gemma":0.00022779209,"threshold_uncertainty_score":0.99974996},"labels":[],"label_agreement":null},{"id":"W186113963","doi":"10.1007/978-3-540-75771-9_1","title":"An Introduction to Evolutionary Computation in Practice","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"","keywords":"Framing (construction); Software deployment; Computation; Evolutionary computation; Curriculum; Computer science; Perception; Broad spectrum; Management science; Data science; Engineering; Sociology; Artificial intelligence; Psychology; Software engineering; Pedagogy; Algorithm","score_opus":0.0640269993333057,"score_gpt":0.37032116278951666,"score_spread":0.30629416345621097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W186113963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027022155,0.002972393,0.96657485,0.013017089,0.0010444684,0.0008143791,0.00002228236,0.00016570381,0.015361788],"genre_scores_gemma":[0.03090789,0.0027636553,0.95517427,0.0013554312,0.0014757093,0.0002745092,0.00026457763,0.00006277408,0.0077211573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967613,0.00009725964,0.00092515413,0.0011860771,0.0007099595,0.00032023067],"domain_scores_gemma":[0.9972538,0.0008510299,0.00029931427,0.0004915624,0.0009903339,0.000113971386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046044882,0.0003886415,0.00042995808,0.00081036944,0.00025205023,0.00004889338,0.00079709094,0.00017268902,0.0000141230485],"category_scores_gemma":[0.00022036016,0.00045216377,0.00007743822,0.00060838,0.00027233778,0.0009533772,0.00036889414,0.0005507909,0.0002436295],"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":[0.00001189065,0.00012815863,0.000008352218,0.000013497943,0.00003196419,0.000043098837,0.0012620992,0.5806781,6.715129e-7,0.39606896,0.0064065745,0.015346627],"study_design_scores_gemma":[0.00017175438,0.00035173417,0.0009037956,0.00021892982,0.00001673321,0.0003574582,0.000493008,0.49155003,0.0000050981243,0.4116167,0.093417384,0.00089736783],"about_ca_topic_score_codex":0.000029714342,"about_ca_topic_score_gemma":0.000040298557,"teacher_disagreement_score":0.08912806,"about_ca_system_score_codex":0.00087255007,"about_ca_system_score_gemma":0.000280509,"threshold_uncertainty_score":0.999793},"labels":[],"label_agreement":null},{"id":"W1861569832","doi":"","title":"An Examination of Lamarckian Genetic Algorithms","year":2001,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Travelling salesman problem; Memetic algorithm; Genetic algorithm; Mathematical optimization; Algorithm; Computer science; Sequence (biology); Set (abstract data type); Artificial neural network; Artificial intelligence; Tree (set theory); Mathematics; Combinatorics","score_opus":0.015259292698691206,"score_gpt":0.2555793886270595,"score_spread":0.24032009592836828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1861569832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06426826,0.000033847115,0.9283275,0.00033733688,0.00003611714,0.000074299176,9.23241e-7,0.00008366609,0.006838044],"genre_scores_gemma":[0.669761,0.000021315978,0.32973957,0.000045911485,0.000037608217,0.0000113088645,0.0000031317977,0.0000024165477,0.00037775448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994207,0.000027732962,0.00013155003,0.00017856376,0.00014006923,0.00010140855],"domain_scores_gemma":[0.999427,0.000019091674,0.00003855144,0.0003816926,0.00007952194,0.000054108594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010958586,0.00005003675,0.000054387467,0.00005911505,0.000059790043,0.000022543169,0.000413098,0.000025199683,0.00005303845],"category_scores_gemma":[0.0000029533594,0.000046408008,0.000020021276,0.0003407024,0.000026370666,0.0003041394,0.00004056873,0.000028949657,0.000025612486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.0250842e-7,0.00024470215,0.0011187952,0.0000026575012,0.000004361018,0.0000033295519,0.00022079014,0.0003624892,0.0036044738,0.15055826,0.00015458919,0.84372514],"study_design_scores_gemma":[0.00012030728,0.000086052576,0.43267804,0.0000022272236,0.0000018122679,0.000025501135,0.00003372251,0.5581478,0.0011166049,0.0049728076,0.0027253167,0.00008978278],"about_ca_topic_score_codex":0.000030330943,"about_ca_topic_score_gemma":0.0000062423064,"teacher_disagreement_score":0.8436354,"about_ca_system_score_codex":0.000011638265,"about_ca_system_score_gemma":0.00002002506,"threshold_uncertainty_score":0.18924637},"labels":[],"label_agreement":null},{"id":"W1878709465","doi":"","title":"Multi-combinative strategy to avoid premature convergence in genetically-generated fuzzy knowledge bases","year":2004,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Premature convergence; Crossover; Construct (python library); Computer science; Genetic algorithm; Fuzzy logic; Convergence (economics); Variation (astronomy); Process (computing); Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","score_opus":0.015070095822042473,"score_gpt":0.2561472285141072,"score_spread":0.24107713269206474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1878709465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08809167,0.0011311948,0.9042561,0.0044166176,0.000111613444,0.0010549466,0.000035201127,0.0005389484,0.00036372532],"genre_scores_gemma":[0.626873,0.000077719356,0.3711668,0.00076891255,0.000042939264,0.00069792056,0.000014377819,0.000022557077,0.00033575055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975785,0.000136167,0.00049884326,0.00078606553,0.00027976217,0.0007206548],"domain_scores_gemma":[0.9981707,0.00008549295,0.00011519262,0.00093925186,0.0002652957,0.00042411126],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039011927,0.00034783082,0.00031404148,0.0004273911,0.00025907828,0.00018382665,0.0012686347,0.00023903514,0.000014643449],"category_scores_gemma":[0.00010839767,0.00035598528,0.00009960625,0.0018882696,0.00008830431,0.00047716068,0.00041699837,0.00044755716,0.000099615136],"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":[0.00003952553,0.0035235595,0.0037607984,0.000064771586,0.000051010153,0.00013990438,0.002054075,0.14439134,0.0847555,0.7407876,0.0019717661,0.01846009],"study_design_scores_gemma":[0.0020763571,0.00059932785,0.15117456,0.00020134653,0.000016053376,0.00012827903,0.00016917891,0.7395778,0.06841986,0.03512863,0.0012872186,0.0012213899],"about_ca_topic_score_codex":0.0024412265,"about_ca_topic_score_gemma":0.0034760928,"teacher_disagreement_score":0.70565903,"about_ca_system_score_codex":0.00056235533,"about_ca_system_score_gemma":0.00059696834,"threshold_uncertainty_score":0.9998892},"labels":[],"label_agreement":null},{"id":"W1885006205","doi":"10.1109/ecnn.2000.886217","title":"Evolving neural networks using attribute grammars","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Grammatical evolution; Computer science; Rule-based machine translation; Parsing; ENCODE; Artificial intelligence; Artificial neural network; Grammar; Representation (politics); Selection (genetic algorithm); Parse tree; Genetic representation; Grammar induction; Genetic algorithm; Theoretical computer science; Genetic programming; Natural language processing; Machine learning; Linguistics; Biology","score_opus":0.03823191381024699,"score_gpt":0.24518472435489044,"score_spread":0.20695281054464346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885006205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026117214,0.00036700023,0.99448913,0.0011492032,0.00015770775,0.00006975645,5.56379e-7,0.000191454,0.00096348],"genre_scores_gemma":[0.76979613,0.000014317877,0.22903757,0.00031601815,0.00013663244,0.0000068860472,0.0000014665882,0.000004956863,0.0006860222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928546,0.00001697716,0.00012582795,0.00022158683,0.00011444723,0.0002356929],"domain_scores_gemma":[0.99947983,0.00003771379,0.000033714263,0.00032519858,0.000049449503,0.0000741098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006759088,0.000075095035,0.000066409004,0.000034158755,0.00024973255,0.000110430956,0.0004093611,0.00003258824,0.00012150799],"category_scores_gemma":[0.0000058456208,0.000069535374,0.000044325825,0.00040764178,0.000023350802,0.0004678429,0.00015362515,0.00008824481,0.000035037276],"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":[7.73749e-7,0.00036245267,0.009029181,0.00000806945,0.00004154641,0.000036700563,0.00028334509,0.3804962,0.00049972464,0.41597012,0.0513793,0.14189257],"study_design_scores_gemma":[0.00006442355,0.000008416102,0.0013826959,0.0000018807541,0.000001918864,0.00002937788,0.000004409084,0.9964021,0.000007314372,0.0007871071,0.0012154719,0.00009488739],"about_ca_topic_score_codex":0.000034754838,"about_ca_topic_score_gemma":0.0000023778293,"teacher_disagreement_score":0.76718444,"about_ca_system_score_codex":0.000031035703,"about_ca_system_score_gemma":0.000004461551,"threshold_uncertainty_score":0.28355703},"labels":[],"label_agreement":null},{"id":"W1889739364","doi":"","title":"Improving evolutionary algorithms by means of an adaptive parameter control approach","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Benchmark (surveying); Estimator; Path (computing); Computer science; Evolutionary algorithm; Population; Mathematical optimization; Premature convergence; Convergence (economics); Process (computing); Outcome (game theory); Diversity (politics); Machine learning; Mathematics; Genetic algorithm","score_opus":0.015443279152457539,"score_gpt":0.23070298693750257,"score_spread":0.21525970778504502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1889739364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017727369,0.00042270456,0.99452424,0.00014943669,0.000085100255,0.0002608904,0.000039887425,0.00012487947,0.0026201506],"genre_scores_gemma":[0.48007137,0.0000020398404,0.5194722,0.000101644466,0.00007456042,0.00006623903,0.0000141316905,0.0000058626515,0.000191984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987392,0.000075655356,0.00025333161,0.0003117245,0.0002698538,0.00035024012],"domain_scores_gemma":[0.9989706,0.000111998765,0.00011433199,0.00050652865,0.00011141306,0.00018507974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000283475,0.0001364051,0.00016538239,0.00005922579,0.00014091913,0.000023089216,0.00056098687,0.0000710573,0.000020562664],"category_scores_gemma":[0.000014590889,0.000117838914,0.00007195744,0.00027241386,0.000089286186,0.0013212776,0.00010551071,0.0001109856,0.000017705363],"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":[0.000037904094,0.0050750365,0.002108346,0.000033482294,0.00019128634,0.0000011776596,0.0013313292,0.0016815322,0.0101344995,0.74931145,0.01161605,0.21847792],"study_design_scores_gemma":[0.0003648419,0.00012801401,0.0030864107,0.0000018680724,0.000011619795,0.000019258563,0.00011899987,0.9931039,0.00057511416,0.0015427307,0.00086616335,0.00018108368],"about_ca_topic_score_codex":0.00016021299,"about_ca_topic_score_gemma":6.5483414e-7,"teacher_disagreement_score":0.99142236,"about_ca_system_score_codex":0.00004568191,"about_ca_system_score_gemma":0.000046502908,"threshold_uncertainty_score":0.48053315},"labels":[],"label_agreement":null},{"id":"W1889811593","doi":"10.1007/978-3-540-24855-2_67","title":"On Multi-class Classification by Way of Niching","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Population; Artificial intelligence; Classifier (UML); Machine learning; Class (philosophy); Niche; Context (archaeology); Data mining; Theoretical computer science","score_opus":0.021663388428801823,"score_gpt":0.25738467567569745,"score_spread":0.23572128724689562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1889811593","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008969552,0.00021203457,0.9954236,0.0012509377,0.0004845821,0.00031250986,0.000014989957,0.00009548845,0.002116152],"genre_scores_gemma":[0.39356205,0.00005630335,0.60489464,0.00079655135,0.00016310856,0.000021973417,0.000020859277,0.00002985777,0.00045465294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972618,0.000019901601,0.00048235402,0.0011407972,0.0007340329,0.00036111224],"domain_scores_gemma":[0.99780095,0.0003197957,0.0003546539,0.0012207057,0.00019143752,0.00011248322],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004372671,0.0003349994,0.00032715223,0.00043771093,0.00023910054,0.00013969539,0.0024140913,0.0002374108,0.000012321736],"category_scores_gemma":[0.000046362406,0.00031197479,0.00010340457,0.00051174714,0.0004959577,0.00036136873,0.0004531954,0.0005791159,0.000038101738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002619101,0.00017321878,0.0000116128695,0.000028669518,0.000009394776,0.0000055288465,0.0003740946,0.0636203,0.0018896618,0.6025292,0.00009078629,0.3312649],"study_design_scores_gemma":[0.0002960857,0.0001263788,0.00024049167,0.00027129505,0.0000041630183,0.000010404273,1.3353694e-7,0.7464512,0.0017992809,0.24959537,0.000815642,0.00038957584],"about_ca_topic_score_codex":0.000025134737,"about_ca_topic_score_gemma":0.000012646088,"teacher_disagreement_score":0.68283087,"about_ca_system_score_codex":0.0003668494,"about_ca_system_score_gemma":0.00038818814,"threshold_uncertainty_score":0.99993324},"labels":[],"label_agreement":null},{"id":"W1894411465","doi":"10.1109/cec.2004.1330988","title":"Automated selection of vision operator libraries with evolutionary algorithms","year":2004,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Operator (biology); Interpretation (philosophy); Domain (mathematical analysis); Artificial intelligence; Process (computing); Image (mathematics); Machine learning; Principal (computer security); Bitwise operation; Algorithm; Mathematics; Programming language","score_opus":0.0068483659293806995,"score_gpt":0.23345032510898817,"score_spread":0.22660195917960746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1894411465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015617739,0.00009393381,0.9797643,0.0018882223,0.00005337063,0.00018743619,0.000004294357,0.0010357004,0.0013550004],"genre_scores_gemma":[0.39555338,0.0000070814917,0.60414714,0.00008173174,0.000024162124,0.000024729614,0.000007914238,0.000005676926,0.00014816955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991513,0.000017081256,0.00018283284,0.00026867207,0.00022862255,0.00015147653],"domain_scores_gemma":[0.9994466,0.000021769078,0.000060520837,0.000246333,0.00016072344,0.00006408793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005853732,0.00010068706,0.00010487763,0.00009096928,0.00019939592,0.00005576748,0.00032314667,0.000047791098,0.000029273562],"category_scores_gemma":[0.000004432698,0.000076997065,0.000028961009,0.00085464027,0.000080416845,0.0011235725,0.00009287718,0.00006624201,0.000029833202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011194588,0.0005130055,0.0007529186,0.000013945644,0.0000335629,0.000003506401,0.00020920124,0.016548943,0.007326001,0.96825254,0.003153113,0.0031820824],"study_design_scores_gemma":[0.0010553045,0.0006984593,0.06420566,0.000052181673,0.000009475991,0.00013921296,0.000057690464,0.8949696,0.020455174,0.015155441,0.0028720156,0.00032979302],"about_ca_topic_score_codex":0.00007826368,"about_ca_topic_score_gemma":0.000005144159,"teacher_disagreement_score":0.9530971,"about_ca_system_score_codex":0.000051033672,"about_ca_system_score_gemma":0.00026447678,"threshold_uncertainty_score":0.31398493},"labels":[],"label_agreement":null},{"id":"W1902126040","doi":"10.1109/isvlsi.2015.101","title":"A Statistical Approach to Probe Chaos from Noise in Analog and Mixed Signal Designs","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Chaotic; Lyapunov exponent; Computer science; Electronic circuit; Noise (video); Analogue electronics; Electronic engineering; Control theory (sociology); Realization (probability); Mixed-signal integrated circuit; Algorithm; Mathematics; Artificial intelligence; Engineering; Statistics; Electrical engineering","score_opus":0.05570957554000015,"score_gpt":0.2662923692584652,"score_spread":0.21058279371846506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902126040","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010284579,0.00003037484,0.98604715,0.00072196603,0.000017733577,0.00023820641,0.000014518587,0.00004846275,0.0025970016],"genre_scores_gemma":[0.41765043,6.399382e-7,0.5820255,0.00014262821,0.000023336017,0.00007053708,0.000009493289,0.000002341077,0.00007507115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920535,0.00004118737,0.00012992874,0.00031942135,0.00014819289,0.00015589184],"domain_scores_gemma":[0.9994641,0.00006968486,0.000015401925,0.00019222559,0.00004017045,0.00021840307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016803232,0.00007312824,0.00009480024,0.0000592485,0.000037798778,0.00005811692,0.00024953915,0.000030460196,0.000007873399],"category_scores_gemma":[0.000016985565,0.00006287712,0.000008405333,0.00028344296,0.000029720995,0.0001524568,0.0001136944,0.00006317296,0.00003681521],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015285881,0.0007664915,0.0061790124,0.000006394105,0.000012957078,0.000011947392,0.0018995185,0.0011366742,0.0015602257,0.9445889,0.012943064,0.0308795],"study_design_scores_gemma":[0.00062568136,0.0001352576,0.09250823,0.0000063872944,0.0000041421586,0.000011038071,0.00016945631,0.8235259,0.00034591032,0.08104456,0.0013687642,0.0002546475],"about_ca_topic_score_codex":0.00035494423,"about_ca_topic_score_gemma":0.000031456635,"teacher_disagreement_score":0.86354434,"about_ca_system_score_codex":0.00002883184,"about_ca_system_score_gemma":0.000074984746,"threshold_uncertainty_score":0.25640544},"labels":[],"label_agreement":null},{"id":"W1903488793","doi":"10.1109/tkde.2015.2453952","title":"A Cooperative Coevolution Framework for Parallel Learning to Rank","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Shandong Province; Academy of Finland; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Rank (graph theory); Coevolution; Context (archaeology); Learning to rank; Artificial intelligence; Divide and conquer algorithms; Machine learning; Evolutionary algorithm; Function (biology); Theoretical computer science; Algorithm; Ranking (information retrieval); Mathematics","score_opus":0.04245486072800539,"score_gpt":0.3010015428149192,"score_spread":0.2585466820869138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1903488793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034731583,0.0002902785,0.99804926,0.00040595434,0.00032601386,0.00028223702,0.0000564966,0.00018665829,0.000055763136],"genre_scores_gemma":[0.40165687,0.0000495786,0.59757715,0.000048496782,0.00012660692,0.00023619572,0.000027939444,0.00001550963,0.0002616546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992255,0.000015708285,0.00013240722,0.00036695783,0.0000796946,0.00017973766],"domain_scores_gemma":[0.9990847,0.0001798882,0.000016701482,0.00043670216,0.000102424776,0.0001795725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019768465,0.00011699357,0.00011276936,0.00010200036,0.00020364986,0.0000692002,0.0003838449,0.000056438963,0.0000019485576],"category_scores_gemma":[0.0000313116,0.000119791606,0.000022263874,0.00034150874,0.00001224223,0.0004422978,0.000013603342,0.00017792944,0.00004380043],"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":[0.0000350104,0.00027796143,0.000005276499,0.00004096758,0.00006426608,0.0000013750993,0.0027747718,0.873185,0.0004935424,0.061732028,0.0036700177,0.05771983],"study_design_scores_gemma":[0.00033424754,0.00013934875,0.000032669745,0.000038373062,0.00001070206,0.0000072349835,0.00006150585,0.96183693,0.00025827726,0.0004653873,0.036647227,0.00016811176],"about_ca_topic_score_codex":0.0000063326083,"about_ca_topic_score_gemma":0.000005948708,"teacher_disagreement_score":0.40130955,"about_ca_system_score_codex":0.00004257848,"about_ca_system_score_gemma":0.00005732877,"threshold_uncertainty_score":0.488496},"labels":[],"label_agreement":null},{"id":"W1910269293","doi":"10.1109/cibcb.2015.7300296","title":"Interactive evolution instead of default parameters","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Casual; Human–computer interaction; Software; Task (project management); Simple (philosophy); User interface; Interface (matter); Control (management); Distributed computing; Artificial intelligence; Programming language; Operating system","score_opus":0.02947235008307265,"score_gpt":0.27189821693683247,"score_spread":0.24242586685375983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910269293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031724356,0.0000365813,0.95566857,0.0005918011,0.00011778736,0.00007921851,0.0000013661801,0.00006929324,0.0117110135],"genre_scores_gemma":[0.8041514,8.872474e-7,0.19553523,0.00004816925,0.000010390141,0.000012201399,0.0000013576292,0.0000016174946,0.00023870623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948317,0.000021970365,0.00012685628,0.00014473782,0.0001374522,0.00008582422],"domain_scores_gemma":[0.9994254,0.00004266442,0.000057530215,0.0002611712,0.00014691436,0.00006632017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010531639,0.000048056543,0.0000647584,0.000054363325,0.000026782618,0.00001406091,0.00030428698,0.000022794178,0.0000033061206],"category_scores_gemma":[0.000031679563,0.000041600968,0.00002946619,0.0002719622,0.000034131972,0.00043064513,0.000103623235,0.000046573907,0.0000614354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008301828,0.00028193305,0.0016152428,0.0000046522937,0.000025187268,0.0000013149343,0.0010750099,0.0024146207,0.0009408519,0.9599215,0.007935407,0.025776021],"study_design_scores_gemma":[0.00082351064,0.00030059353,0.016849441,0.000023007622,0.00000934015,0.00003717028,0.0010284755,0.80004555,0.006013734,0.16740292,0.007161181,0.0003050407],"about_ca_topic_score_codex":0.00020302304,"about_ca_topic_score_gemma":0.000007672387,"teacher_disagreement_score":0.79763097,"about_ca_system_score_codex":0.00006486319,"about_ca_system_score_gemma":0.000075354015,"threshold_uncertainty_score":0.16964382},"labels":[],"label_agreement":null},{"id":"W1914928883","doi":"10.1109/incos.2015.54","title":"Energy Availability Forecasting for Harvesting-aware Wireless Sensor Networks: Analysis of Energy Demand of a Predictor Based on Evolutionary Fuzzy Rules","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"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; Wireless sensor network; Fuzzy logic; Efficient energy use; Energy (signal processing); Energy harvesting; Energy management; Evolutionary algorithm; Reliability (semiconductor); Microcontroller; Reliability engineering; Real-time computing; Embedded system; Distributed computing; Power (physics); Engineering; Artificial intelligence; Computer network","score_opus":0.031021737575516045,"score_gpt":0.23970205845565487,"score_spread":0.20868032088013883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1914928883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018373664,0.00009009963,0.9802691,0.00018343769,0.00006861158,0.00012646813,0.00013188398,0.00008743312,0.0006693042],"genre_scores_gemma":[0.8571437,0.0000040801256,0.14216328,0.00007834331,0.00008173829,0.00010285052,0.00013658649,0.000009246774,0.0002801579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823105,0.00010301644,0.0005618735,0.0004864659,0.00035743232,0.0002601479],"domain_scores_gemma":[0.99743056,0.0007572501,0.00033306834,0.0006416947,0.00067436585,0.00016307674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045381096,0.00017109734,0.00038548792,0.00026974457,0.00012794469,0.000021928827,0.00047707613,0.000093595045,0.000008104642],"category_scores_gemma":[0.000096543336,0.0001528689,0.0002288041,0.0011928906,0.00012376359,0.00022346209,0.00012141285,0.000049259725,3.776923e-7],"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":[0.000072861665,0.00073896116,0.015321617,0.000046925667,0.00031855388,0.0000012775049,0.000066552784,0.8550526,0.00006781418,0.1165205,0.0026026603,0.009189694],"study_design_scores_gemma":[0.00038388793,0.0001866396,0.0059244307,0.000028114615,0.000119121236,0.0000014658175,0.000024612642,0.99000067,0.00019497809,0.0025622007,0.00042620781,0.00014767883],"about_ca_topic_score_codex":0.00045817113,"about_ca_topic_score_gemma":0.0001234377,"teacher_disagreement_score":0.83877003,"about_ca_system_score_codex":0.000078117315,"about_ca_system_score_gemma":0.00021459527,"threshold_uncertainty_score":0.6233813},"labels":[],"label_agreement":null},{"id":"W192439236","doi":"10.1007/978-3-642-41888-4_16","title":"Fitness Morphs and Nonlinear Projections of Agent-Case Embeddings to Characterize Fitness Landscapes","year":2013,"lang":"en","type":"book-chapter","venue":"Emergence, complexity and computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Guelph","funders":"","keywords":"Fitness landscape; Cellular automaton; Set (abstract data type); Euclidean space; Computer science; Metric (unit); Fitness approximation; Representation (politics); Artificial intelligence; Theoretical computer science; Mathematics; Machine learning; Fitness function; Genetic algorithm; Combinatorics","score_opus":0.05144254749948868,"score_gpt":0.28126428168967466,"score_spread":0.22982173419018598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W192439236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081498615,0.00041861518,0.8964607,0.0021611124,0.0007977677,0.0019666045,0.00039038443,0.0003035676,0.01600265],"genre_scores_gemma":[0.3584187,0.0010369543,0.56919676,0.00058983144,0.0010690489,0.0005507079,0.001185426,0.00014172502,0.067810856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998362,0.00002865713,0.0005109854,0.0006285567,0.00026034028,0.00020945621],"domain_scores_gemma":[0.99880725,0.00006933787,0.00031114303,0.00031119396,0.000327923,0.0001731536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014122677,0.0003065168,0.00038297355,0.00025513896,0.00042449735,0.00010694249,0.00028702256,0.00014385846,0.00017223596],"category_scores_gemma":[0.000008722632,0.0003162675,0.000079790196,0.00020820025,0.00014429244,0.00037596657,0.0003550486,0.00019771674,0.000051427076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002247247,0.00028167403,0.00012248312,0.00045269876,0.00019377333,0.00006378914,0.002625479,0.0018464797,0.00026844474,0.8973499,0.0057466044,0.091026194],"study_design_scores_gemma":[0.0005550187,0.00035978542,0.0070087914,0.00028048674,0.000091283255,0.0007518662,0.00015331124,0.8310593,0.000046994675,0.13377404,0.024809979,0.001109167],"about_ca_topic_score_codex":0.00019016046,"about_ca_topic_score_gemma":0.000042977917,"teacher_disagreement_score":0.8292128,"about_ca_system_score_codex":0.000017840732,"about_ca_system_score_gemma":0.000056832258,"threshold_uncertainty_score":0.99992895},"labels":[],"label_agreement":null},{"id":"W193823973","doi":"","title":"Analysis of a restricted test case set for a sorting network genetic algorithm","year":2007,"lang":"en","type":"article","venue":"Conference on Artificial Intelligence for Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sorting; Sorting network; Measure (data warehouse); Computer science; Genetic algorithm; Set (abstract data type); Sorting algorithm; Evolutionary algorithm; Test (biology); Quality (philosophy); Algorithm; Machine learning; Artificial intelligence; Data mining","score_opus":0.08506752857658785,"score_gpt":0.35452879918198543,"score_spread":0.2694612706053976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W193823973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097615493,0.000033933007,0.9961634,0.00043022272,0.00006562798,0.0017060513,0.0002798453,0.000114050505,0.00023069832],"genre_scores_gemma":[0.48555323,0.000014022535,0.5122895,0.000067242865,0.00017664522,0.0017228108,0.00010425222,0.00001218564,0.0000600644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783266,0.000017327398,0.0008952594,0.00061053486,0.00018618247,0.00045803803],"domain_scores_gemma":[0.99675,0.0011649869,0.00039939245,0.00078186084,0.0007497733,0.00015400985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000503683,0.00018358782,0.00029732715,0.00034916124,0.0005639219,0.000094425675,0.00070822175,0.00010501626,0.000015217661],"category_scores_gemma":[0.00014009942,0.0001941179,0.00023486374,0.0030148495,0.00013483419,0.00011009094,0.000075358286,0.00010975131,0.000014242843],"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":[0.000007106577,0.00024175015,0.000058341353,0.000008774467,0.00007951499,0.000002506055,0.00018562618,0.0075311125,0.00039742305,0.55103314,0.00008733877,0.44036737],"study_design_scores_gemma":[0.000033735945,0.00015811544,0.00044313478,0.000009274723,0.00016900785,0.000014215147,0.00022372587,0.86744565,0.0018832653,0.12792362,0.001495461,0.00020079505],"about_ca_topic_score_codex":0.000056137163,"about_ca_topic_score_gemma":0.00013442054,"teacher_disagreement_score":0.85991454,"about_ca_system_score_codex":0.00005224967,"about_ca_system_score_gemma":0.00014416913,"threshold_uncertainty_score":0.79158986},"labels":[],"label_agreement":null},{"id":"W1950070584","doi":"10.1007/978-3-540-78671-9_25","title":"Cooperative Problem Decomposition in Pareto Competitive Classifier Models of Coevolution","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Genetic programming; Pareto principle; Classifier (UML); Artificial intelligence; Coevolution; Modularity (biology); Population; Machine learning; Mathematical optimization; Pareto optimal; Multi-objective optimization; Mathematics","score_opus":0.019881005426592532,"score_gpt":0.25393094741623956,"score_spread":0.23404994198964701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1950070584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001706764,0.00045859267,0.9887011,0.00053644105,0.0002500584,0.0006620557,0.000022814605,0.00006738034,0.0091309],"genre_scores_gemma":[0.45955706,0.00027734923,0.53949773,0.00027446105,0.00013058387,0.00005566844,0.000033229146,0.000023039202,0.00015086899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685496,0.00005650675,0.00068434863,0.0011709643,0.00078811526,0.00044512303],"domain_scores_gemma":[0.9979511,0.00028770257,0.00033943742,0.00074847805,0.00056407606,0.000109205204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042704996,0.00039260928,0.0005169738,0.00073643867,0.0002398825,0.00008907126,0.0015188834,0.00030188993,0.0000068372015],"category_scores_gemma":[0.000013931069,0.00038681904,0.00010045755,0.00089934055,0.00090382935,0.0009133962,0.0005899739,0.00068369025,0.000013449456],"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":[0.000011488412,0.00019013199,0.000073211355,0.000036942896,0.000013945424,0.000039559003,0.002091733,0.49244714,0.00033243175,0.43916687,0.000083688756,0.065512836],"study_design_scores_gemma":[0.00030332236,0.00015431787,0.00045403247,0.00039416205,0.0000036538866,0.000060444912,8.022824e-7,0.8164641,0.000422977,0.18106288,0.00028279994,0.00039647357],"about_ca_topic_score_codex":0.00006944139,"about_ca_topic_score_gemma":0.0002082159,"teacher_disagreement_score":0.45938638,"about_ca_system_score_codex":0.00051455485,"about_ca_system_score_gemma":0.00075714674,"threshold_uncertainty_score":0.9998584},"labels":[],"label_agreement":null},{"id":"W1956399340","doi":"","title":"Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system.","year":2005,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Feature selection; Genetic programming; Artificial intelligence; Pattern recognition (psychology); Discriminant; Class (philosophy); Computer science; Linear discriminant analysis; Selection (genetic algorithm); Domain (mathematical analysis); Object (grammar); Feature (linguistics); Machine learning; Mathematics","score_opus":0.0923641545439538,"score_gpt":0.4554306298013764,"score_spread":0.3630664752574226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956399340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1990224,0.011110288,0.78599584,0.00087775575,0.000653186,0.0015532593,0.000033702043,0.00027998802,0.00047358297],"genre_scores_gemma":[0.9305857,0.0009819822,0.06723312,0.00008591641,0.00029982338,0.00037160248,0.000010623441,0.00003983312,0.0003914219],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9967993,0.0002834215,0.00073391414,0.000771424,0.0008861176,0.00052582263],"domain_scores_gemma":[0.99792063,0.00006776447,0.00075051526,0.0005933293,0.00036376872,0.0003040145],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008966681,0.000368843,0.0004986086,0.0005303373,0.00070735055,0.0017739991,0.00300909,0.00019213876,0.00011390723],"category_scores_gemma":[0.00004296799,0.00032834164,0.00020450848,0.0016113044,0.00008312684,0.002580788,0.00082497735,0.0005363729,0.00002023757],"study_design_candidate":"observational","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":[0.00009264626,0.0018691376,0.014467617,0.0002372025,0.0003095148,0.00003413779,0.0009202303,0.0022677516,0.4169693,0.00083616097,0.024857692,0.53713864],"study_design_scores_gemma":[0.0021977755,0.00011364884,0.6501362,0.0005924493,0.00018038841,0.00072584284,0.000508192,0.10787055,0.1233493,0.0017212525,0.110938184,0.0016662306],"about_ca_topic_score_codex":0.0009331072,"about_ca_topic_score_gemma":0.0002613531,"teacher_disagreement_score":0.73156327,"about_ca_system_score_codex":0.00054552255,"about_ca_system_score_gemma":0.00021508413,"threshold_uncertainty_score":0.99991685},"labels":[],"label_agreement":null},{"id":"W1961525806","doi":"10.1109/cec.2004.1331176","title":"Maintaining diversity and increasing the accuracy of classification rules through automatic speciation","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic algorithm; Computer science; Subspecies; Artificial intelligence; Machine learning; Diversity (politics); Class (philosophy); Set (abstract data type); Data mining; Biology; Evolutionary biology; Ecology","score_opus":0.03590550162497821,"score_gpt":0.2680311957703238,"score_spread":0.2321256941453456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1961525806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4067488,0.000053105963,0.58264464,0.0069768014,0.000016401276,0.00010825464,0.0000010550093,0.00006424495,0.0033866623],"genre_scores_gemma":[0.8125512,0.000037245325,0.18721342,0.000117281175,0.000047938556,0.0000035814314,0.0000016235401,0.0000011691066,0.000026554802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995011,0.000044168864,0.00013147961,0.00012316015,0.00012650351,0.00007355567],"domain_scores_gemma":[0.9993152,0.00029755506,0.00011024721,0.00021497413,0.00004546622,0.00001655225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027704483,0.000044686127,0.000051769275,0.000020713083,0.0006197827,0.000032914086,0.0002652019,0.000019003604,0.00001335642],"category_scores_gemma":[0.00004989809,0.00003244769,0.000017215227,0.00013606162,0.000048786824,0.0006493568,0.00039888365,0.000042564952,0.000009593231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.2469027e-7,0.0000367966,0.004802138,0.0000034399877,0.0000063788907,7.048398e-8,0.0020804454,0.000074595744,0.0002771398,0.9180658,0.00017165816,0.07448083],"study_design_scores_gemma":[0.000083783394,0.000007484737,0.43231505,0.0000071809886,0.0000048816437,0.0000070384544,0.0002700338,0.54680634,0.00014333702,0.019232469,0.0010741876,0.000048216647],"about_ca_topic_score_codex":0.00011000181,"about_ca_topic_score_gemma":0.000008238416,"teacher_disagreement_score":0.89883333,"about_ca_system_score_codex":0.000032943244,"about_ca_system_score_gemma":0.00001667166,"threshold_uncertainty_score":0.47669303},"labels":[],"label_agreement":null},{"id":"W1963955771","doi":"10.1142/s146902680300080x","title":"MAPPING REFERENCE CODE TO IRREGULAR DSPS WITHIN THE RETARGETABLE, OPTIMIZING COMPILER COGEN(T)","year":2003,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Unreachable code; Dead code; Compiler; Parallel computing; Code generation; Programming language; Code (set theory); Object code; Redundant code; Set (abstract data type); Schedule; Abstraction; Dead code elimination; Operating system","score_opus":0.041280316975775395,"score_gpt":0.30289987243902566,"score_spread":0.2616195554632503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963955771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020058,0.0004564524,0.98956126,0.006161309,0.00024523662,0.00028668862,0.000019502266,0.000030218316,0.0012335348],"genre_scores_gemma":[0.6318509,0.00009693958,0.36641598,0.0012257618,0.00017867188,0.00007958747,0.000012729985,0.000009825909,0.00012960266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997993,0.00008774608,0.0007693459,0.00030368892,0.000665873,0.00018034854],"domain_scores_gemma":[0.9973167,0.00041999304,0.0004577429,0.00027912995,0.0013558591,0.000170561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073249143,0.00016501472,0.00018153542,0.00024889616,0.00036350178,0.00027460905,0.0014339392,0.00005088123,0.000028562423],"category_scores_gemma":[0.000083468214,0.00013199328,0.00008646372,0.00061214244,0.00012940497,0.0004848761,0.00016155672,0.00028288364,0.000055175948],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046153637,0.00009435235,0.00010367248,0.0000027551516,0.00007029041,0.0000034027973,0.0004838975,0.25516263,0.00016716757,0.73311627,0.00049476937,0.010296211],"study_design_scores_gemma":[0.00032867462,0.00011585153,0.002542392,0.00011328595,0.000026584285,0.001173061,0.0012723624,0.16803034,0.002159324,0.5890368,0.23471971,0.00048159657],"about_ca_topic_score_codex":0.0000067768697,"about_ca_topic_score_gemma":0.0000027380743,"teacher_disagreement_score":0.6298451,"about_ca_system_score_codex":0.000074105556,"about_ca_system_score_gemma":0.00023363293,"threshold_uncertainty_score":0.53825295},"labels":[],"label_agreement":null},{"id":"W1964029556","doi":"10.2316/journal.201.2010.3.201-2025","title":"CONTROL OF CHAOS BY OPTIMAL DESIGN OF DC–DC CONVERTER USING GENETIC ALGORITHM","year":2010,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"CHAOS (operating system); Converters; Control theory (sociology); Chaotic; Genetic algorithm; Power (physics); Computer science; Mode (computer interface); Control (management); Algorithm; Physics; Artificial intelligence; Machine learning","score_opus":0.015436938168782742,"score_gpt":0.23248775239229316,"score_spread":0.2170508142235104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964029556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005053888,0.002264975,0.99135923,0.00010590617,0.0004432235,0.0006709347,0.00005412434,0.000026866448,0.00002084823],"genre_scores_gemma":[0.95942986,0.000049004426,0.04022034,0.000047983576,0.000112113725,0.000059309572,0.000002127814,0.000009498502,0.00006977057],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986342,0.00009005145,0.00053905154,0.0002989522,0.00021536171,0.000222338],"domain_scores_gemma":[0.99883956,0.00016580235,0.00026875563,0.00038319762,0.00022845187,0.00011420699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032475695,0.00015796204,0.00037880434,0.000075537435,0.00008617361,0.000047996153,0.00040720816,0.00009862406,0.000011205729],"category_scores_gemma":[0.000014396429,0.00013512309,0.000072499046,0.00012696859,0.00014410481,0.00015793079,0.00004286518,0.00012147796,0.0000071587897],"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":[0.000079976264,0.00081598177,0.0013871016,0.00024678998,0.00056678517,0.00000979581,0.0009810253,0.026591394,0.78924674,0.049363792,0.0014741834,0.12923643],"study_design_scores_gemma":[0.0007120489,0.00013610274,0.00014913664,0.000028058117,0.000029646086,0.00004166438,0.00003649362,0.9929817,0.0038688297,0.00011207591,0.0017647769,0.00013948679],"about_ca_topic_score_codex":0.00022998404,"about_ca_topic_score_gemma":3.7987783e-7,"teacher_disagreement_score":0.9663903,"about_ca_system_score_codex":0.000013298729,"about_ca_system_score_gemma":0.00006236466,"threshold_uncertainty_score":0.551016},"labels":[],"label_agreement":null},{"id":"W1967703293","doi":"10.1007/s10710-006-9015-5","title":"Estimation of evolvability genetic algorithm and dynamic environments","year":2006,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"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 Guelph","funders":"","keywords":"Evolvability; Computer science; Genetic algorithm; Evolutionary computation; Computation; Evolutionary algorithm; Population; Selection (genetic algorithm); Artificial intelligence; Algorithm; Mathematical optimization; Machine learning; Evolutionary biology; Mathematics; Biology","score_opus":0.003496496500966776,"score_gpt":0.2117898430544903,"score_spread":0.20829334655352352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967703293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20054032,0.0031167099,0.7958115,0.00015724971,0.000039541883,0.00023698562,0.0000061171363,0.000054752472,0.000036824233],"genre_scores_gemma":[0.4306864,0.00006686903,0.5690642,0.0000063050766,0.000016157395,0.00004342039,0.000008754015,0.0000056406125,0.000102228965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988772,0.000035035406,0.00028738682,0.00039930348,0.00017586694,0.00022520707],"domain_scores_gemma":[0.99943197,0.000042408235,0.0001012777,0.00033662145,0.000021138405,0.00006660986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013504516,0.00014945336,0.00015805426,0.00006563512,0.00019437305,0.00007275307,0.00019864098,0.000053218995,0.000003619712],"category_scores_gemma":[0.000007643582,0.00014032882,0.000031836305,0.00018966074,0.00015782734,0.00013095027,0.00016110213,0.00006311375,0.000003185429],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.865852e-7,0.0001164457,0.006535518,0.0000340284,0.0000078125895,0.0000010598079,0.0000512984,0.005245726,0.00022297405,0.00039810233,0.000019053563,0.987367],"study_design_scores_gemma":[0.00017302799,0.00005977155,0.2360298,0.000009008869,0.000012781367,0.000027901942,0.0000059469376,0.7506626,0.000056591587,0.0122409,0.00060562143,0.00011602242],"about_ca_topic_score_codex":0.00036233693,"about_ca_topic_score_gemma":0.0000072608823,"teacher_disagreement_score":0.987251,"about_ca_system_score_codex":0.000021010506,"about_ca_system_score_gemma":0.000019326968,"threshold_uncertainty_score":0.5722443},"labels":[],"label_agreement":null},{"id":"W1967797406","doi":"10.1109/cig.2014.6932910","title":"Comparing the structure of probabilistic 4- and 8-state finite transducer representations for Prisoner's Dilemma","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Pairwise comparison; Probabilistic logic; Multidimensional scaling; Embedding; Computer science; Representation (politics); Cluster analysis; Theoretical computer science; Euclidean distance; Euclidean geometry; Mathematics; Algorithm; Artificial intelligence; Machine learning","score_opus":0.020896703124361874,"score_gpt":0.2579016915020231,"score_spread":0.2370049883776612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967797406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08694216,0.000014911982,0.9106629,0.001738615,0.000029924666,0.00030905736,0.00001062808,0.000030203004,0.0002616413],"genre_scores_gemma":[0.91177475,0.0000021425626,0.08800363,0.00005327281,0.000021109074,0.000025722395,0.0000045064157,0.00000280895,0.000112055066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994876,0.000024351153,0.00014689355,0.00017399361,0.000075923504,0.000091252026],"domain_scores_gemma":[0.9991969,0.00036011194,0.0000501376,0.00029010323,0.00007446022,0.000028296214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009430365,0.000054125787,0.000079095444,0.000020399804,0.00016160295,0.000040767576,0.00025324302,0.000014073633,0.0000044570497],"category_scores_gemma":[0.000036914313,0.000035305377,0.000025934996,0.00013234252,0.000069527305,0.00012529071,0.00004809254,0.000041626077,5.773468e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031798359,0.000040813175,0.0017309079,0.000033659497,0.000016160393,3.608277e-8,0.0010435127,0.021125326,0.0019238065,0.96578133,0.00042499093,0.007876259],"study_design_scores_gemma":[0.00020644888,0.000027619622,0.027169738,0.0000046201503,0.0000074339537,0.0000025725155,0.000019066427,0.8574185,0.00080467534,0.111820735,0.0024554136,0.00006319021],"about_ca_topic_score_codex":0.000040250496,"about_ca_topic_score_gemma":0.000050461535,"teacher_disagreement_score":0.85396063,"about_ca_system_score_codex":0.000004144743,"about_ca_system_score_gemma":0.000014564962,"threshold_uncertainty_score":0.14397115},"labels":[],"label_agreement":null},{"id":"W1970624954","doi":"10.5555/2025756.2025766","title":"Basic object oriented genetic programming","year":2011,"lang":"en","type":"article","venue":"International Conference Industrial, Engineering & Other Applications Applied Intelligent Systems","topic":"Evolutionary Algorithms and Applications","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":"Carleton University","funders":"","keywords":"Genetic programming; Computer science; Object (grammar); Object-oriented programming; Programming language; Theoretical computer science; Linear programming; Parity (physics); Algorithm; Artificial intelligence","score_opus":0.06662924958501129,"score_gpt":0.25454613978348095,"score_spread":0.18791689019846966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970624954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009006696,0.00011442729,0.9816919,0.00014165118,0.0007701299,0.0016888354,0.000031792453,0.0006556884,0.014004884],"genre_scores_gemma":[0.9251031,0.00004352435,0.06732211,0.000058742353,0.0007571975,0.0059047723,0.000047644644,0.000065466236,0.00069743657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740964,0.00003006768,0.0008126441,0.00078195735,0.0005161748,0.00044948657],"domain_scores_gemma":[0.9981512,0.000079927726,0.0002965336,0.00093715097,0.00032837022,0.00020681098],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031380853,0.0003651141,0.00029005538,0.00036103284,0.00017811228,0.00028794922,0.0018219635,0.00021464808,0.0002150477],"category_scores_gemma":[0.00002051107,0.00037924843,0.000107762186,0.00079323596,0.00007189126,0.00025922092,0.0002159333,0.00038127636,0.00043047147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007345529,0.00025779023,0.00025079446,0.000014233882,0.00013698342,0.0000021553067,0.00041077312,0.002401713,0.0017173344,0.964629,0.0003461038,0.029825758],"study_design_scores_gemma":[0.0006612845,0.000083308296,0.0004423656,0.00012772398,0.000042084725,0.00007553587,0.0005171192,0.2177469,0.006291305,0.0020338558,0.77094597,0.0010325584],"about_ca_topic_score_codex":0.00031510455,"about_ca_topic_score_gemma":0.0000043754058,"teacher_disagreement_score":0.96259516,"about_ca_system_score_codex":0.00022616965,"about_ca_system_score_gemma":0.00016609488,"threshold_uncertainty_score":0.99986595},"labels":[],"label_agreement":null},{"id":"W1970756392","doi":"10.1074/jbc.m211875200","title":"Characterization and in Vivo Functional Analysis of Splice Variants of Cypher","year":2003,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; National Heart, Lung, and Blood Institute; Muscular Dystrophy Association; Heart and Stroke Foundation of Canada","keywords":"splice; In vivo; Computational biology; Characterization (materials science); Biology; Genetics; Cancer research; Medicine; Gene; Nanotechnology; Materials science","score_opus":0.018857817653916253,"score_gpt":0.23221462587852326,"score_spread":0.213356808224607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970756392","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89005834,0.00007206074,0.10951572,0.00006595399,0.000014610443,0.000014041683,0.000006785427,0.0000017208386,0.00025075782],"genre_scores_gemma":[0.99381864,0.00007644863,0.0060306187,0.000017007143,0.000017559783,8.6583225e-7,0.0000026633177,7.4490407e-7,0.000035467114],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994341,0.000021913946,0.00030785712,0.00008832321,0.00009341715,0.000054374927],"domain_scores_gemma":[0.99942565,0.000057992096,0.00028517077,0.00008322628,0.000114477654,0.000033489727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021405089,0.000043408443,0.00016855926,0.000045979166,0.000014154046,0.000004226372,0.000119031596,0.00005854217,0.0001030893],"category_scores_gemma":[0.000058485897,0.00003214289,0.000059227623,0.0004870652,0.000040479616,0.00008870319,0.000023636343,0.000067825174,1.2458291e-7],"study_design_candidate":"bench_or_experimental","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":[0.00000811574,0.00013456709,0.022318074,0.0000060899806,0.000054773933,0.0000013374959,0.000016980332,0.00015834751,0.9760233,0.0009809557,0.000008032067,0.00028943835],"study_design_scores_gemma":[0.00030367155,0.000047835212,0.8234488,0.00001572714,0.00004519363,0.000045788853,0.000028565328,0.006254295,0.16845681,0.0008601505,0.00041581644,0.000077372846],"about_ca_topic_score_codex":0.0000010128075,"about_ca_topic_score_gemma":1.4096419e-7,"teacher_disagreement_score":0.80756646,"about_ca_system_score_codex":0.000011522339,"about_ca_system_score_gemma":0.000028885474,"threshold_uncertainty_score":0.1310749},"labels":[],"label_agreement":null},{"id":"W1970757919","doi":"10.1007/s10710-012-9159-4","title":"Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming","year":2012,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Evolvability; Robustness (evolution); Genetic programming; Neutral network; Computer science; Fitness landscape; Phenotype; Neutrality; Neutral mutation; Genotype; Evolutionary algorithm; Biology; Evolutionary dynamics; Evolutionary biology; Genetics; Artificial intelligence; Population; Gene","score_opus":0.012921765718736695,"score_gpt":0.2800358134837032,"score_spread":0.2671140477649665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970757919","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81785333,0.0031076244,0.17819613,0.00019228009,0.00010187056,0.00044278006,0.000026451413,0.00004967017,0.00002988147],"genre_scores_gemma":[0.75158334,0.000041728086,0.24817051,0.000016720787,0.00004172221,0.000082564504,0.00001928168,0.000010051143,0.000034109147],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984699,0.00011313873,0.0003824841,0.0003943592,0.00023416062,0.00040596767],"domain_scores_gemma":[0.9988981,0.0003179498,0.00016223908,0.00041534984,0.00009046581,0.00011588682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028184496,0.0002061598,0.00031235907,0.00027252713,0.00027115512,0.00005595037,0.0004014053,0.00008140233,0.0000021659864],"category_scores_gemma":[0.00007535067,0.00015854696,0.00008805079,0.0013673161,0.00021949397,0.00015205836,0.0003193425,0.00017330359,0.0000026476828],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007998309,0.0001733359,0.8539673,0.00004013787,0.00012416605,5.121746e-7,0.0007101918,0.0033150348,0.000021258555,0.0020324853,0.0000057600564,0.13960184],"study_design_scores_gemma":[0.00015202597,0.000058187317,0.5765215,0.000024288518,0.00008699469,0.0000031258503,0.00008068069,0.42239752,0.000005032996,0.00037233208,0.00017971481,0.00011854991],"about_ca_topic_score_codex":0.00053345517,"about_ca_topic_score_gemma":0.0004825792,"teacher_disagreement_score":0.4190825,"about_ca_system_score_codex":0.000052061117,"about_ca_system_score_gemma":0.000035550584,"threshold_uncertainty_score":0.6465357},"labels":[],"label_agreement":null},{"id":"W1971470684","doi":"10.1109/cibcb.2008.4675788","title":"Transience in the simulation of ring species","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Ring (chemistry); Fertility; Population; Computer science; Robot; Terminal (telecommunication); Grid; Point (geometry); Task (project management); Artificial intelligence; Ecology; Biology; Evolutionary biology; Engineering; Mathematics; Computer network; Demography","score_opus":0.039768860782249206,"score_gpt":0.25740690799024946,"score_spread":0.21763804720800026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971470684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09496905,0.000017023889,0.8958273,0.00070807076,0.000010019445,0.000048199603,1.9664249e-7,0.000013029005,0.008407109],"genre_scores_gemma":[0.9799046,0.000007924441,0.019892743,0.00005366755,0.000009339336,0.000003724901,1.748418e-7,4.5208913e-7,0.00012739954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997091,0.000010599738,0.00007613768,0.00006477721,0.00009332146,0.000046005425],"domain_scores_gemma":[0.99974567,0.00007705147,0.000013744989,0.00014180015,0.000015897664,0.0000058480578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007207578,0.00002042614,0.00002666447,0.00002216841,0.000054275948,0.000004598572,0.00026535205,0.0000071240897,0.0000062403924],"category_scores_gemma":[0.0000039333604,0.0000136237,0.000012493673,0.00027550082,0.000031229207,0.00015718359,0.000012717092,0.000024223391,0.000003688599],"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":[6.8942376e-7,0.0001266397,0.00453411,0.0000031660418,0.0000012772241,0.0000029267355,0.0060903095,0.25983846,0.0010985108,0.7239374,0.00011337785,0.00425313],"study_design_scores_gemma":[0.00004933792,0.000008234353,0.22702484,0.0000016087452,2.1572477e-7,0.000004955474,0.000057240824,0.7694429,0.0002986052,0.0021567247,0.0009306334,0.000024686744],"about_ca_topic_score_codex":0.00001649127,"about_ca_topic_score_gemma":0.0000055657524,"teacher_disagreement_score":0.8849355,"about_ca_system_score_codex":0.0000039860984,"about_ca_system_score_gemma":0.000010700023,"threshold_uncertainty_score":0.05555584},"labels":[],"label_agreement":null},{"id":"W1972314940","doi":"10.1007/s10845-008-0175-4","title":"Simultaneous optimization of parts and operations sequences in SSMS: a chaos embedded Taguchi particle swarm optimization approach","year":2008,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":26,"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; Chaotic; Taguchi methods; Multi-swarm optimization; Mathematical optimization; Genetic algorithm; Set (abstract data type); Machining; Meta-optimization; Swarm behaviour; Engineering; Computer science; Algorithm; Mathematics; Artificial intelligence; Machine learning; Mechanical engineering","score_opus":0.022387963457414013,"score_gpt":0.2476547388629086,"score_spread":0.22526677540549458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972314940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23150966,0.00021509097,0.7678194,0.000193687,0.00006312873,0.00013069664,0.0000010548557,0.000012160591,0.000055099317],"genre_scores_gemma":[0.6931043,0.0005120762,0.30629063,0.000023893666,0.000029000465,0.000006003474,0.0000018405018,0.000004421863,0.000027787255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987822,0.00005496667,0.0005955544,0.00017173366,0.0002440466,0.00015146504],"domain_scores_gemma":[0.9992648,0.00010098982,0.00023849291,0.00016406452,0.00014860342,0.000083048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023998618,0.000108408785,0.00019205823,0.00015912339,0.00014105975,0.000046899702,0.0002785185,0.00004612448,0.000010762648],"category_scores_gemma":[0.000059053145,0.0000942779,0.000050556646,0.00021291686,0.00006749801,0.0006110188,0.00006513461,0.00013942648,7.43237e-7],"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":[0.0000066465823,0.00015479737,0.00015426666,0.000010865472,0.000012641162,0.000013775226,0.0016708482,0.9961862,0.0001452762,0.00059515657,0.000006293254,0.0010432197],"study_design_scores_gemma":[0.00018194852,0.00007387271,0.00015811731,0.00003338754,0.0000066453313,0.0002679496,0.0002528746,0.9621001,0.03669651,0.00011880941,0.000016990965,0.000092817616],"about_ca_topic_score_codex":0.000019712425,"about_ca_topic_score_gemma":0.0000033502647,"teacher_disagreement_score":0.4615947,"about_ca_system_score_codex":0.000055540862,"about_ca_system_score_gemma":0.00007455852,"threshold_uncertainty_score":0.38445413},"labels":[],"label_agreement":null},{"id":"W1973368448","doi":"10.1145/1138470.1138473","title":"Open BEAGLE","year":2006,"lang":"en","type":"article","venue":"ACM SIGEVOlution","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Crossover; Software; Evolutionary algorithm; Theoretical computer science; Evolutionary computation; Variation (astronomy); Listing (finance); Software evolution; Software engineering; Programming language; Software system; Artificial intelligence; Software construction","score_opus":0.01794381408884477,"score_gpt":0.2636760968243031,"score_spread":0.24573228273545833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973368448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010670808,0.00039466837,0.96057796,0.005290138,0.00021485494,0.00033381538,0.0000052077507,0.000276114,0.022236457],"genre_scores_gemma":[0.8066889,0.0000069721,0.18987691,0.00017035792,0.00013855389,0.000054276785,0.000027943433,0.0000060682773,0.003029995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991938,0.000024672354,0.00015478925,0.00029418903,0.00014423905,0.00018834967],"domain_scores_gemma":[0.9989643,0.000046214074,0.000058244394,0.0008367041,0.0000642741,0.000030250121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015154801,0.000076149576,0.000073308845,0.000045560213,0.00027472395,0.00015401158,0.0021215873,0.0000420549,0.000026498934],"category_scores_gemma":[0.000027083019,0.00007619237,0.0000329075,0.00038764044,0.000029923476,0.000867247,0.0009738913,0.0000626677,0.00032776935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001144741,0.00014191319,0.0008782948,0.0000016549202,0.0000033095619,0.0000014689136,0.000019955029,0.0006142872,0.0019550794,0.95436114,0.03198802,0.010033754],"study_design_scores_gemma":[0.00045701375,0.00005519217,0.13952863,0.000009357458,0.0000057283705,0.000017932362,0.00000796051,0.075303525,0.0013067542,0.46993488,0.31307217,0.00030083716],"about_ca_topic_score_codex":0.00058090565,"about_ca_topic_score_gemma":0.000020612351,"teacher_disagreement_score":0.7960181,"about_ca_system_score_codex":0.0000690827,"about_ca_system_score_gemma":0.000057282723,"threshold_uncertainty_score":0.42129213},"labels":[],"label_agreement":null},{"id":"W1974873444","doi":"10.1016/j.procs.2013.06.065","title":"Software Evolution as SaaS: Evolution of Intelligent Design in Cloud","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Software evolution; Computer science; Evolvability; Software engineering; Software development; Software; Software as a service; Cloud computing; Software system; Inheritance (genetic algorithm); Software construction; Data science; Programming language","score_opus":0.014486930275159293,"score_gpt":0.23873079679132056,"score_spread":0.22424386651616127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974873444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020076828,0.0002637374,0.9778275,0.00047703233,0.0004956167,0.00061122427,8.2468415e-7,0.00015603428,0.00009118378],"genre_scores_gemma":[0.580786,0.000008515873,0.41894475,0.000057334353,0.00008490797,0.00009489972,5.5421475e-7,0.0000042389647,0.000018785824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768794,0.000053617365,0.0004406381,0.0007167807,0.000627861,0.00047318623],"domain_scores_gemma":[0.9983611,0.00014531487,0.0001743119,0.0006055547,0.0005450977,0.0001686259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079083187,0.00016845924,0.00017905537,0.0004444051,0.00022179089,0.00014966479,0.0018956843,0.00006161106,0.000013001246],"category_scores_gemma":[0.00012750042,0.000161008,0.000050285078,0.002820292,0.00036337686,0.0018735297,0.0005892454,0.00015271018,0.00023510032],"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":[0.000011072006,0.0010790452,0.016027879,0.00010816262,0.000014709599,0.0000059147515,0.0034107196,0.056513414,0.008821985,0.6615243,0.0028702344,0.24961258],"study_design_scores_gemma":[0.00016158038,0.00016094543,0.042689495,0.00005490025,0.0000019492502,0.000031658197,0.000031170148,0.84101737,0.002430325,0.113104835,0.000093684,0.00022208021],"about_ca_topic_score_codex":0.00028601373,"about_ca_topic_score_gemma":0.0000048672705,"teacher_disagreement_score":0.78450394,"about_ca_system_score_codex":0.00044792544,"about_ca_system_score_gemma":0.0008324092,"threshold_uncertainty_score":0.6565716},"labels":[],"label_agreement":null},{"id":"W1976292963","doi":"10.1109/tla.2005.1642441","title":"Generating Ensemble of Classifiers through Unsupervised Feature Selection","year":2005,"lang":"en","type":"article","venue":"IEEE Latin America Transactions","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Random subspace method; Artificial intelligence; Computer science; Feature selection; Hidden Markov model; Pattern recognition (psychology); Machine learning; Feature (linguistics); Selection (genetic algorithm); Cascading classifiers; Context (archaeology); Set (abstract data type); Ensemble learning; Support vector machine","score_opus":0.018618261006247614,"score_gpt":0.25185819096505613,"score_spread":0.23323992995880852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976292963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048733926,0.00007317458,0.9879404,0.0047003436,0.00014020076,0.00015080573,0.000010910695,0.00019133218,0.0019194796],"genre_scores_gemma":[0.4588005,0.000044680062,0.5400271,0.00025671712,0.00010944724,0.000044967797,0.0000035371977,0.00000853073,0.0007045521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998977,0.000048903014,0.00024510318,0.00030727615,0.0002013646,0.00022032054],"domain_scores_gemma":[0.9993489,0.000076390344,0.00010963738,0.00029529093,0.000107086395,0.00006272051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005203243,0.00013208698,0.00016001095,0.000067720466,0.00037559963,0.000037191152,0.00027340365,0.00007465877,0.00007356097],"category_scores_gemma":[0.0000035706985,0.00013468652,0.00011141015,0.00095459743,0.00007663911,0.0005584296,0.0000051738125,0.00022274225,0.00002872744],"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":[0.000007767625,0.0005110851,0.00008474013,0.000020903919,0.00009437154,8.731984e-7,0.00295992,0.35701653,0.23732838,0.0055704443,0.005471581,0.39093342],"study_design_scores_gemma":[0.000320903,0.00008412075,0.0005854226,0.000013549464,0.000025533407,0.000020362551,0.00012731884,0.9390297,0.027101818,0.0005442177,0.031906575,0.00024046745],"about_ca_topic_score_codex":0.00009165543,"about_ca_topic_score_gemma":0.000033380416,"teacher_disagreement_score":0.5820132,"about_ca_system_score_codex":0.00006124924,"about_ca_system_score_gemma":0.00008740096,"threshold_uncertainty_score":0.54923564},"labels":[],"label_agreement":null},{"id":"W1977005518","doi":"10.3758/bf03192711","title":"A comparison of the randomization test with theF test when error is skewed","year":2005,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Randomization; Resampling; Statistics; Test (biology); Mathematics; Skew; Type I and type II errors; Restricted randomization; Permutation (music); Computer science; Medicine; Randomized controlled trial; Surgery; Biology","score_opus":0.22585174721536294,"score_gpt":0.5473035776803988,"score_spread":0.3214518304650359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977005518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014984184,0.00020498286,0.9749549,0.0073603727,0.00004043876,0.0012448253,0.000015534519,0.00007002657,0.0011247032],"genre_scores_gemma":[0.2546078,0.0000072470334,0.7433104,0.000043798296,0.000055957134,0.00040229768,0.0000023615403,0.00001143794,0.0015587198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754363,0.00074674556,0.0003129062,0.0003442485,0.000734863,0.00031759054],"domain_scores_gemma":[0.99633217,0.002000005,0.00012512713,0.00095892005,0.00048702108,0.00009676361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028257852,0.00011091009,0.00021410032,0.00012560491,0.0004453827,0.00008620125,0.0012685367,0.000060853938,0.0000734082],"category_scores_gemma":[0.0005530832,0.00006915597,0.00007350272,0.001058184,0.00028342093,0.00026067448,0.00035399993,0.00036312576,0.000018687037],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083945655,0.00614025,0.2039126,0.000042616302,0.000035567886,0.0000028510333,0.009331742,0.0003459568,0.104146585,0.01685259,0.01063922,0.6484661],"study_design_scores_gemma":[0.005477252,0.0007888982,0.35515207,0.00012545384,0.00007326999,0.000046345343,0.00064242294,0.38095248,0.19143644,0.00403266,0.060717963,0.0005547261],"about_ca_topic_score_codex":0.00006711032,"about_ca_topic_score_gemma":0.000024855728,"teacher_disagreement_score":0.64791137,"about_ca_system_score_codex":0.00007092531,"about_ca_system_score_gemma":0.000203791,"threshold_uncertainty_score":0.34255686},"labels":[],"label_agreement":null},{"id":"W1977113926","doi":"10.1155/2010/409045","title":"Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming","year":2010,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Evolvability; Computer science; Population; Representation (politics); Genetic programming; Tree (set theory); Symbolic regression; Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Biology; Genetics","score_opus":0.022156313430246256,"score_gpt":0.2836967631056357,"score_spread":0.2615404496753894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977113926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04513359,0.00006353095,0.953783,0.00018690921,0.00006868791,0.00063686667,0.0000072620232,0.00007570166,0.00004446518],"genre_scores_gemma":[0.52442896,0.0000013603499,0.4754198,0.000041228264,0.000038169932,0.000050699808,0.000013048295,0.000004860097,0.000001907423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986351,0.000025829035,0.00043514825,0.00050691434,0.00019559702,0.00020142006],"domain_scores_gemma":[0.9979919,0.0011675975,0.00024005379,0.00021216208,0.00030337894,0.0000848997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038198676,0.0001467682,0.00018925892,0.000090963826,0.00040351137,0.00011167528,0.00025489394,0.00007379352,0.0000013686373],"category_scores_gemma":[0.00009995589,0.00014570456,0.000046261866,0.00022300526,0.00017573565,0.00024322481,0.00014897599,0.00015034285,8.7408637e-7],"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":[0.000024238985,0.00014635663,0.00083028275,0.00005562021,0.00002623165,3.8767845e-7,0.0011962575,0.09297252,0.011570907,0.4533378,0.000011578429,0.43982783],"study_design_scores_gemma":[0.00020963194,0.000057836412,0.003500771,0.000010168073,0.000010449294,0.00001040859,0.00015121512,0.8797015,0.0026297488,0.11355023,0.00002381803,0.0001442332],"about_ca_topic_score_codex":0.000025284815,"about_ca_topic_score_gemma":0.000002954469,"teacher_disagreement_score":0.786729,"about_ca_system_score_codex":0.000010520979,"about_ca_system_score_gemma":0.000066064626,"threshold_uncertainty_score":0.594166},"labels":[],"label_agreement":null},{"id":"W1978176266","doi":"10.1145/773365.773371","title":"An EGA approach to the compile-time assignment of data to multiple memories in digital-signal processors","year":2003,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Computer science; Compiler; Parallel computing; Dual (grammatical number); Programming language","score_opus":0.03251975303947704,"score_gpt":0.2719346247869187,"score_spread":0.23941487174744167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978176266","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014697551,0.000038013895,0.98021126,0.0032041043,0.00007262148,0.001114289,0.000083014,0.000093200804,0.0004859411],"genre_scores_gemma":[0.49008062,9.248205e-7,0.50888336,0.0006828331,0.00013138952,0.00011053644,0.00006175657,0.000014299684,0.000034259807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727947,0.00017316299,0.00045782616,0.0009999989,0.0006059731,0.00048356928],"domain_scores_gemma":[0.99590206,0.000504248,0.00009669689,0.003111124,0.00010953597,0.0002763512],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0005466578,0.00027407118,0.0003175564,0.00022058735,0.00020153937,0.00025646706,0.006045704,0.00005544815,0.0000056625595],"category_scores_gemma":[0.00011510548,0.00019961494,0.000054591754,0.0012247296,0.000093356495,0.00056845776,0.0019860968,0.00030515358,0.00003338092],"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":[0.00005362569,0.002227813,0.0037969803,0.00006703531,0.00008169605,0.0000084294725,0.014126611,0.4852534,0.0020192284,0.01135318,0.010854911,0.4701571],"study_design_scores_gemma":[0.0010416441,0.00077190745,0.0121172555,0.0000755655,0.000012483085,0.00008557796,0.0001593498,0.8763291,0.0011998079,0.017625177,0.0897358,0.0008463272],"about_ca_topic_score_codex":0.00004004885,"about_ca_topic_score_gemma":0.00004808758,"teacher_disagreement_score":0.47538307,"about_ca_system_score_codex":0.000047326055,"about_ca_system_score_gemma":0.00016204886,"threshold_uncertainty_score":0.9993321},"labels":[],"label_agreement":null},{"id":"W1979699444","doi":"10.1145/1274000.1274070","title":"Exploring medical data using visual spaces with genetic programming and implicit functional mappings","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Functional reactive programming; Scalar (mathematics); Reactive programming; Functional programming; Extension (predicate logic); Theoretical computer science; Set (abstract data type); Inductive programming; Artificial intelligence; Machine learning; Programming paradigm; Mathematics; Programming language","score_opus":0.12438272513794114,"score_gpt":0.3095728765293999,"score_spread":0.18519015139145878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979699444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22712106,0.0000726139,0.7719542,0.0005341263,0.000040503808,0.000084734944,5.7345954e-7,0.0000900039,0.00010220466],"genre_scores_gemma":[0.40001905,0.000017174816,0.59960175,0.00010902124,0.00019347329,0.000012638344,0.0000071318545,0.000006299137,0.000033492608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988024,0.000007944281,0.00015065404,0.00040962716,0.00038293042,0.000246396],"domain_scores_gemma":[0.9993481,0.00006905632,0.000039624785,0.00033198108,0.000047659265,0.00016357437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034948502,0.000089784495,0.000075471144,0.00006835514,0.00023905034,0.000119831624,0.00039875723,0.000028848657,0.000015354648],"category_scores_gemma":[0.0000101257265,0.000071345,0.0000091635,0.00033479926,0.0000748552,0.0007654991,0.0005055373,0.000091648966,0.0000043224168],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017851487,0.0003380498,0.029542264,0.000041986874,0.00008699053,0.0000829296,0.0005458968,0.00028718935,0.0019309055,0.10313884,0.00029006953,0.86369705],"study_design_scores_gemma":[0.0004753313,0.000116323194,0.1671282,0.000051632735,0.000015243056,0.00085570046,0.0005013824,0.81568825,0.00020096012,0.0004900816,0.01410993,0.00036695987],"about_ca_topic_score_codex":0.00011676209,"about_ca_topic_score_gemma":0.000036528898,"teacher_disagreement_score":0.86333007,"about_ca_system_score_codex":0.000018704193,"about_ca_system_score_gemma":0.00008267417,"threshold_uncertainty_score":0.29093644},"labels":[],"label_agreement":null},{"id":"W198006216","doi":"","title":"Automatically generated object-oriented genetic programs to optimize adaptive job shop control and scheduling system.","year":2001,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Scheduling (production processes); Job shop scheduling; Artificial intelligence; Operations management; Engineering; Schedule; Operating system","score_opus":0.014836159282080014,"score_gpt":0.21105963147051163,"score_spread":0.1962234721884316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W198006216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.576054,0.00012369113,0.42170727,0.0008001408,0.000067283305,0.0005384853,0.000012314494,0.00022838835,0.00046843055],"genre_scores_gemma":[0.6828251,0.000010998989,0.31665978,0.000070856266,0.000038534792,0.0000055553664,0.000007091189,0.000012323182,0.00036977394],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978333,0.00019521064,0.00028472056,0.00072125974,0.00047985945,0.00048565943],"domain_scores_gemma":[0.9982221,0.000095609335,0.00017746649,0.0006319681,0.00042077698,0.0004520753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044711572,0.00025512,0.00037792535,0.0002594295,0.0007693017,0.00010242193,0.0008991549,0.00017899506,0.000024517209],"category_scores_gemma":[0.000040858074,0.00029971838,0.00011999102,0.0011732739,0.00017008002,0.00073094567,0.00045524488,0.00027759487,0.000098955716],"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":[0.0023255756,0.003283834,0.44572484,0.0006542912,0.0023627642,0.0017211485,0.019216824,0.0757442,0.073000155,0.27664554,0.00079065753,0.09853016],"study_design_scores_gemma":[0.0031544128,0.0005078689,0.29200438,0.00021820603,0.000120343044,0.00022252956,0.0016440743,0.7002174,0.00015285921,0.00033540817,0.000764039,0.00065844326],"about_ca_topic_score_codex":0.00007297587,"about_ca_topic_score_gemma":0.000052358784,"teacher_disagreement_score":0.6244732,"about_ca_system_score_codex":0.00019096621,"about_ca_system_score_gemma":0.00013670661,"threshold_uncertainty_score":0.9999455},"labels":[],"label_agreement":null},{"id":"W1980948051","doi":"10.1145/2598394.2598494","title":"Incorporating expert knowledge in object-oriented genetic programming","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Computer science; Variety (cybernetics); Object-oriented programming; Expert system; Class (philosophy); Object (grammar); Programming language; Artificial intelligence; Theoretical computer science; Machine learning; Software engineering","score_opus":0.012483241654857567,"score_gpt":0.25974680570015407,"score_spread":0.2472635640452965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980948051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013663608,0.00012720434,0.97772235,0.00038099487,0.00008991891,0.00014878312,9.6686826e-8,0.00017038186,0.007696651],"genre_scores_gemma":[0.50970733,0.0000018895677,0.48986477,0.00007056463,0.00006406979,0.00008278598,0.0000011750226,0.0000035106414,0.00020388496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923366,0.000041512445,0.00018395069,0.0002710444,0.00009004378,0.000179788],"domain_scores_gemma":[0.9994941,0.000053587824,0.000040255964,0.00030636942,0.000048624675,0.000057063964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016968635,0.000076390745,0.000077127974,0.000076456185,0.00010576353,0.000047889618,0.00033462467,0.00003049534,0.0000054769757],"category_scores_gemma":[0.000016474107,0.00006952998,0.000022899501,0.00058547646,0.000026356578,0.00017759808,0.00015787366,0.00006848417,0.00006335995],"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":[5.35404e-7,0.00024536112,0.0035793555,0.000005847205,0.0000027055464,0.0000016848734,0.00087510806,0.00015429949,0.0007218392,0.503161,0.00045667068,0.49079558],"study_design_scores_gemma":[0.00030340801,0.000064430984,0.018167745,0.00001924797,7.2597265e-7,0.000010430421,0.00011372918,0.9288385,0.0004830288,0.005715244,0.046073902,0.00020960823],"about_ca_topic_score_codex":0.00007454466,"about_ca_topic_score_gemma":0.000075603035,"teacher_disagreement_score":0.9286842,"about_ca_system_score_codex":0.000031619456,"about_ca_system_score_gemma":0.00004134503,"threshold_uncertainty_score":0.28353503},"labels":[],"label_agreement":null},{"id":"W1981971424","doi":"10.1016/j.tcs.2006.04.003","title":"Weighted multirecombination evolution strategies","year":2006,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Mathematics; Combinatorics","score_opus":0.005149068878092617,"score_gpt":0.23049791992071902,"score_spread":0.22534885104262642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981971424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021412402,0.00003001929,0.96864086,0.0012688845,0.00032729417,0.00014579346,0.0000010953413,0.00036356316,0.007810108],"genre_scores_gemma":[0.7376535,8.4324586e-7,0.2621403,0.000048815582,0.00011813743,0.000013014304,0.0000024720339,0.0000033974795,0.00001951434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982188,0.000052756004,0.00023880195,0.000563616,0.00052869535,0.00039734988],"domain_scores_gemma":[0.99892837,0.00009730651,0.000063607105,0.0005515131,0.00025323816,0.000105977364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005802673,0.00013334403,0.000107610525,0.00018095471,0.0006438031,0.00048397612,0.0014074912,0.000043839886,0.000020768231],"category_scores_gemma":[0.000009123352,0.00011687214,0.000045960358,0.0014156449,0.0013450912,0.0014811804,0.0003760146,0.00011772702,0.000116948366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.6434577e-7,0.0001009858,0.000038829585,0.0000015740044,7.983283e-7,0.0000013638393,0.00003188668,0.00041331458,0.0008187148,0.9890114,0.000083638886,0.009496708],"study_design_scores_gemma":[0.00009527599,0.000034422654,0.008094009,0.0000038140884,0.0000010517724,0.0000106641,0.000003298301,0.5100461,0.00051787525,0.48099434,0.000104489816,0.00009467388],"about_ca_topic_score_codex":0.000030462781,"about_ca_topic_score_gemma":0.000001544875,"teacher_disagreement_score":0.7162411,"about_ca_system_score_codex":0.0001273549,"about_ca_system_score_gemma":0.00017274955,"threshold_uncertainty_score":0.49560446},"labels":[],"label_agreement":null},{"id":"W1982679994","doi":"10.1142/s0129183102003450","title":"FUZZY REINFORCEMENT LEARNING","year":2002,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Fuzzy logic; Artificial intelligence; Computer science; Reinforcement learning; Extension (predicate logic); Adaptive neuro fuzzy inference system; Neuro-fuzzy; Inference; Fuzzy inference; Knowledge base; Fuzzy set operations; Fuzzy control system; Machine learning","score_opus":0.023636247267134574,"score_gpt":0.25815441488724405,"score_spread":0.2345181676201095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982679994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024197523,0.000156653,0.9857961,0.0023179806,0.0003565736,0.000024899184,4.8564e-7,0.000020338555,0.008907249],"genre_scores_gemma":[0.9788605,0.000093166615,0.019561648,0.00018107041,0.0005590658,0.0000020400569,0.0000012544422,0.0000045351903,0.0007367296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904734,0.000015921749,0.00025170005,0.000090424786,0.00050222565,0.00009241728],"domain_scores_gemma":[0.9991443,0.00003730339,0.00025835156,0.00011403318,0.00039425926,0.000051746836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099955556,0.00006372116,0.000076451324,0.000054394084,0.00006155741,0.00008199609,0.00081372657,0.000017519953,0.0000220289],"category_scores_gemma":[0.000014245296,0.000059013037,0.00009118758,0.00008551176,0.00001738235,0.00059195794,0.00011234582,0.00017273563,0.000052186468],"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":[0.0000048858437,0.0002528402,0.00029001554,0.0000019635936,0.00013971842,0.000031359497,0.0010707036,0.2542495,0.0019100386,0.32987192,0.003389264,0.4087878],"study_design_scores_gemma":[0.0003734725,0.000061569684,0.00025445875,0.000020301695,0.000004622742,0.00009676381,0.000009107037,0.83203644,0.0005174737,0.15719108,0.009341686,0.00009304882],"about_ca_topic_score_codex":0.0000016201561,"about_ca_topic_score_gemma":8.3245375e-8,"teacher_disagreement_score":0.9764407,"about_ca_system_score_codex":0.00006063913,"about_ca_system_score_gemma":0.00001998434,"threshold_uncertainty_score":0.24064818},"labels":[],"label_agreement":null},{"id":"W1983838844","doi":"10.1145/1822327.1822330","title":"Toward an estimation of distribution algorithm for the evolution of artificial neural networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Probabilistic logic; Neuroevolution; Estimation of distribution algorithm; Computer science; Chromosome; Artificial neural network; Evolutionary algorithm; Domain (mathematical analysis); Artificial intelligence; Algorithm; Evolutionary computation; Probabilistic neural network; Machine learning; Mathematics; Time delay neural network; Gene; Biology","score_opus":0.01749351702648979,"score_gpt":0.2649539502937995,"score_spread":0.24746043326730968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983838844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007253658,0.000024439529,0.9914636,0.0005722802,0.00030307437,0.0002871844,0.00003397562,0.00004430554,0.00001748677],"genre_scores_gemma":[0.76293355,0.0000010411652,0.2368414,0.000007965788,0.00010927192,0.00004410867,0.000050663137,0.0000023500409,0.000009648463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933106,0.000016968923,0.00024332071,0.0001581585,0.00013288447,0.00011762608],"domain_scores_gemma":[0.99917793,0.00012750995,0.00012913944,0.0003412913,0.00019078742,0.00003336223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027641497,0.000062925654,0.000080777005,0.000023178403,0.00014623912,0.000023500046,0.00038382562,0.00005520254,0.0000055285527],"category_scores_gemma":[0.000024260335,0.000045965415,0.000057808596,0.00027112375,0.00008761884,0.0003482946,0.00004889998,0.00008564938,6.8696045e-7],"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":[0.000002042176,0.00008513368,0.000023397226,0.0000024894653,0.0000040373384,2.273821e-8,0.000035574096,0.025355432,0.00065030146,0.4673558,0.0000889929,0.5063968],"study_design_scores_gemma":[0.00007719138,0.00007157461,0.00470487,0.0000013051502,0.000008456347,0.0000034985292,0.00003516133,0.97622925,0.00095736346,0.017781178,0.00007862638,0.00005155434],"about_ca_topic_score_codex":0.00013596534,"about_ca_topic_score_gemma":0.000023274462,"teacher_disagreement_score":0.9508738,"about_ca_system_score_codex":0.000020861144,"about_ca_system_score_gemma":0.00004247569,"threshold_uncertainty_score":0.18744151},"labels":[],"label_agreement":null},{"id":"W1985171145","doi":"10.1080/01969720802069831","title":"INFLUENCE OF TEMPERATURE ON SWARMBOTS THAT LEARN","year":2008,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ethogram; Ethology; Artificial intelligence; Computer science; Swarm behaviour; Cybernetics; Control (management); Swarm robotics; Duration (music); Machine learning; Ecology","score_opus":0.017008710101454404,"score_gpt":0.2303582216459071,"score_spread":0.2133495115444527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985171145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98962086,0.0006742163,0.00580558,0.00034136753,0.00020534862,0.0002597475,0.000007672861,0.00010632512,0.0029788644],"genre_scores_gemma":[0.9955942,0.00008865465,0.0020217441,0.00006560152,0.00008005739,0.000029658951,0.0000027342876,0.000007944216,0.0021094014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99895936,0.000045342615,0.00021572533,0.00027257722,0.0003328329,0.00017414302],"domain_scores_gemma":[0.9989799,0.00007543935,0.00011844935,0.00061737,0.00013077533,0.00007802592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010682586,0.000113994756,0.00015929964,0.00005747324,0.0001387873,0.000031029034,0.0006119403,0.00007866738,0.0000015061084],"category_scores_gemma":[0.000015399315,0.00010001372,0.000044942506,0.00029853443,0.00008409525,0.00013363389,0.000087168184,0.00014276824,0.00009989273],"study_design_candidate":"observational","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":[0.000008149714,0.00053112896,0.013980548,0.00012101468,0.0000619226,0.000059052156,0.0031153196,0.31076398,0.01685882,0.6382587,0.015027952,0.0012134297],"study_design_scores_gemma":[0.0014548284,0.000787894,0.701011,0.0006781288,0.00002623344,0.0008520615,0.00025853553,0.13768372,0.0162975,0.0032771113,0.13625231,0.0014206736],"about_ca_topic_score_codex":0.000096261356,"about_ca_topic_score_gemma":0.0000012424864,"teacher_disagreement_score":0.68703043,"about_ca_system_score_codex":0.000027740129,"about_ca_system_score_gemma":0.00006223694,"threshold_uncertainty_score":0.40784413},"labels":[],"label_agreement":null},{"id":"W1985849007","doi":"10.1109/cec.2012.6252966","title":"On run time libraries and hierarchical symbiosis","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Coevolution; Reinforcement learning; Genetic programming; Domain (mathematical analysis); Artificial intelligence; Code (set theory); Theoretical computer science; Programming language; Set (abstract data type)","score_opus":0.00922143711061991,"score_gpt":0.215162458524291,"score_spread":0.2059410214136711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985849007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092373505,0.0005380177,0.7581263,0.023443045,0.00017356116,0.00022209894,0.000005874299,0.00059804274,0.12451951],"genre_scores_gemma":[0.83132017,0.000008677008,0.1645205,0.0010090291,0.00009407033,0.000015302126,0.0000021536473,0.000003476629,0.003026631],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996225,0.0000118950165,0.000053411433,0.00010967688,0.000073008196,0.00012945644],"domain_scores_gemma":[0.99962074,0.00009718512,0.000009465914,0.00017318016,0.0000064941987,0.000092922266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000524691,0.000045054483,0.000041874766,0.000025265099,0.00011281618,0.000055457207,0.00016027116,0.000021993448,0.000077456156],"category_scores_gemma":[0.0000065176223,0.000035209276,0.000013345003,0.000104209525,0.00004016743,0.0004400479,0.00012141991,0.00004719424,0.00029008125],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.8090795e-7,0.000046751742,0.00028780717,6.1158005e-7,0.0000019430313,9.4063395e-8,0.00006194379,4.6061243e-7,0.000095619755,0.98945457,0.005197533,0.0048523154],"study_design_scores_gemma":[0.00059326424,0.0002995898,0.21684642,0.000023663768,0.000012097358,0.000082199826,0.000030577696,0.22956727,0.0047153966,0.41957292,0.12748738,0.00076922734],"about_ca_topic_score_codex":0.0000022018605,"about_ca_topic_score_gemma":4.1665363e-8,"teacher_disagreement_score":0.7389466,"about_ca_system_score_codex":0.0000028836955,"about_ca_system_score_gemma":0.000009452251,"threshold_uncertainty_score":0.37285045},"labels":[],"label_agreement":null},{"id":"W1987023923","doi":"10.1145/1569901.1570220","title":"Evolving java objects using a grammar-based approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Programming language; Java; Executable; Object-oriented programming; Scala; Generics in Java; Grammar; Java annotation; Suite; Test suite; Real time Java; Genetic programming; Artificial intelligence; Test case","score_opus":0.02424239627315119,"score_gpt":0.2548619523944566,"score_spread":0.23061955612130539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987023923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014847269,0.00007540706,0.97847337,0.00064444076,0.00003587817,0.00011955796,4.254849e-7,0.00023746146,0.018928725],"genre_scores_gemma":[0.47657746,5.0645883e-7,0.5228453,0.00039341484,0.000035327128,0.0000043279456,0.0000017714036,0.000001989248,0.00013988365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919426,0.000018668043,0.00012418657,0.0002849352,0.0001676397,0.00021032417],"domain_scores_gemma":[0.9994003,0.000025501235,0.000036714846,0.0004013663,0.00006122092,0.00007489285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011366669,0.00008708642,0.00007794644,0.00006724423,0.00021080524,0.00010969168,0.00043118396,0.000034935067,0.00001245326],"category_scores_gemma":[0.0000093785,0.00007835062,0.00004837179,0.00048477136,0.000017865497,0.00032847843,0.000042933192,0.00007038479,0.000018618563],"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":[0.0000031507768,0.0010883586,0.0004588425,0.00001642573,0.000015584503,0.000010854974,0.00043515136,0.037248015,0.010962803,0.8761447,0.004534349,0.06908176],"study_design_scores_gemma":[0.00012974095,0.000025852272,0.0020666264,0.0000041724466,0.0000022151553,0.000011484564,0.000015107061,0.99037325,0.00039236012,0.0064491015,0.00041246702,0.000117605225],"about_ca_topic_score_codex":0.000033682514,"about_ca_topic_score_gemma":8.7015286e-7,"teacher_disagreement_score":0.95312524,"about_ca_system_score_codex":0.00004421789,"about_ca_system_score_gemma":0.00009098352,"threshold_uncertainty_score":0.31950456},"labels":[],"label_agreement":null},{"id":"W1988076045","doi":"10.1007/s00500-011-0720-5","title":"Bio-inspired computing for hybrid information technology","year":2011,"lang":"en","type":"article","venue":"Soft Computing","topic":"Evolutionary Algorithms and Applications","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":"","keywords":"Computer science; Computer architecture; Artificial intelligence","score_opus":0.018863660318010254,"score_gpt":0.23717514462860195,"score_spread":0.2183114843105917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988076045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031854488,0.000047851747,0.96516144,0.00050665834,0.00028684342,0.00030640856,0.000004141651,0.00077661,0.0010555382],"genre_scores_gemma":[0.5577334,7.427995e-7,0.4419989,0.00016879155,0.000066439105,0.000009175869,0.0000093921835,0.0000047380854,0.0000084221865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989109,0.000013765998,0.00035737688,0.00025783063,0.000118197524,0.0003419407],"domain_scores_gemma":[0.9990689,0.000092955,0.00020348263,0.00038375962,0.00019329238,0.00005757085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002725954,0.00012901341,0.00013409334,0.00022130774,0.00051176804,0.000073522875,0.00077008136,0.000059627975,0.0000027809597],"category_scores_gemma":[0.000060000737,0.00013572433,0.000065302396,0.00051051786,0.00005906134,0.00055860437,0.0003708272,0.00011924255,0.00006505535],"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":[0.0000033516308,0.00009137767,0.003420611,0.00003677668,0.00002429552,0.0000017540284,0.001316754,0.0007263376,0.00015525994,0.4042198,0.0009999782,0.5890037],"study_design_scores_gemma":[0.00034400806,0.0000717754,0.0041340576,0.00002729116,0.000004931069,0.000041390536,0.000090784306,0.9570971,0.0015032006,0.029198542,0.0072643934,0.00022251217],"about_ca_topic_score_codex":0.000013588994,"about_ca_topic_score_gemma":4.588617e-7,"teacher_disagreement_score":0.9563708,"about_ca_system_score_codex":0.00003937774,"about_ca_system_score_gemma":0.000058743495,"threshold_uncertainty_score":0.55346775},"labels":[],"label_agreement":null},{"id":"W1988844742","doi":"10.1016/j.patcog.2014.05.003","title":"Dynamic selection of classifiers—A comprehensive review","year":2014,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":323,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fundação Araucária; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Classifier (UML); Computer science; Machine learning; Artificial intelligence; Selection (genetic algorithm); Relation (database); Data mining","score_opus":0.02605157689707486,"score_gpt":0.2663556851878202,"score_spread":0.24030410829074536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988844742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008628677,0.0005262882,0.98854166,0.001207087,0.00010868108,0.000197351,0.000008303454,0.000083031824,0.0006989245],"genre_scores_gemma":[0.93428177,0.0031686954,0.05981158,0.0022892426,0.00007729977,0.00013751024,0.00015478337,0.000013255462,0.00006585157],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929696,0.0000727078,0.00019825014,0.00020144344,0.000128996,0.00010161078],"domain_scores_gemma":[0.99940425,0.000060721915,0.00013091521,0.00017941245,0.00019002473,0.000034692282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011086452,0.00007176674,0.00011867595,0.00005190382,0.00006587357,0.00001208639,0.00017144316,0.000031774216,0.00005272468],"category_scores_gemma":[0.000013553141,0.00007189023,0.00005202877,0.00025355438,0.00002117037,0.00018443794,0.000037554197,0.00007323673,0.00015610017],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.325657e-7,0.00006257285,0.00025413604,0.0002959768,0.000009266812,1.605342e-7,0.000020793015,0.0000062549466,0.0015050742,0.00029244192,0.0010331192,0.99651957],"study_design_scores_gemma":[0.0007474959,0.00033049402,0.0739559,0.001935349,0.00007307674,0.00009977312,0.00002308906,0.8652847,0.00313532,0.024708077,0.029121133,0.00058559875],"about_ca_topic_score_codex":0.000014557823,"about_ca_topic_score_gemma":0.0000043663736,"teacher_disagreement_score":0.99593395,"about_ca_system_score_codex":0.00002359331,"about_ca_system_score_gemma":0.000013220016,"threshold_uncertainty_score":0.29315987},"labels":[],"label_agreement":null},{"id":"W1990194847","doi":"10.1115/1.3462919","title":"A System Framework With Online Monitoring and Evaluation for Design Evolution of Engineering Systems","year":2010,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bond graph; Mechatronics; Computer-automated design; Genetic programming; Computer science; Domain (mathematical analysis); Systems engineering; Control engineering; Systems design; Software engineering; Engineering; Artificial intelligence","score_opus":0.012479440373737003,"score_gpt":0.2612656044936706,"score_spread":0.2487861641199336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990194847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40489328,0.00006206383,0.5946232,0.000017994884,0.00028041736,0.00010826485,4.0790505e-7,0.000011986043,0.0000023833602],"genre_scores_gemma":[0.640202,0.0000040071773,0.35973436,5.5816474e-7,0.00005449493,0.0000029263756,1.1296471e-7,0.0000014638166,6.350121e-8],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900633,0.000007769801,0.00042984862,0.00007329487,0.00035676165,0.00012601435],"domain_scores_gemma":[0.99878275,0.00022060618,0.00029389726,0.00009734596,0.0005462165,0.000059197027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022576258,0.00006836673,0.00012677956,0.00048073463,0.00008322008,0.00012621278,0.00021261908,0.000035822522,3.2357356e-8],"category_scores_gemma":[0.0002545241,0.000058272806,0.000013134561,0.00062722206,0.000028937058,0.0025981842,0.000037640682,0.00016959733,4.5374918e-8],"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":[0.000002561756,0.000007443293,0.00082280795,0.00010077463,0.0000028293066,1.130316e-7,0.00059970946,0.96446747,0.0022933255,0.028413905,3.8267436e-7,0.0032886853],"study_design_scores_gemma":[0.00023733631,0.00006579702,0.027962606,0.00043769027,0.000004403216,0.000111972404,0.0002884548,0.9705029,0.00027689594,0.00003411059,0.000017125527,0.000060679165],"about_ca_topic_score_codex":0.000003670125,"about_ca_topic_score_gemma":5.6658397e-8,"teacher_disagreement_score":0.23530872,"about_ca_system_score_codex":0.000090143934,"about_ca_system_score_gemma":0.00014071971,"threshold_uncertainty_score":0.2376296},"labels":[],"label_agreement":null},{"id":"W1991112568","doi":"10.1109/cimsivp.2014.7013279","title":"Finding optimal transformation function for image thresholding using genetic programming","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Thresholding; Transformation (genetics); Genetic programming; Computer science; Image (mathematics); Artificial intelligence; Function (biology); Computer vision; Genetic algorithm; Pattern recognition (psychology); Machine learning; Biology","score_opus":0.025941573047686104,"score_gpt":0.2724775401970555,"score_spread":0.2465359671493694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991112568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023522597,0.000014911957,0.9749493,0.00024327231,0.00012679394,0.00033543567,7.8307926e-7,0.00016278058,0.0006441544],"genre_scores_gemma":[0.28705102,0.0000010300064,0.7126999,0.00003568538,0.00009738098,0.00006191358,0.000004788068,0.0000051903835,0.000043141506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992895,0.0000120394425,0.00018518894,0.00020465426,0.000108685905,0.00019993585],"domain_scores_gemma":[0.999623,0.00004295008,0.000054059303,0.00016991934,0.000067077184,0.00004296601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021687972,0.00007793397,0.00006584247,0.00006804897,0.00037691728,0.00012788184,0.00019403861,0.000033642846,0.00000791538],"category_scores_gemma":[0.000008324131,0.000074806005,0.000054846816,0.00021018212,0.000016204756,0.00074505317,0.000028243176,0.00004348541,0.0000075285575],"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":[0.000008140539,0.000109558656,0.00014378446,0.000070687056,0.000021724027,2.0304236e-7,0.0007189081,0.03994938,0.021930154,0.5604587,0.00026460463,0.37632412],"study_design_scores_gemma":[0.00018780828,0.00005845962,0.00057722366,0.000007702886,0.000008430782,0.000007714779,0.000038882627,0.98752004,0.0011429401,0.0031194475,0.0072244643,0.000106905536],"about_ca_topic_score_codex":0.00000871631,"about_ca_topic_score_gemma":8.700563e-7,"teacher_disagreement_score":0.9475706,"about_ca_system_score_codex":0.000033774493,"about_ca_system_score_gemma":0.000018693672,"threshold_uncertainty_score":0.30505005},"labels":[],"label_agreement":null},{"id":"W1993756550","doi":"10.1145/1276958.1277234","title":"The effects of solution density in the search space on finding spatially robust solutions","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robustness (evolution); Mathematical optimization; Computer science; Mathematics; Algorithm","score_opus":0.02757215062327319,"score_gpt":0.26879154607710537,"score_spread":0.24121939545383217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993756550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049880527,0.000060593342,0.94091445,0.0056726886,0.00014066637,0.00031100633,4.3990633e-7,0.00003323392,0.0029863645],"genre_scores_gemma":[0.98084056,0.000021693779,0.018743949,0.000114876886,0.000050737413,0.000014596605,0.000001055504,0.000002666289,0.00020985577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99892706,0.00010884744,0.00016429204,0.00017994705,0.00031569,0.0003041642],"domain_scores_gemma":[0.99790543,0.0014893655,0.00004716113,0.00045767834,0.00006830924,0.00003208178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016626777,0.00007008542,0.00006376132,0.00006999938,0.0006497412,0.0000484116,0.00063763343,0.000038845243,0.0000013390359],"category_scores_gemma":[0.00009742368,0.000041626823,0.00004351669,0.0005993702,0.00009058369,0.00012276122,0.00014624113,0.00017992487,0.000017912746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004487168,0.00016034085,0.0006346903,0.000007816239,0.000005393724,0.0000036836375,0.00059010164,0.002004442,0.0020180114,0.9819954,0.0010282436,0.0115474],"study_design_scores_gemma":[0.00052056543,0.0003015631,0.59756726,0.00006461549,0.000008853516,0.000025107483,0.00030727132,0.37280095,0.011082118,0.015748926,0.0013339332,0.00023883693],"about_ca_topic_score_codex":0.00035289995,"about_ca_topic_score_gemma":0.00089643616,"teacher_disagreement_score":0.9662465,"about_ca_system_score_codex":0.0000630374,"about_ca_system_score_gemma":0.0000588621,"threshold_uncertainty_score":0.49973497},"labels":[],"label_agreement":null},{"id":"W1995771052","doi":"10.1038/npre.2010.3913.2","title":"Design of a dynamic model of genes with multiple autonomous regulatory modules by evolution in silico","year":2010,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"In silico; Benchmark (surveying); Crossover; Computer science; Exploit; Evolutionary algorithm; Genetic algorithm; Computational biology; Artificial intelligence; Gene; Machine learning; Biology; Genetics","score_opus":0.007717461571683098,"score_gpt":0.22871241982902168,"score_spread":0.2209949582573386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995771052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19365336,0.0017127396,0.8035229,0.00018862492,0.00014938413,0.00058462954,0.000069334026,0.00008326815,0.000035787918],"genre_scores_gemma":[0.6442429,0.00004537434,0.35549286,0.000008087372,0.000013947305,0.00010831114,0.000023535385,0.00001387196,0.00005115086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982359,0.00004092715,0.00042381152,0.0006935453,0.00037472942,0.0002310973],"domain_scores_gemma":[0.9982216,0.00010687957,0.00048507532,0.00082325295,0.00030116417,0.00006206194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034780707,0.0002661707,0.00038165174,0.0002492399,0.000054838765,0.00002392001,0.0011585703,0.00092121627,0.0000011960112],"category_scores_gemma":[0.00003793675,0.00024935493,0.00007695339,0.00031361787,0.00014505639,0.00023257635,0.00055127224,0.00132396,5.375485e-7],"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":[0.000049892173,0.0004275486,0.0010512418,0.00025628653,0.00005757445,6.2745596e-7,0.00085687963,0.5693397,0.40350887,0.017796278,0.00030383188,0.0063512577],"study_design_scores_gemma":[0.0002787991,0.00003928485,0.003873421,0.00013442211,0.000014072024,0.000003919278,0.000008888089,0.9708791,0.010251295,0.014265915,0.00001391627,0.00023697955],"about_ca_topic_score_codex":0.00010671083,"about_ca_topic_score_gemma":0.00005403931,"teacher_disagreement_score":0.4505895,"about_ca_system_score_codex":0.00018801977,"about_ca_system_score_gemma":0.0003440189,"threshold_uncertainty_score":0.9999959},"labels":[],"label_agreement":null},{"id":"W1995804124","doi":"10.1186/1687-6180-2012-28","title":"Biologically inspired signal processing: analyses, algorithms and applications","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Signal processing; Algorithm; SIGNAL (programming language); Multidimensional signal processing; Digital signal processing; Computer hardware; Programming language","score_opus":0.042750614885376084,"score_gpt":0.3442295501907098,"score_spread":0.3014789353053337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995804124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017542882,0.028213,0.96753037,0.00064063317,0.000050167015,0.00021204505,0.0000034472487,0.00010547642,0.0014905808],"genre_scores_gemma":[0.8600598,0.000777296,0.1378517,0.00049387815,0.00067110744,0.00008857169,0.0000049265095,0.000016145468,0.00003656867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978041,0.00011907813,0.00060763425,0.0004522041,0.0004436053,0.0005733855],"domain_scores_gemma":[0.9987085,0.00014420776,0.00044446,0.00017986534,0.0001972242,0.00032578115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070835045,0.00027117247,0.00028863037,0.00030012475,0.0007862219,0.00034960208,0.0006995077,0.0000949725,0.000020120033],"category_scores_gemma":[0.000019161733,0.00021429038,0.0000684882,0.0011777555,0.00018600715,0.0031362132,0.00012929802,0.0006100304,0.000015282836],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012966354,0.00036627662,0.002890565,0.00003081827,0.000006301191,0.000009217146,0.00018945636,0.0019633921,0.0009265288,0.0039041394,0.000021739834,0.9896786],"study_design_scores_gemma":[0.0033090578,0.0010762027,0.06424246,0.0011235592,0.00009013112,0.0027479664,0.0010020091,0.5742034,0.0027667487,0.09541931,0.2513071,0.002712052],"about_ca_topic_score_codex":0.000001356446,"about_ca_topic_score_gemma":7.234476e-7,"teacher_disagreement_score":0.98696655,"about_ca_system_score_codex":0.00010910942,"about_ca_system_score_gemma":0.00014981412,"threshold_uncertainty_score":0.8738508},"labels":[],"label_agreement":null},{"id":"W1997899762","doi":"10.1145/1276958.1277165","title":"Learning recursive programs with cooperative coevolution of genetic code mapping and genotype","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Genetic programming; Probabilistic logic; Computer science; Fibonacci number; Set (abstract data type); Encoding (memory); Population; Function (biology); Grammatical evolution; Theoretical computer science; Code (set theory); Mathematical optimization; Artificial intelligence; Mathematics; Discrete mathematics; Programming language; Biology; Genetics","score_opus":0.013782526660064104,"score_gpt":0.23631852004359505,"score_spread":0.22253599338353094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997899762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12929296,0.00016341514,0.86892295,0.00016221002,0.000014537814,0.0001854112,4.1629406e-7,0.000054461183,0.0012036329],"genre_scores_gemma":[0.6497987,0.000019801419,0.34988657,0.000018567904,0.000016537017,0.000008731406,0.0000025115032,0.0000029358234,0.00024568517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993807,0.000019083243,0.00013706538,0.00020158869,0.000116020805,0.00014556193],"domain_scores_gemma":[0.9995296,0.000040704057,0.000069865855,0.00012583482,0.0001824073,0.000051588064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015585024,0.00006639446,0.00007821936,0.0000481966,0.00014749897,0.00002305585,0.00012643574,0.000027883767,0.0000034000027],"category_scores_gemma":[0.000007675566,0.000053410968,0.000010567204,0.00037175757,0.000091965776,0.00013335724,0.00005799353,0.00007764458,0.0000039989577],"study_design_candidate":"observational","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":[0.00004129825,0.0004030337,0.09041134,0.00005316318,0.00010573788,0.0000175668,0.008614965,0.0047746925,0.007154713,0.45640668,0.00019702128,0.4318198],"study_design_scores_gemma":[0.0012044271,0.0020835316,0.7002791,0.00012649305,0.00002272665,0.00019184318,0.0028568138,0.2724659,0.0034155732,0.004860858,0.011880337,0.000612335],"about_ca_topic_score_codex":0.000041973573,"about_ca_topic_score_gemma":0.000041428128,"teacher_disagreement_score":0.6098678,"about_ca_system_score_codex":0.000020848262,"about_ca_system_score_gemma":0.000038476956,"threshold_uncertainty_score":0.21780361},"labels":[],"label_agreement":null},{"id":"W1998452541","doi":"10.1007/s10710-008-9067-9","title":"Coevolutionary bid-based genetic programming for problem decomposition in classification","year":2008,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Bidding; Classifier (UML); Genetic programming; Support vector machine","score_opus":0.019829532841100332,"score_gpt":0.2670076920093719,"score_spread":0.24717815916827157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998452541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120837405,0.0023235732,0.8738951,0.0011481432,0.00009211499,0.0013571645,0.0000073939063,0.00027233973,0.00006676851],"genre_scores_gemma":[0.4134088,0.000059399463,0.5852459,0.000044304557,0.000063819345,0.0010654619,0.000044501132,0.000013748528,0.00005409262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817055,0.000059826645,0.00042928229,0.0006388526,0.00022323575,0.00047825984],"domain_scores_gemma":[0.999104,0.00010407779,0.00013818736,0.00037902853,0.00014575983,0.00012894897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022873204,0.00022535547,0.00020404474,0.00022341158,0.00062412163,0.000112779606,0.00036764963,0.00010177656,0.0000022515692],"category_scores_gemma":[0.000017731578,0.00022486047,0.00007370311,0.00063088344,0.00013594537,0.00020901799,0.0000836546,0.00012140255,0.0000066701896],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035491594,0.00092488696,0.096613504,0.00023972878,0.000027383036,0.000015348407,0.00052263425,0.009602569,0.0014133162,0.0050083306,0.0006776244,0.88491917],"study_design_scores_gemma":[0.0008280945,0.00023492565,0.1959968,0.000049429902,0.000014219249,0.00011489783,0.00002674105,0.7882788,0.0000481247,0.0054113464,0.008669637,0.0003269904],"about_ca_topic_score_codex":0.00017144755,"about_ca_topic_score_gemma":0.000037093112,"teacher_disagreement_score":0.8845922,"about_ca_system_score_codex":0.000077188284,"about_ca_system_score_gemma":0.00015104293,"threshold_uncertainty_score":0.9169544},"labels":[],"label_agreement":null},{"id":"W2000526024","doi":"10.1145/1276958.1277163","title":"Environment as a spatial constraint on the growth of structural form","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Constraint (computer-aided design); Context (archaeology); Fitness function; Genome; Computer science; Evolutionary biology; Genetic algorithm; Stability (learning theory); Phenotype; Biology; Artificial intelligence; Mathematical optimization; Mathematics; Genetics; Machine learning","score_opus":0.009458543202408508,"score_gpt":0.22134493720810067,"score_spread":0.21188639400569215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000526024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07506608,0.0000062652784,0.90508556,0.0038916687,0.000041020747,0.00014403531,0.0000023202629,0.00002358823,0.015739435],"genre_scores_gemma":[0.97536606,0.0000014705482,0.024079612,0.00037447375,0.000028707602,0.000004854027,8.360917e-7,0.0000013466307,0.00014265536],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995144,0.0000061140045,0.00011208371,0.00010980073,0.0001554609,0.000102114966],"domain_scores_gemma":[0.9996043,0.000091633556,0.00003775693,0.00021207887,0.000013068213,0.000041162395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001390011,0.00004692083,0.00004028555,0.000017595183,0.00008010003,0.000010027376,0.0002880464,0.000016105934,0.000145411],"category_scores_gemma":[0.000008633134,0.000027737893,0.000029307079,0.000057443758,0.000081038874,0.000052799925,0.00006916891,0.000047743742,0.000042424257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011000714,0.000016525004,0.00010984497,5.18424e-7,0.000003268395,5.6104324e-7,0.000104920575,0.00000908425,0.00037422584,0.9872267,0.00012999703,0.01202325],"study_design_scores_gemma":[0.00062929315,0.0005641954,0.1664167,0.000012804662,0.000008639184,0.00008388979,0.00035314192,0.107161336,0.07428701,0.64590716,0.0041988175,0.00037697863],"about_ca_topic_score_codex":0.00013493911,"about_ca_topic_score_gemma":0.0000059563504,"teacher_disagreement_score":0.90029997,"about_ca_system_score_codex":0.000020132054,"about_ca_system_score_gemma":0.000020778505,"threshold_uncertainty_score":0.15921485},"labels":[],"label_agreement":null},{"id":"W2000599920","doi":"10.1142/s0218001404003587","title":"PROJECT CellNet: EVOLVING AN AUTONOMOUS PATTERN RECOGNIZER","year":2004,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Set (abstract data type); Artificial intelligence; Genetic algorithm; Evolutionary algorithm; Binary number; Pattern recognition (psychology); Software; Machine learning; Mathematics","score_opus":0.08000262937064781,"score_gpt":0.3221495007818917,"score_spread":0.24214687141124389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000599920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11619597,0.00008559186,0.8800618,0.0023939006,0.0009072339,0.0001187555,0.0000274502,0.000042626376,0.00016663907],"genre_scores_gemma":[0.9763349,0.00015731512,0.022090225,0.00070934533,0.0006498418,0.000011601333,0.000021167658,0.000011721064,0.000013835662],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983328,0.000064307926,0.0006856025,0.00029604763,0.00042955604,0.00019168289],"domain_scores_gemma":[0.9984379,0.00007427025,0.0004042974,0.00016116397,0.000790752,0.00013164383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004061788,0.00015581235,0.00015891565,0.00030378424,0.00013083496,0.00037942393,0.00074809487,0.00006728221,0.00011425243],"category_scores_gemma":[0.000055989938,0.00014661561,0.000098298406,0.00018316192,0.00007582785,0.0012801671,0.000111836816,0.0002490139,0.000110787114],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008708519,0.00030011934,0.00017096337,0.0000040962314,0.000036178026,0.000062750965,0.0010317172,0.00018436769,0.0010583933,0.00080784794,0.000033468663,0.9963014],"study_design_scores_gemma":[0.001167935,0.0020331945,0.00842473,0.0010202551,0.00009888109,0.0044152536,0.0034029922,0.16881055,0.09742844,0.7076616,0.0036790813,0.0018570849],"about_ca_topic_score_codex":0.00016947545,"about_ca_topic_score_gemma":0.000057465768,"teacher_disagreement_score":0.9944443,"about_ca_system_score_codex":0.00009576261,"about_ca_system_score_gemma":0.00014895132,"threshold_uncertainty_score":0.5978811},"labels":[],"label_agreement":null},{"id":"W2003350518","doi":"10.1088/1478-3975/9/5/056001","title":"Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives","year":2012,"lang":"en","type":"article","venue":"Physical Biology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; New York State Stem Cell Science; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Pareto principle; Computer science; ENCODE; Context (archaeology); Selection (genetic algorithm); Fitness function; Relation (database); Function (biology); Multi-objective optimization; Mathematical optimization; Genetic algorithm; Algorithm; Artificial intelligence; Mathematics; Machine learning; Gene; Data mining; Biology; Genetics","score_opus":0.014767141693922039,"score_gpt":0.27640103463651844,"score_spread":0.2616338929425964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003350518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06558381,0.00016548077,0.9336034,0.00015437634,0.00015337726,0.00016148537,0.000021090482,0.00008105132,0.00007591022],"genre_scores_gemma":[0.7845108,0.000003268654,0.21484175,0.000043201137,0.0004904258,0.00007548975,0.000019605119,0.0000045767656,0.000010862391],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990783,0.00009343049,0.0001460899,0.0002893785,0.00006973575,0.00032306503],"domain_scores_gemma":[0.9991583,0.00014585647,0.000062571264,0.00038340187,0.000088947214,0.00016092694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011552534,0.00010871423,0.00018450043,0.00004529497,0.0000974568,0.000008524419,0.00043110602,0.000056820252,0.0000027458536],"category_scores_gemma":[0.000022561058,0.00009484305,0.000058972488,0.00036976355,0.00008860427,0.0003163263,0.00015449844,0.00007943258,0.000026101523],"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":[0.00003729801,0.0041812835,0.018481217,0.000011168314,0.00010174478,8.359617e-7,0.0025274127,0.03960739,0.074481405,0.57116556,0.0005903572,0.28881434],"study_design_scores_gemma":[0.00021898358,0.00023584216,0.058794744,0.0000033744877,0.0000070858305,0.0000044061867,0.00004971987,0.92758316,0.0035868676,0.008614086,0.00070666365,0.0001950409],"about_ca_topic_score_codex":0.0000818172,"about_ca_topic_score_gemma":0.0000023704224,"teacher_disagreement_score":0.8879758,"about_ca_system_score_codex":0.00004775796,"about_ca_system_score_gemma":0.000024660949,"threshold_uncertainty_score":0.38675874},"labels":[],"label_agreement":null},{"id":"W2004249039","doi":"10.1109/devlrn.2010.5578854","title":"Discovering sensor space: Constructing spatial embeddings that explain sensor correlations","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Rochester","keywords":"Embedding; Computer science; Wireless sensor network; Task (project management); Space (punctuation); Variance (accounting); Correlation; Algorithm; Topology (electrical circuits); Theoretical computer science; Artificial intelligence; Mathematics; Engineering; Geometry; Combinatorics","score_opus":0.01238155686690839,"score_gpt":0.24252907030720824,"score_spread":0.23014751344029985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004249039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18073894,0.000004625029,0.8077588,0.0021851456,0.00068006676,0.00015820347,0.000008584972,0.00027844787,0.008187186],"genre_scores_gemma":[0.6360615,0.0000020291177,0.36239737,0.00006605433,0.00017409193,0.000018299846,0.00000513775,0.000008007496,0.0012675226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988876,0.000020317735,0.00019244806,0.00039111965,0.00022559898,0.0002829075],"domain_scores_gemma":[0.9990083,0.00020243862,0.00010187244,0.0004911884,0.00007258031,0.00012362376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014777182,0.00014439855,0.00012222416,0.00007899055,0.00043854964,0.00020597904,0.00037189122,0.00007735829,0.00014290925],"category_scores_gemma":[0.00005521467,0.00013466968,0.0000675934,0.00025775566,0.00009925349,0.0006773813,0.00021297649,0.00031472946,0.00013298077],"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":[0.0000025559564,0.00009355127,0.01742435,0.000008794323,0.00002435036,0.000012677772,0.0012650838,0.0009932125,0.039912175,0.92191285,0.0012233888,0.017126989],"study_design_scores_gemma":[0.00069161976,0.000037201735,0.015247887,0.000025678559,0.000017498016,0.0005411636,0.0021347182,0.93697387,0.014317424,0.005973441,0.023264498,0.0007749894],"about_ca_topic_score_codex":0.00019433419,"about_ca_topic_score_gemma":0.00009660887,"teacher_disagreement_score":0.9359807,"about_ca_system_score_codex":0.000022208187,"about_ca_system_score_gemma":0.000057671015,"threshold_uncertainty_score":0.54916704},"labels":[],"label_agreement":null},{"id":"W2007872667","doi":"10.1145/1774088.1774328","title":"Probabilistic developmental program evolution","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Probabilistic logic; Computer science; Genetic programming; Population; Chromosome; Algorithm; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.009840436786740764,"score_gpt":0.24489455627367665,"score_spread":0.2350541194869359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007872667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05813366,0.000008434865,0.90157634,0.0015898545,0.00029357214,0.0005380292,0.0000010293232,0.0009339469,0.036925152],"genre_scores_gemma":[0.47917905,2.14434e-7,0.52004457,0.000031383654,0.000035207122,0.0001356713,0.0000021330397,0.0000018415269,0.0005698995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994416,0.0000056183617,0.0000981838,0.0002021059,0.00011378306,0.00013867297],"domain_scores_gemma":[0.9996482,0.00001675734,0.000019527823,0.00020043814,0.000054158198,0.000060914936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008214454,0.00005624889,0.000038149028,0.000030013516,0.00014527388,0.000058676687,0.00036300218,0.00003189472,0.000053662872],"category_scores_gemma":[0.000016361037,0.00004806435,0.000019942472,0.0002568578,0.00004128193,0.00025052854,0.0001027468,0.00009840866,0.00023970862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.7990888e-7,0.00015348263,0.0003383852,0.000001543712,0.0000018001659,4.179641e-7,0.000026657945,0.000004888058,0.0016166941,0.9374549,0.0009803482,0.059420753],"study_design_scores_gemma":[0.00040523757,0.00011703793,0.17804231,0.000005595295,0.000004731024,0.00020752518,0.00004475146,0.44785047,0.001064704,0.20970927,0.16202894,0.0005194378],"about_ca_topic_score_codex":0.000016524456,"about_ca_topic_score_gemma":0.00003319909,"teacher_disagreement_score":0.7277456,"about_ca_system_score_codex":0.00003089736,"about_ca_system_score_gemma":0.00010580886,"threshold_uncertainty_score":0.30810493},"labels":[],"label_agreement":null},{"id":"W2009371048","doi":"10.1109/tevc.2013.2252852","title":"Guest Editorial: Special Issue on Understanding Complex Evolutionary Systems","year":2013,"lang":"en","type":"editorial","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Evolutionary computation; Judgement; Evolutionary algorithm; Set (abstract data type); Simple (philosophy); Data science; Artificial intelligence; Selection (genetic algorithm); Quality (philosophy); Range (aeronautics); Management science; Theoretical computer science; Machine learning; Epistemology","score_opus":0.028362221496766017,"score_gpt":0.27014994298826395,"score_spread":0.24178772149149794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009371048","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010604722,0.000081477054,0.48143706,0.0005012412,0.51415646,0.0009160309,0.00053868204,0.0005789616,0.0017890538],"genre_scores_gemma":[0.0020151807,0.0002044682,0.008980644,0.00008078859,0.9837343,0.0007117573,0.0015108689,0.00014034931,0.0026216179],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99162954,0.00052954466,0.0014117208,0.0020080411,0.003455439,0.00096568774],"domain_scores_gemma":[0.99400425,0.0022472348,0.00078360457,0.0012826779,0.0012445112,0.0004377372],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047487655,0.0011084513,0.00091036403,0.0012095402,0.0021775805,0.000595413,0.0016394664,0.001569142,0.00017697738],"category_scores_gemma":[0.00005245337,0.0012381325,0.00048501167,0.0014779958,0.0003209175,0.0014804923,0.000030398853,0.0022232237,0.0032804026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049428487,0.00072210946,2.3640897e-7,0.000073384515,0.00013290996,0.000008242225,0.00008783351,0.1660762,0.000011149681,0.003465548,0.8285571,0.0008158615],"study_design_scores_gemma":[0.00091736636,0.0005293682,0.000029081295,0.00024125093,0.00007514808,0.000021142583,0.00008471693,0.23518491,0.00000507852,0.0037603932,0.75817746,0.000974079],"about_ca_topic_score_codex":0.0003080431,"about_ca_topic_score_gemma":0.000013181628,"teacher_disagreement_score":0.4724564,"about_ca_system_score_codex":0.0042095515,"about_ca_system_score_gemma":0.0011225967,"threshold_uncertainty_score":0.999727},"labels":[],"label_agreement":null},{"id":"W2009955015","doi":"10.1145/1569901.1596274","title":"Design &amp; Implementation of Parallel Linear GP for the IBM Cell Processor","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"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":"Brock University","keywords":"Operand; Computer science; Parallel computing; SIMD; Population; IBM; Selection (genetic algorithm); Algorithm; Artificial intelligence; Computer hardware","score_opus":0.0421437294131212,"score_gpt":0.3239630175252129,"score_spread":0.28181928811209167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009955015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037988974,0.00006316372,0.9940089,0.0046245335,0.000018796285,0.00061086565,0.0000019624224,0.00003753169,0.0002543743],"genre_scores_gemma":[0.14184542,0.000018807914,0.85673296,0.00032070008,0.00003907644,0.00014194798,0.0000046601403,0.0000020795862,0.0008943563],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995305,0.0000099001345,0.00013753636,0.0001273277,0.00009346541,0.00010129803],"domain_scores_gemma":[0.99951047,0.00010085842,0.00006244602,0.00021470038,0.000091767666,0.00001978645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015323711,0.00004770127,0.00004688347,0.000017258772,0.0001247791,0.000017128985,0.0003499357,0.000014934796,0.000017150775],"category_scores_gemma":[0.0000029442892,0.000031459254,0.000029022547,0.00015723816,0.000011742141,0.00014287437,0.000019827028,0.000023178889,0.000011009305],"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":[0.00005768031,0.000936858,0.00016612279,0.000053457992,0.000040411207,2.7125452e-7,0.0032322295,0.045788042,0.016429301,0.60684294,0.07105102,0.2554017],"study_design_scores_gemma":[0.002657644,0.00048612506,0.0068466538,0.00000575446,0.000027873186,0.000004820362,0.00027412575,0.8336141,0.0329899,0.07950342,0.04328198,0.00030759018],"about_ca_topic_score_codex":0.0000138859505,"about_ca_topic_score_gemma":0.0000047249173,"teacher_disagreement_score":0.78782606,"about_ca_system_score_codex":0.000007145576,"about_ca_system_score_gemma":0.000054036525,"threshold_uncertainty_score":0.1282871},"labels":[],"label_agreement":null},{"id":"W2011114846","doi":"10.1145/1569901.1569970","title":"A novel approach to adaptive isolation in evolution strategies","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Isolation (microbiology); Computer science; Adaptation (eye); Range (aeronautics); Engineering; Biology; Bioinformatics","score_opus":0.023783947388980684,"score_gpt":0.25016278627330923,"score_spread":0.22637883888432855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011114846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012417418,0.000016173759,0.9548271,0.0013797557,0.000018703378,0.0001910578,8.0373655e-7,0.00009651784,0.042228192],"genre_scores_gemma":[0.57208127,3.943116e-7,0.42763337,0.00013717738,0.000018223946,0.000018398992,0.0000014376698,9.333212e-7,0.00010880595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993698,0.000010034218,0.00012307035,0.00024465582,0.00011421369,0.00013820821],"domain_scores_gemma":[0.9996718,0.000012142048,0.00002215424,0.00020361083,0.000045686895,0.00004458443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000947437,0.00006446026,0.000059013055,0.00011027405,0.00006261041,0.00006147752,0.00025929496,0.0000312529,0.0000016900896],"category_scores_gemma":[0.0000038879666,0.000060731436,0.000018123392,0.0006725935,0.000007989412,0.0006839356,0.000033638105,0.0000578198,0.000027108645],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015238227,0.00017276248,0.00001578994,3.5987375e-7,6.9438994e-7,7.8782755e-8,0.0002137364,0.012042731,0.0018514924,0.9823482,0.00020581334,0.0031468214],"study_design_scores_gemma":[0.0001397682,0.00007510602,0.098563015,0.0000034633372,5.900194e-7,0.000005262191,0.00024903257,0.8463023,0.000029335995,0.054353666,0.0001784444,0.000100022495],"about_ca_topic_score_codex":0.000096674026,"about_ca_topic_score_gemma":0.00001638929,"teacher_disagreement_score":0.92799455,"about_ca_system_score_codex":0.000080545564,"about_ca_system_score_gemma":0.0000637487,"threshold_uncertainty_score":0.24765562},"labels":[],"label_agreement":null},{"id":"W2011644900","doi":"10.1145/1068009.1068311","title":"Evolving recurrent models using linear GP","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science","score_opus":0.048231247947865784,"score_gpt":0.29154664856048174,"score_spread":0.24331540061261595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011644900","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020273116,0.00024526424,0.98966473,0.001891747,0.000063969164,0.000063612264,7.8497567e-7,0.00014054109,0.0059020296],"genre_scores_gemma":[0.22699632,0.000012329143,0.77215534,0.00021790364,0.00016677033,0.0000057708908,8.905086e-7,0.0000031234968,0.0004415751],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940586,0.000009540944,0.00012015924,0.00019766946,0.00012313019,0.00014361642],"domain_scores_gemma":[0.99955446,0.000017751687,0.000026563086,0.00028402303,0.000060585786,0.000056647867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082316576,0.00005875107,0.000051163337,0.000037244114,0.00014576402,0.000043259402,0.00032964972,0.000021919825,0.00003887428],"category_scores_gemma":[0.0000033422402,0.000053230287,0.000031834286,0.00020165513,0.000012391247,0.00072230294,0.00013252709,0.00005920736,0.00006287012],"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":[5.781434e-7,0.00017915003,0.000034802953,0.0000034984548,0.000007771338,8.816505e-7,0.0002928197,0.16101828,0.0007164333,0.72738147,0.004891587,0.10547275],"study_design_scores_gemma":[0.000052158197,0.0000068529553,0.000053429114,0.0000046702476,0.0000012241949,0.0000073162314,0.000005077752,0.9857727,0.0001740201,0.0075334846,0.0063127847,0.00007623417],"about_ca_topic_score_codex":0.000019580832,"about_ca_topic_score_gemma":0.0000037932523,"teacher_disagreement_score":0.8247545,"about_ca_system_score_codex":0.00004866713,"about_ca_system_score_gemma":0.000040238734,"threshold_uncertainty_score":0.21706682},"labels":[],"label_agreement":null},{"id":"W2013406396","doi":"10.1038/nmeth.3180","title":"Two-factor designs","year":2014,"lang":"en","type":"article","venue":"Nature Methods","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Computational biology; Computer science; Biology","score_opus":0.033893313029410045,"score_gpt":0.3926086461626029,"score_spread":0.3587153331331928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013406396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015694632,0.0002964591,0.9881335,0.0014000604,0.00039582272,0.0000728134,9.404349e-7,0.00016871109,0.009374763],"genre_scores_gemma":[0.041147724,0.000002887767,0.9575047,0.0006546517,0.00022009957,0.000017171285,0.0000013146808,0.0000054365278,0.00044604606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990582,0.00027483798,0.00010165366,0.00027031987,0.00013387183,0.00016115417],"domain_scores_gemma":[0.99887705,0.0004149145,0.000041188432,0.00052601384,0.000065265966,0.00007556212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006418286,0.000084283325,0.000095740164,0.000050892973,0.00012881329,0.000050283514,0.0006300253,0.00011963711,0.000023808707],"category_scores_gemma":[0.00014861877,0.00007044535,0.000051467185,0.00036044253,0.000022565398,0.00016885021,0.000107895314,0.00034914727,0.0000428402],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.2217607e-7,0.000022738233,0.000036484074,0.0000017975899,0.000004626484,3.877044e-7,0.00004614708,0.000014922512,0.006130162,0.6781199,0.0007137006,0.3149087],"study_design_scores_gemma":[0.00040866763,0.00006949662,0.015024253,0.000008630313,0.000008545067,0.00003869587,0.000006362138,0.25013036,0.025685819,0.24330759,0.46493062,0.00038097287],"about_ca_topic_score_codex":0.000003088651,"about_ca_topic_score_gemma":5.743134e-7,"teacher_disagreement_score":0.46421692,"about_ca_system_score_codex":0.000019180088,"about_ca_system_score_gemma":0.000026877007,"threshold_uncertainty_score":0.2872678},"labels":[],"label_agreement":null},{"id":"W2014058093","doi":"10.1198/000313006x89947","title":"The PDF of a Function of a Random Variable","year":2006,"lang":"en","type":"article","venue":"The American Statistician","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Probability density function; Random variable; Mathematics; Probability mass function; Variable (mathematics); Inverse function; Function (biology); Inverse; Analogy; Applied mathematics; Statistical physics; Statistics; Mathematical analysis; Geometry; Physics","score_opus":0.004906086841960577,"score_gpt":0.22151449896902503,"score_spread":0.21660841212706444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014058093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027055289,0.00009138419,0.9922117,0.00053517695,0.000043641317,0.000106952124,0.000018548168,0.000015400132,0.004271645],"genre_scores_gemma":[0.8937675,0.000023902625,0.10515629,0.00012518745,0.0000665935,0.00004166908,0.0000046447544,0.000004619945,0.00080959324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993964,0.00006759963,0.00018901155,0.000091565526,0.00014580427,0.00010962665],"domain_scores_gemma":[0.9987417,0.0005669383,0.00025688412,0.00034058423,0.00008030156,0.00001362931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022838044,0.000047742233,0.00010835043,0.00001836655,0.00016766411,0.000013862618,0.0003759758,0.0000051761676,0.000010186019],"category_scores_gemma":[0.000024060362,0.000027706315,0.000024867615,0.0004773068,0.00033845648,0.000042120933,0.000048537106,0.000041509622,0.0000150019605],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026578138,0.000048891816,0.00008031757,0.0000026982343,0.000015340931,1.8699455e-7,0.000050702656,0.00062006724,0.001119858,0.9366482,0.0047608768,0.056626275],"study_design_scores_gemma":[0.0009398217,0.00046884175,0.20863628,0.00001944913,0.00007172714,0.000008929263,0.0004052345,0.18780203,0.000532435,0.55899465,0.04190984,0.00021077666],"about_ca_topic_score_codex":0.0016471202,"about_ca_topic_score_gemma":0.000034368695,"teacher_disagreement_score":0.89106196,"about_ca_system_score_codex":0.000009311309,"about_ca_system_score_gemma":0.000050389473,"threshold_uncertainty_score":0.2489964},"labels":[],"label_agreement":null},{"id":"W2016428245","doi":"10.1080/11762320802027869","title":"Adaptive Fuzzy‐Lyapunov Controller Using Biologically Inspired Swarm Intelligence","year":2008,"lang":"en","type":"article","venue":"Applied Bionics and Biomechanics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Swarm behaviour; Controller (irrigation); Fuzzy logic; Swarm intelligence; Lyapunov function; Control theory (sociology); Computer science; Control engineering; Artificial intelligence; Engineering; Machine learning; Control (management); Biology; Nonlinear system; Particle swarm optimization","score_opus":0.04479450929476107,"score_gpt":0.24125487429058953,"score_spread":0.19646036499582847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016428245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030304607,0.000521765,0.96762925,0.00040861248,0.000112764494,0.00036844253,0.000016303646,0.00015736833,0.0004808754],"genre_scores_gemma":[0.85164845,0.0006748824,0.14712211,0.00042429607,0.00005630459,0.00003192833,0.0000074621335,0.0000081639055,0.00002641088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986053,0.000019635345,0.00030850124,0.0005515542,0.00019315242,0.00032183697],"domain_scores_gemma":[0.9992439,0.00006510959,0.00013498012,0.00031832795,0.00010345487,0.00013420593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020099858,0.00020511603,0.00022278886,0.0001133765,0.000535145,0.000055894187,0.00050968456,0.00016083036,0.0000037025754],"category_scores_gemma":[0.0000071289396,0.00016465143,0.00006248663,0.0007286435,0.00013395521,0.00012215163,0.00037975327,0.00014601338,0.0000319972],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016663105,0.00015493954,0.000005199558,0.0000033946687,0.000029026955,0.000006576137,0.00011650573,0.000086154156,0.039174788,0.9168609,0.00005907579,0.043486793],"study_design_scores_gemma":[0.000535837,0.00026292095,0.00010115938,0.000011869111,0.00001554295,0.00013919506,0.0001131054,0.8039726,0.0120932525,0.17686087,0.0053433436,0.0005503006],"about_ca_topic_score_codex":0.000021047592,"about_ca_topic_score_gemma":0.0000012964776,"teacher_disagreement_score":0.82134384,"about_ca_system_score_codex":0.00004597777,"about_ca_system_score_gemma":0.00008555968,"threshold_uncertainty_score":0.67142904},"labels":[],"label_agreement":null},{"id":"W2016866640","doi":"10.1109/cig.2013.6633619","title":"Landscape automata for search based procedural content generation","year":2013,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Terrain; Automaton; Fitness landscape; Landform; Computer science; Cellular automaton; Representation (politics); Encoding (memory); Variety (cybernetics); Theoretical computer science; Artificial intelligence; Geography; Cartography","score_opus":0.06521564580974955,"score_gpt":0.272212435644394,"score_spread":0.20699678983464448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016866640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010421575,0.000012672288,0.9734978,0.014605425,0.00005006957,0.00053467014,0.0000028261136,0.000157094,0.00071787037],"genre_scores_gemma":[0.49831942,0.0000010937774,0.49799383,0.0010076567,0.00011368909,0.0006397013,0.000036864523,0.000004024674,0.001883729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994736,0.000008650729,0.00009706094,0.00017947685,0.000106516054,0.00013472323],"domain_scores_gemma":[0.9995097,0.00003116866,0.000016314812,0.00021907658,0.00017194971,0.000051773368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008082946,0.000049627568,0.000045230325,0.00003094512,0.0001354324,0.00011745607,0.00025392737,0.00002117026,0.000069834874],"category_scores_gemma":[0.0000074199043,0.000038633658,0.00002779072,0.00011180903,0.000010441098,0.00033535593,0.00003777695,0.000025247578,0.00011664915],"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":[0.000002575481,0.0003872015,0.00070497085,0.0000348916,0.000017467277,3.6641822e-7,0.00015045596,0.0020262648,0.046623662,0.5719379,0.31088164,0.067232564],"study_design_scores_gemma":[0.00025306927,0.000038738963,0.0037797792,0.0000013680326,8.6579615e-7,0.0000016736163,0.00001307968,0.98994684,0.0034155264,0.0005354165,0.0019521927,0.00006144638],"about_ca_topic_score_codex":0.000037828577,"about_ca_topic_score_gemma":0.0000045993793,"teacher_disagreement_score":0.9879206,"about_ca_system_score_codex":0.000012615805,"about_ca_system_score_gemma":0.00006375774,"threshold_uncertainty_score":0.15754348},"labels":[],"label_agreement":null},{"id":"W2016878627","doi":"10.1109/have.2013.6679618","title":"Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework","year":2013,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Fitness function; Genetic programming; Computer science; Signature (topology); Fitness approximation; Identity (music); Artificial intelligence; Haptic technology; Machine learning; Curse of dimensionality; Dimensionality reduction; Pattern recognition (psychology); Genetic algorithm; Data mining; Mathematics","score_opus":0.018038354518953244,"score_gpt":0.2555047289424851,"score_spread":0.23746637442353186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016878627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005350146,0.000021280906,0.9885334,0.008899628,0.0004006039,0.000593322,0.000014834434,0.00020815645,0.000793794],"genre_scores_gemma":[0.4983022,0.0000017218089,0.4975735,0.001395817,0.00021565815,0.0009651044,0.00004273381,0.000009966121,0.001493302],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903065,0.000012947905,0.00017135518,0.00038045298,0.00021065031,0.00019396588],"domain_scores_gemma":[0.9987384,0.0002609975,0.0000614909,0.0006302637,0.00022479756,0.00008404499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013222152,0.00011110211,0.000090009045,0.00007979178,0.00038779504,0.00022953597,0.00053179124,0.00010626653,0.0001464519],"category_scores_gemma":[0.00006423756,0.000099811106,0.00007744979,0.00046061206,0.000036405698,0.00079406565,0.000046154895,0.00013879205,0.00033820397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003495679,0.0004556613,0.00008811521,0.000012963225,0.000012489039,9.887056e-8,0.000036860853,0.0035507807,0.0034269162,0.96709234,0.019349022,0.005971276],"study_design_scores_gemma":[0.0002768422,0.000067161054,0.015796283,0.000015365502,0.00000770174,0.0000010379176,0.000019282677,0.940364,0.0002513826,0.03598288,0.007048984,0.00016906028],"about_ca_topic_score_codex":0.00005031231,"about_ca_topic_score_gemma":0.000006105787,"teacher_disagreement_score":0.93681324,"about_ca_system_score_codex":0.000045014072,"about_ca_system_score_gemma":0.000054051023,"threshold_uncertainty_score":0.43470407},"labels":[],"label_agreement":null},{"id":"W2017118179","doi":"10.1109/igarss.2015.7325699","title":"Unsupervised land-cover classification through hyper-heuristic-based Harmony Search","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Harmony search; Computer science; Land cover; Heuristic; Genetic programming; Artificial intelligence; Harmony (color); Machine learning; Cover (algebra); Pattern recognition (psychology); Land use; Engineering","score_opus":0.10815075594176088,"score_gpt":0.30295692205608277,"score_spread":0.1948061661143219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017118179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00820714,0.0000804027,0.9588842,0.010643183,0.00011888749,0.00019945214,0.000007176347,0.00028268708,0.021576904],"genre_scores_gemma":[0.74320465,0.000010085133,0.25329703,0.0013397365,0.00012396378,0.00006492812,0.000038230402,0.0000106094285,0.0019107813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998791,0.000057359117,0.00018023733,0.00036815467,0.0003740307,0.00022924022],"domain_scores_gemma":[0.9988777,0.00008080602,0.000032261894,0.000627932,0.00023720703,0.0001440968],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022833646,0.00010891934,0.00009963286,0.0000477592,0.0001450297,0.0001176006,0.00062985095,0.000056348865,0.00006824339],"category_scores_gemma":[0.000025166515,0.000093305694,0.000044326076,0.00046246845,0.000053620304,0.0004556153,0.000099121455,0.00010994596,0.0010485108],"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":[0.000027900463,0.001074821,0.0128388945,0.000027764114,0.000031380976,0.000010289236,0.0010901126,0.008904178,0.0036222737,0.8203787,0.13149592,0.020497737],"study_design_scores_gemma":[0.00070097274,0.0000614007,0.01074153,0.000004913293,0.000004819509,0.0000061552796,0.00003866318,0.9043433,0.0011561582,0.0068887724,0.075858265,0.00019504568],"about_ca_topic_score_codex":0.00015162381,"about_ca_topic_score_gemma":0.0000028616835,"teacher_disagreement_score":0.89543915,"about_ca_system_score_codex":0.00007082257,"about_ca_system_score_gemma":0.00025516044,"threshold_uncertainty_score":0.9997293},"labels":[],"label_agreement":null},{"id":"W2018463075","doi":"10.1145/1389095.1389141","title":"Nested evolution of an autonomous agent using descriptive encoding","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Science Foundation","keywords":"Neuroevolution; Computer science; Encoding (memory); Reinforcement learning; Genetic programming; Artificial intelligence; Process (computing); Evolutionary algorithm; Artificial neural network; Autonomous agent; Machine learning; Programming language","score_opus":0.05213425871765713,"score_gpt":0.26318797858109705,"score_spread":0.2110537198634399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018463075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33246076,0.00004224644,0.666177,0.000049298993,0.00006313244,0.000079731064,0.00000111615,0.00008281875,0.0010439102],"genre_scores_gemma":[0.7190374,0.000003050573,0.28076574,0.000016084703,0.00002848574,0.000005560469,0.0000013568118,0.0000027388924,0.00013956412],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992881,0.00003104475,0.00018040866,0.00022045779,0.0001393493,0.00014063717],"domain_scores_gemma":[0.9994344,0.000017633083,0.00007440701,0.00030382353,0.00010392073,0.00006582302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091228794,0.00007054147,0.000086838045,0.000080301485,0.00023738942,0.00001358391,0.00030973513,0.000032379645,0.00001151932],"category_scores_gemma":[0.0000066384914,0.00006794882,0.000036546702,0.0003789372,0.000058171063,0.00061161746,0.00008565315,0.000047408404,0.000009653494],"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":[0.0000065486256,0.00090547994,0.004233379,0.000014996783,0.00003765314,0.000032855292,0.0054896083,0.02571229,0.11024005,0.84082156,0.0003692042,0.012136362],"study_design_scores_gemma":[0.00012266234,0.000055463217,0.025628362,0.000006287729,0.000003696876,0.00011286636,0.00016121891,0.9678338,0.0033905704,0.0023485315,0.00022553059,0.00011099692],"about_ca_topic_score_codex":0.00043159202,"about_ca_topic_score_gemma":0.000008520387,"teacher_disagreement_score":0.9421215,"about_ca_system_score_codex":0.00017587095,"about_ca_system_score_gemma":0.00015750014,"threshold_uncertainty_score":0.27708727},"labels":[],"label_agreement":null},{"id":"W2018535136","doi":"10.1145/2597453.2597455","title":"DEAP","year":2014,"lang":"en","type":"article","venue":"ACM SIGEVOlution","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Python (programming language); Executable; Computer science; Programming language; Evolutionary algorithm; Evolutionary computation; Computation; Software engineering; Theoretical computer science; Artificial intelligence","score_opus":0.012173400080853441,"score_gpt":0.2369747470227567,"score_spread":0.22480134694190326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018535136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077783936,0.00011392211,0.9845712,0.0026895406,0.00017376716,0.0000724532,6.51983e-7,0.00025750854,0.0043425495],"genre_scores_gemma":[0.8542365,0.00000804248,0.14492913,0.00025455956,0.00011438779,0.000028280134,0.0000052260575,0.000004091951,0.00041979842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930733,0.000035206845,0.00011303141,0.00023179187,0.000143127,0.0001695076],"domain_scores_gemma":[0.9989878,0.00007974412,0.000041047886,0.00079005933,0.000055863013,0.00004548783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017581778,0.00006713923,0.000059361348,0.00004681505,0.00019175555,0.000036256544,0.000760661,0.00003994239,0.000013491811],"category_scores_gemma":[0.00011329603,0.00006574592,0.000035809186,0.0002745035,0.000027131655,0.00034256716,0.0002096092,0.000064600885,0.00045510088],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.297727e-7,0.000043937558,0.0004961828,0.0000021643439,0.0000031735565,2.3008236e-7,0.00004411311,0.00023843779,0.0013363386,0.9357296,0.0044142082,0.057691053],"study_design_scores_gemma":[0.00026001796,0.000067283014,0.077070735,0.000007389279,0.0000046357663,0.000013198064,0.0000061633878,0.3952284,0.00065213186,0.23581481,0.29064187,0.00023336988],"about_ca_topic_score_codex":0.000020259644,"about_ca_topic_score_gemma":0.0000018115272,"teacher_disagreement_score":0.8464581,"about_ca_system_score_codex":0.000038912185,"about_ca_system_score_gemma":0.000023077324,"threshold_uncertainty_score":0.5849553},"labels":[],"label_agreement":null},{"id":"W2022030072","doi":"10.1109/cec.2010.5586239","title":"Evolution for automatic assessment of the difficulty of sokoban boards","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Representation (politics); Computer science; Population; String (physics); Genetic programming; Simple (philosophy); Evolutionary algorithm; Sequence (biology); Genetic algorithm; Artificial intelligence; Algorithm; Theoretical computer science; Machine learning; Mathematics","score_opus":0.00906479607195665,"score_gpt":0.27877915082291754,"score_spread":0.2697143547509609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022030072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16757895,0.0000036313745,0.82967234,0.0010748398,0.00016986825,0.0002574835,0.000008108837,0.00003430754,0.0012004903],"genre_scores_gemma":[0.74871737,2.5036906e-7,0.25103715,0.000013167334,0.00001796113,0.000041577037,9.692294e-7,0.0000014379896,0.0001701174],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994742,0.0000111860045,0.00017216484,0.00010859976,0.00015537093,0.000078462865],"domain_scores_gemma":[0.99926907,0.000060547878,0.00010645726,0.000424453,0.000119263605,0.000020232375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014876614,0.000043741784,0.00007255199,0.000019990599,0.00008434138,0.000008766853,0.00048300656,0.00002846441,0.000011499824],"category_scores_gemma":[0.000017084049,0.00002700869,0.000070921036,0.00018422588,0.000056079876,0.000100206584,0.000097263044,0.00005404965,7.394938e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.288472e-7,0.00013373399,0.0017589589,0.000014924679,0.0000052129235,8.722073e-9,0.00003197492,0.000099139084,0.03045037,0.96432006,0.00089975283,0.0022857322],"study_design_scores_gemma":[0.000104688916,0.000020424422,0.3725477,0.0000040567793,0.0000040035993,0.0000014555794,0.000011083908,0.6129914,0.0021889084,0.01178962,0.00030387062,0.000032790293],"about_ca_topic_score_codex":0.000023494164,"about_ca_topic_score_gemma":0.000026871952,"teacher_disagreement_score":0.95253044,"about_ca_system_score_codex":0.000016213076,"about_ca_system_score_gemma":0.0001038744,"threshold_uncertainty_score":0.11013824},"labels":[],"label_agreement":null},{"id":"W2023328067","doi":"10.1145/1830483.1830653","title":"Object-level recombination of commodity applications","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Santa Fe Institute","keywords":"Computer science; Object (grammar); Commodity; Resolver; Ancestor; Selection (genetic algorithm); Key (lock); Simple (philosophy); Theoretical computer science; Artificial intelligence; Operating system","score_opus":0.021521516594752002,"score_gpt":0.26155355811694203,"score_spread":0.24003204152219004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023328067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004335426,0.000005714374,0.9781404,0.0013374386,0.00009268837,0.00018434967,0.00001067056,0.00010093402,0.015792338],"genre_scores_gemma":[0.69725955,0.000002944913,0.30202365,0.000054164782,0.000029703902,0.000101079895,0.000011111618,0.0000022202605,0.00051560736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994758,0.000010342026,0.0001510995,0.0001632581,0.000116110234,0.00008343032],"domain_scores_gemma":[0.99917233,0.00006555616,0.000065606684,0.0005124116,0.00014106224,0.000043017386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014926282,0.00004970678,0.000062531246,0.00005076728,0.000113501716,0.00001965007,0.0005293134,0.00004045765,0.000048251175],"category_scores_gemma":[0.000009778271,0.000046951885,0.000032642656,0.00033779556,0.00004635497,0.00022379511,0.00009766444,0.00010813175,0.000058741138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.4712663e-7,0.00013705912,0.0002640305,0.000002359892,0.0000022107633,2.8808136e-8,0.000030473218,0.000005198331,0.0037290002,0.94775367,0.0012914854,0.04678431],"study_design_scores_gemma":[0.0005922623,0.00007105735,0.13794106,0.000005166892,0.000009250432,0.000026978252,0.000039366434,0.17051452,0.023619218,0.5344189,0.13235801,0.00040423864],"about_ca_topic_score_codex":0.000040130515,"about_ca_topic_score_gemma":0.00003936396,"teacher_disagreement_score":0.6929241,"about_ca_system_score_codex":0.000007750526,"about_ca_system_score_gemma":0.000044448807,"threshold_uncertainty_score":0.19146425},"labels":[],"label_agreement":null},{"id":"W2023975480","doi":"10.1142/s0219265909002625","title":"USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS","year":2009,"lang":"en","type":"article","venue":"Journal of Interconnection Networks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Adaptability; Scripting language; Learning classifier system; Human–computer interaction; Unsupervised learning","score_opus":0.05289993945390044,"score_gpt":0.31284676810128415,"score_spread":0.2599468286473837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023975480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10299317,0.00032112867,0.8958936,0.0002736705,0.00033966152,0.00014603771,9.0643766e-7,0.000013412764,0.00001843648],"genre_scores_gemma":[0.9838715,0.000034923854,0.015537777,0.00006760207,0.00043848742,0.0000053279246,0.0000040528466,0.000004888652,0.00003541999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909586,0.00009064792,0.00043417476,0.00015258223,0.00009356315,0.00013318604],"domain_scores_gemma":[0.9988826,0.00030071381,0.00038658734,0.000084633306,0.000293492,0.000052010884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032348727,0.00009228376,0.00015707214,0.0002088725,0.00016424488,0.0002075523,0.00012562324,0.00007209083,0.0000031465142],"category_scores_gemma":[0.000092753966,0.00008636479,0.000055892262,0.000248385,0.00001382435,0.0010839995,0.000014429151,0.0002767823,1.8722474e-7],"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":[0.00002140903,0.000051974705,0.000116707786,0.0000015771569,0.000010110577,0.0000011112221,0.00009392917,0.979004,0.0013236003,0.0048010456,0.00015632677,0.0144181615],"study_design_scores_gemma":[0.00048207404,0.00041723144,0.0005975812,0.00007547243,0.000008868344,0.000055372664,0.00023478449,0.99685955,0.0000963743,0.00036934105,0.000716277,0.000087074885],"about_ca_topic_score_codex":0.000015632078,"about_ca_topic_score_gemma":0.000015372894,"teacher_disagreement_score":0.8808783,"about_ca_system_score_codex":0.00013536532,"about_ca_system_score_gemma":0.000075844786,"threshold_uncertainty_score":0.35218537},"labels":[],"label_agreement":null},{"id":"W2024144496","doi":"10.1007/bf03037572","title":"Logic-based genetic programming with definite clause translation grammars","year":2001,"lang":"en","type":"article","venue":"New Generation Computing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":33,"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; Definite clause grammar; Tree-adjoining grammar; Programming language; Natural language processing; Context-free grammar; Genetic programming; Artificial intelligence; Context-sensitive grammar; Rule-based machine translation; Attribute grammar; Grammar; Affix grammar; Synchronous context-free grammar; L-attributed grammar; Generative grammar; Linguistics; Example-based machine translation","score_opus":0.04981549137993206,"score_gpt":0.2629529065584332,"score_spread":0.2131374151785011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024144496","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023955934,0.00020870543,0.9731438,0.0017754597,0.00011520941,0.00029834895,5.192316e-7,0.0003162064,0.00018581789],"genre_scores_gemma":[0.33277786,0.000006469646,0.666421,0.00035859155,0.0003424303,0.00001277669,0.000028967317,0.000009508473,0.000042390235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866265,0.00005508772,0.00028204976,0.00044248853,0.00027574893,0.00028198276],"domain_scores_gemma":[0.99922293,0.00006511226,0.00012504807,0.00035031867,0.00011493711,0.00012167087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014920808,0.00015732192,0.00011249419,0.0001035892,0.0004441182,0.0002641999,0.00030679288,0.000059973823,0.000008567744],"category_scores_gemma":[0.0000073040997,0.00014673246,0.000046399575,0.000718767,0.000026855767,0.00024383666,0.00003104925,0.0001145398,0.000024616516],"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":[0.0000039690453,0.0000777294,0.0017456614,0.000004164735,0.000009129797,0.000008612827,0.0001925631,0.25731483,0.0012203342,0.017617,0.00030869193,0.7214973],"study_design_scores_gemma":[0.0005465224,0.00012935502,0.002123264,0.00001257574,0.00000865732,0.00003344344,0.000007282577,0.9850116,0.00027284326,0.0008834218,0.010763062,0.00020799383],"about_ca_topic_score_codex":0.000052735704,"about_ca_topic_score_gemma":0.00006218015,"teacher_disagreement_score":0.7276967,"about_ca_system_score_codex":0.000043629163,"about_ca_system_score_gemma":0.00017051389,"threshold_uncertainty_score":0.59835756},"labels":[],"label_agreement":null},{"id":"W2024519280","doi":"10.1016/j.mechatronics.2011.11.012","title":"Design evolution of mechatronic systems through modeling, on-line monitoring, and evolutionary optimization","year":2012,"lang":"en","type":"article","venue":"Mechatronics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"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 British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Mechatronics; Genetic programming; Control engineering; Conveyor system; MATLAB; Computer-automated design; Domain (mathematical analysis); Systems design; Computer science; Engineering; Systems engineering; Artificial intelligence","score_opus":0.04281174981100926,"score_gpt":0.26963445309039136,"score_spread":0.2268227032793821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024519280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002040128,0.012194157,0.98436934,0.00018912023,0.0005628794,0.00038587532,0.0000073798674,0.00014681256,0.00010430809],"genre_scores_gemma":[0.6515955,0.00043520238,0.3476381,0.000006623447,0.00018452368,0.000073095754,0.0000071129034,0.000013597501,0.0000462726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998375,0.000104936444,0.0003654196,0.00033488183,0.00040420608,0.0004155688],"domain_scores_gemma":[0.9989494,0.0000721662,0.00017634088,0.00053733605,0.00016326738,0.00010149936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049814454,0.0001887763,0.00019988282,0.000109235756,0.00024709638,0.00003945812,0.0004199236,0.00012712704,0.0000038451285],"category_scores_gemma":[0.000021148473,0.00018708964,0.000053901145,0.00035839828,0.00003544069,0.0009975261,0.00017003845,0.00016390576,0.000015141982],"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":[0.000004011903,0.00010100486,0.000047256694,0.000011531584,0.000012539605,7.772463e-8,0.00010150834,0.6460439,0.00008958608,0.3533433,0.00005148405,0.00019381645],"study_design_scores_gemma":[0.00026247776,0.00016403884,0.000027394746,0.000040334642,0.000017897628,0.000013965179,0.00010707945,0.98817295,0.0002732863,0.010456279,0.00028681027,0.00017748051],"about_ca_topic_score_codex":0.00005831889,"about_ca_topic_score_gemma":1.4480001e-7,"teacher_disagreement_score":0.6495553,"about_ca_system_score_codex":0.00035809932,"about_ca_system_score_gemma":0.00014552535,"threshold_uncertainty_score":0.76292944},"labels":[],"label_agreement":null},{"id":"W2025201938","doi":"10.1142/s1469026805001441","title":"USING COMPETITIVE CO-EVOLUTION TO EVOLVE BETTER PATTERN RECOGNISERS","year":2005,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Task (project management); Software; Binary number; Artificial intelligence; Reliability (semiconductor); Base (topology); Pattern recognition (psychology); Arithmetic","score_opus":0.03700165440096965,"score_gpt":0.3397540574526187,"score_spread":0.3027524030516491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025201938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055589573,0.00014518603,0.9845346,0.0086260615,0.00015265634,0.00017667741,0.000034792414,0.000030922663,0.0007400977],"genre_scores_gemma":[0.6940048,0.000034484867,0.3038094,0.0013602438,0.0006947556,0.00003343711,0.000016389882,0.0000086991595,0.000037817248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982958,0.00003913846,0.00062947813,0.00028277028,0.0005808673,0.00017196075],"domain_scores_gemma":[0.99780977,0.00022071468,0.0003491388,0.0001643221,0.0012684506,0.00018759539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026651702,0.00015181546,0.00016532977,0.0003788958,0.00018476571,0.00017570032,0.0009162451,0.000051577365,0.00004208509],"category_scores_gemma":[0.00002207783,0.0001532097,0.00010111426,0.0003435769,0.000090408896,0.0007750137,0.00013720727,0.00018681263,0.00011547045],"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":[0.000013851871,0.00031696114,0.0009020627,0.00000464168,0.00013140446,0.00000585731,0.0005132271,0.20971791,0.0004085762,0.28099915,0.0007552872,0.50623107],"study_design_scores_gemma":[0.0006282615,0.00023086235,0.011204345,0.00016823622,0.00005232821,0.0011410104,0.00065990334,0.6292372,0.002199264,0.20465837,0.14908978,0.00073045195],"about_ca_topic_score_codex":0.000014200531,"about_ca_topic_score_gemma":0.000004192669,"teacher_disagreement_score":0.6884458,"about_ca_system_score_codex":0.00022201326,"about_ca_system_score_gemma":0.00016896875,"threshold_uncertainty_score":0.624771},"labels":[],"label_agreement":null},{"id":"W2025458325","doi":"10.1145/1143997.1144159","title":"Improving GP classifier generalization using a cluster separation metric","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Genetic programming; Computer science; Classifier (UML); Artificial intelligence; Machine learning; Cluster analysis; Fitness function; Maximization; Generalization; Genetic algorithm; Mathematical optimization; Mathematics","score_opus":0.019611863531352507,"score_gpt":0.27277440334368325,"score_spread":0.2531625398123307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025458325","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012650191,0.000060737195,0.98344356,0.0004106795,0.00010356172,0.00012949533,7.402524e-7,0.00013296455,0.0030680893],"genre_scores_gemma":[0.49361125,0.0000017565637,0.5038158,0.00024921744,0.00021310714,0.00001782835,0.000015523328,0.0000059900403,0.0020694956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992603,0.000025664907,0.00017845051,0.0002385789,0.00015484824,0.0001421702],"domain_scores_gemma":[0.99955565,0.000020257696,0.00007034572,0.00023786162,0.00008758713,0.000028311746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011046571,0.000073282456,0.000058379577,0.0001399779,0.00020279226,0.00013585214,0.00019119593,0.00004391182,0.000014422116],"category_scores_gemma":[0.000005420888,0.00006719493,0.00003288557,0.00082634384,0.000012613673,0.0006458087,0.00007622973,0.00003684525,0.000029134522],"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":[0.0000015956679,0.0001712436,0.0011891384,0.000010082487,0.000008806731,0.000001369207,0.00006158132,0.1068904,0.039005496,0.82538253,0.008304283,0.018973498],"study_design_scores_gemma":[0.000121087534,0.000007771702,0.0022115773,0.0000012710324,0.000003881848,0.000008453976,0.0000031191423,0.99043924,0.0016952418,0.0030381172,0.0023725345,0.00009772599],"about_ca_topic_score_codex":0.00038737876,"about_ca_topic_score_gemma":0.000026405893,"teacher_disagreement_score":0.8835488,"about_ca_system_score_codex":0.00007080315,"about_ca_system_score_gemma":0.00004413206,"threshold_uncertainty_score":0.27401298},"labels":[],"label_agreement":null},{"id":"W2025620430","doi":"10.1145/2598394.2598488","title":"On improving grammatical evolution performance in symbolic regression with attribute grammar","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Science Foundation Ireland","keywords":"Grammatical evolution; Symbolic regression; Computer science; Grammar; Natural language processing; Artificial intelligence; Linguistics; Genetic programming","score_opus":0.0073717154893587385,"score_gpt":0.21380086865900755,"score_spread":0.2064291531696488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025620430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17467879,0.000008897404,0.82300895,0.000844145,0.000041037463,0.00014286653,3.5579453e-7,0.00012684157,0.00114811],"genre_scores_gemma":[0.9490217,0.0000030920744,0.05055253,0.000089282796,0.000041106152,0.000050363025,0.0000038343946,0.000005669629,0.00023240173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989363,0.000042622676,0.00017900494,0.00032663156,0.00024708034,0.00026835225],"domain_scores_gemma":[0.9992793,0.00008755859,0.000055920493,0.00045126915,0.000046572113,0.000079339705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002780668,0.000118081975,0.000116982796,0.00010736457,0.00016809731,0.00005070403,0.00037061295,0.00005131565,0.000005426113],"category_scores_gemma":[0.000026197995,0.00008056057,0.000022737035,0.00046949903,0.000040451112,0.00037063874,0.000092353526,0.00015760754,0.000050980507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008327355,0.00013309586,0.0058816574,0.000017721806,0.0000022073427,0.000001194561,0.00005387671,0.0015404021,0.00019658337,0.9479628,0.00012742411,0.04407469],"study_design_scores_gemma":[0.00034920842,0.00021546919,0.09841804,0.000054340373,0.0000017152698,0.000012898225,0.000008551593,0.891931,0.00024176075,0.008338665,0.0002826192,0.00014573778],"about_ca_topic_score_codex":0.000042181455,"about_ca_topic_score_gemma":0.000018228513,"teacher_disagreement_score":0.93962413,"about_ca_system_score_codex":0.000091984104,"about_ca_system_score_gemma":0.00004109373,"threshold_uncertainty_score":0.32851645},"labels":[],"label_agreement":null},{"id":"W2026440556","doi":"10.1007/s10710-011-9144-3","title":"The evolution of higher-level biochemical reaction models","year":2011,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"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":"Genetic programming; Computer science; Programming language; Syntax; Construct (python library); Feature (linguistics); Artificial intelligence; Domain (mathematical analysis); Interface (matter); Grammar; Theoretical computer science; Grammatical evolution; Machine learning","score_opus":0.029252218412441498,"score_gpt":0.23690475355481502,"score_spread":0.20765253514237353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026440556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07099562,0.0074344585,0.9191833,0.00036444206,0.00023055483,0.00026228165,0.0000038811672,0.00014925141,0.0013762242],"genre_scores_gemma":[0.7594833,0.000091478105,0.23999515,0.0000071637764,0.00004400502,0.000057798596,0.0000017645574,0.0000050163653,0.00031436104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992044,0.000025070056,0.00019711717,0.00023704948,0.00013720799,0.00019913907],"domain_scores_gemma":[0.9993914,0.000036996837,0.00008588245,0.0003406485,0.00008678642,0.00005828291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016363214,0.00009605575,0.00008668502,0.000038181068,0.000295746,0.000039676794,0.00032891062,0.000049050243,0.000001524953],"category_scores_gemma":[0.000007861096,0.000068663474,0.000037224097,0.00024952964,0.00011407297,0.00014814208,0.00013024606,0.000072784125,0.0000030642948],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020850684,0.0002993383,0.0034118127,0.000046391062,0.000051416573,0.000001295052,0.0007458896,0.00015531591,0.007327814,0.24780896,0.0008711837,0.7392597],"study_design_scores_gemma":[0.0004234215,0.00021002073,0.104291126,0.000035694557,0.000038234117,0.000060071492,0.00013729825,0.5883047,0.0013537718,0.29810145,0.006687276,0.00035692615],"about_ca_topic_score_codex":0.0007105521,"about_ca_topic_score_gemma":0.00000879567,"teacher_disagreement_score":0.7389028,"about_ca_system_score_codex":0.000026904181,"about_ca_system_score_gemma":0.000036472924,"threshold_uncertainty_score":0.28000152},"labels":[],"label_agreement":null},{"id":"W2026638829","doi":"10.1109/ismvl.2013.40","title":"Noise-Tolerant Model of a Ternary Inverter Based on Markov Random Field","year":2013,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ternary operation; Noise (video); Computer science; Inverter; Noise immunity; Electronic engineering; CMOS; Markov chain; Markov random field; Spice; Field (mathematics); Markov model; Markov process; Algorithm; Electrical engineering; Mathematics; Artificial intelligence; Engineering; Telecommunications; Transmission (telecommunications); Machine learning; Voltage; Statistics","score_opus":0.010830345948985576,"score_gpt":0.21248739288795632,"score_spread":0.20165704693897074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026638829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006374495,0.0000065748045,0.9717793,0.006250094,0.000036426245,0.000195728,0.0000017901624,0.00005495134,0.01530066],"genre_scores_gemma":[0.8249772,0.000002122583,0.16987297,0.003647501,0.000019879855,0.00008739303,0.000001456775,0.000003604427,0.00138783],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937946,0.000015563128,0.00015461192,0.00018379575,0.0001527402,0.00011382057],"domain_scores_gemma":[0.9993269,0.0001099025,0.00003670372,0.00041804137,0.000054100947,0.0000543645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060711474,0.00007238679,0.00009711921,0.000051416373,0.000047212303,0.000025215997,0.00039164093,0.00003402812,0.0001819843],"category_scores_gemma":[0.000005638769,0.000054961783,0.00005930317,0.0001060955,0.000017454417,0.00020089929,0.00006425994,0.000059271893,0.00008162314],"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":[0.00015289783,0.002666433,0.0038222224,0.000103161714,0.00007964625,0.0000077543,0.00077277905,0.22116992,0.02807874,0.19646476,0.3023492,0.24433248],"study_design_scores_gemma":[0.00057544734,0.00004805839,0.00071811816,0.000009398346,0.0000015012508,7.8383084e-7,0.000002654499,0.99193835,0.001197674,0.0053287544,0.00010622456,0.00007304771],"about_ca_topic_score_codex":0.00012353068,"about_ca_topic_score_gemma":0.0000016708715,"teacher_disagreement_score":0.81860274,"about_ca_system_score_codex":0.00000838385,"about_ca_system_score_gemma":0.000036446596,"threshold_uncertainty_score":0.22412764},"labels":[],"label_agreement":null},{"id":"W2027203222","doi":"10.1109/cec.2012.6252905","title":"Use of evolutionary computation techniques for exploration and prediction of helicopter loads","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"Defence Science and Technology Group","keywords":"Particle swarm optimization; Evolutionary computation; Computer science; Genetic algorithm; Differential evolution; Evolutionary algorithm; Computation; Genetic programming; Control theory (sociology); Artificial neural network; Mathematical optimization; Artificial intelligence; Algorithm; Machine learning; Mathematics; Control (management)","score_opus":0.05857261462961532,"score_gpt":0.27881530879459965,"score_spread":0.22024269416498432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027203222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008148438,0.00008982506,0.99085826,0.00036874195,0.000050040187,0.00029328637,0.000023279847,0.00007035071,0.00009778767],"genre_scores_gemma":[0.5435235,0.000026826881,0.45627472,0.000018258606,0.000035097677,0.000060882998,0.000023543029,0.0000021672556,0.00003504667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995067,0.000015851147,0.0001995047,0.000102964914,0.00009618813,0.000078772806],"domain_scores_gemma":[0.9995031,0.00008189089,0.000095197116,0.00011799804,0.00017102412,0.00003082372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010720851,0.000049621354,0.000071358336,0.000075864045,0.00005289358,0.000009660833,0.00006318639,0.00003696929,0.0000017933439],"category_scores_gemma":[0.00001190154,0.00004624603,0.000024625042,0.00013822266,0.000039512586,0.0015974317,0.000043439435,0.000020010844,6.389792e-7],"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":[0.000025603684,0.0007999813,0.013740579,0.00014374284,0.000040923773,4.1641353e-8,0.0012329153,0.0008369539,0.03633641,0.7569951,0.012315493,0.17753229],"study_design_scores_gemma":[0.0002833746,0.00031869428,0.0839277,0.00003933117,0.000019761908,0.0000110497,0.00007238413,0.8309382,0.048962127,0.0270221,0.00824906,0.00015617367],"about_ca_topic_score_codex":0.00001702728,"about_ca_topic_score_gemma":4.5451776e-7,"teacher_disagreement_score":0.8301013,"about_ca_system_score_codex":0.0000141299515,"about_ca_system_score_gemma":0.000014847135,"threshold_uncertainty_score":0.18858583},"labels":[],"label_agreement":null},{"id":"W2027249873","doi":"10.1155/2013/578710","title":"Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems","year":2013,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Space–time block code; Computer science; MIMO; Artificial bee colony algorithm; Decoding methods; Block (permutation group theory); Minimum mean square error; Mathematical optimization; Block code; Mathematics; Statistics; Artificial intelligence; Estimator; Telecommunications","score_opus":0.022967987309282987,"score_gpt":0.3076158358688979,"score_spread":0.2846478485596149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027249873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037946552,0.0006286149,0.95810574,0.0008321306,0.00070935144,0.0015456578,0.000013866887,0.00009488377,0.00012319747],"genre_scores_gemma":[0.9633543,0.0000473192,0.03510135,0.00007456169,0.00015279453,0.0012219562,0.000010165001,0.000012933598,0.000024647872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976506,0.00009212637,0.00085256447,0.0006496695,0.00030876556,0.00044623564],"domain_scores_gemma":[0.9984856,0.00066184776,0.00020341515,0.00028893954,0.00027092564,0.0000892662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044841855,0.00020258942,0.00024439354,0.00024891843,0.00023624652,0.00019290937,0.0006386732,0.000087297376,0.000009130038],"category_scores_gemma":[0.00014514463,0.00020914292,0.00006851598,0.0009947035,0.00012613731,0.00084357616,0.00010917209,0.00019382834,0.00016296263],"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":[0.000015926616,0.00023731962,0.00008063448,0.000031429736,0.0000037397842,0.0000021491737,0.0004387896,0.64822006,0.0030987659,0.19142915,0.000012848743,0.15642917],"study_design_scores_gemma":[0.000047346926,0.00008602348,0.0007984077,0.00005790524,0.0000024680237,0.000004230007,0.00050887256,0.9284833,0.005666137,0.06328816,0.0008177128,0.00023943446],"about_ca_topic_score_codex":0.00042413396,"about_ca_topic_score_gemma":0.0010417901,"teacher_disagreement_score":0.9254077,"about_ca_system_score_codex":0.00017846827,"about_ca_system_score_gemma":0.00007112144,"threshold_uncertainty_score":0.8528601},"labels":[],"label_agreement":null},{"id":"W2029064195","doi":"10.1016/j.tcs.2008.09.027","title":"A cyclic binary morphism avoiding Abelian fourth powers","year":2008,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"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 Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Morphism; Abelian group; Mathematics; Binary number; Discrete mathematics; Combinatorics; Algebra over a field; Pure mathematics; Arithmetic","score_opus":0.013840604077014223,"score_gpt":0.23986156262289027,"score_spread":0.22602095854587603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029064195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08454341,0.000052510346,0.90758145,0.0035657322,0.00043497322,0.00016512167,0.000001801632,0.00034953616,0.0033054538],"genre_scores_gemma":[0.7328626,0.000018772747,0.26639596,0.00053602096,0.00013356327,0.000013310939,8.8986525e-7,0.0000072382263,0.00003167264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727553,0.00009525984,0.00029305072,0.0008745011,0.0007619809,0.00069970166],"domain_scores_gemma":[0.99810225,0.00022438113,0.00007392586,0.0010316344,0.00015723154,0.00041057207],"candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.00088361296,0.00020880948,0.00019362663,0.00025873384,0.0021640693,0.00018326294,0.0028354398,0.00005598768,0.00003977123],"category_scores_gemma":[0.00005098839,0.00018225517,0.00009273928,0.0020181814,0.0032455965,0.0009372516,0.0011114369,0.0003212458,0.00031076025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000172402,0.000092809256,0.00008334674,0.000001987792,0.000002970901,0.000040705927,0.00047124006,0.00044198992,0.0013790558,0.9927157,0.00030777935,0.004460671],"study_design_scores_gemma":[0.00026059354,0.00019373318,0.0057625277,0.000022489576,0.000003464512,0.00043878955,0.000010757783,0.8800451,0.0012985026,0.11045939,0.001129608,0.00037501327],"about_ca_topic_score_codex":0.0000070640212,"about_ca_topic_score_gemma":2.0958485e-7,"teacher_disagreement_score":0.8822563,"about_ca_system_score_codex":0.00008734578,"about_ca_system_score_gemma":0.00017555707,"threshold_uncertainty_score":0.999467},"labels":[],"label_agreement":null},{"id":"W2030792885","doi":"10.1007/s10710-010-9113-2","title":"Open issues in genetic programming","year":2010,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":230,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Computer science; Genetic programming; Mainstream; Field (mathematics); Face (sociological concept); Range (aeronautics); Data science; Artificial intelligence; Management science; Operations research; Sociology; Mathematics; Political science","score_opus":0.01086872803388606,"score_gpt":0.2758444820490048,"score_spread":0.26497575401511875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030792885","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76842374,0.008727514,0.21081784,0.0063897134,0.000823268,0.0027258017,0.000006145555,0.0006589267,0.0014270726],"genre_scores_gemma":[0.27879634,0.000086855274,0.7200388,0.000063244675,0.0001259308,0.00036464963,0.0000050108447,0.000014721788,0.00050446513],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983384,0.000041733805,0.0003169158,0.00062818395,0.00018593387,0.0004888031],"domain_scores_gemma":[0.9990349,0.000046079185,0.00008098958,0.00061480113,0.000066241686,0.000157009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003378578,0.00020833188,0.0002172411,0.00012517239,0.00032873408,0.0008186177,0.0013190132,0.000090194706,0.00001826801],"category_scores_gemma":[0.000033022377,0.0001895372,0.000037428592,0.0005684093,0.00010823319,0.00032176365,0.0008531375,0.0002644508,0.000023057866],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023194302,0.00016862559,0.040653452,0.00002428412,0.000008069536,0.000011507455,0.0003426344,0.0000970699,0.00045437462,0.004202815,0.00017748833,0.95385736],"study_design_scores_gemma":[0.0012037434,0.00029108877,0.3173167,0.00007158389,0.000022841454,0.000291475,0.00012030056,0.31660405,0.00020263082,0.028944794,0.3339917,0.0009390831],"about_ca_topic_score_codex":0.0016415959,"about_ca_topic_score_gemma":0.00056682253,"teacher_disagreement_score":0.9529183,"about_ca_system_score_codex":0.000012592942,"about_ca_system_score_gemma":0.00006850283,"threshold_uncertainty_score":0.7893949},"labels":[],"label_agreement":null},{"id":"W2033905545","doi":"10.1007/s10710-014-9234-0","title":"Exploring non-photorealistic rendering with genetic programming","year":2014,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"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; Genetic programming; Rendering (computer graphics); Computer graphics (images); Artificial intelligence; Human–computer interaction","score_opus":0.025256679513746137,"score_gpt":0.22603377179609863,"score_spread":0.2007770922823525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033905545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1500857,0.000622686,0.84788954,0.00022999945,0.00013042174,0.00040458207,0.00000135953,0.00036405306,0.0002716587],"genre_scores_gemma":[0.48849654,0.000057504854,0.5107272,0.000030249865,0.00014526662,0.0004464968,0.0000051105612,0.00002107938,0.000070548434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807775,0.000043979922,0.00029760358,0.00069101714,0.00029200478,0.00059765973],"domain_scores_gemma":[0.9988708,0.00007549956,0.000110475914,0.0006253694,0.00009030715,0.00022752839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023957773,0.00028676045,0.0002433499,0.00012537718,0.00057062344,0.0003975171,0.0005002013,0.000048459693,0.0000029957223],"category_scores_gemma":[0.000023820434,0.00024068405,0.00004813697,0.0005195409,0.00010857424,0.00030180358,0.00023690437,0.0001596995,0.00000977442],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004958591,0.00007898926,0.0076706437,0.00009431245,0.000026062675,0.000009485822,0.00044777963,0.0018129706,0.0001618754,0.0014759843,0.000028754133,0.9881882],"study_design_scores_gemma":[0.0008445355,0.0006276343,0.10487848,0.00017104337,0.00006522827,0.0003413475,0.0001262988,0.8527383,0.00018589343,0.002390951,0.03675673,0.0008735524],"about_ca_topic_score_codex":0.00052980514,"about_ca_topic_score_gemma":0.000048732443,"teacher_disagreement_score":0.98731464,"about_ca_system_score_codex":0.000028690501,"about_ca_system_score_gemma":0.000043962285,"threshold_uncertainty_score":0.9814811},"labels":[],"label_agreement":null},{"id":"W2035498056","doi":"10.1109/cec.2009.4982964","title":"Augmenting artificial development with local fitness","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Fitness function; Computer science; Neighbourhood (mathematics); Process (computing); Fitness landscape; Artificial intelligence; Hill climbing; Artificial life; Function (biology); Fitness approximation; Mathematical optimization; Machine learning; Mathematics; Genetic algorithm; Biology","score_opus":0.014645464186212622,"score_gpt":0.23226685927047466,"score_spread":0.21762139508426204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035498056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010371188,0.000009105677,0.9825214,0.001890906,0.000021695829,0.00005929355,8.6477094e-8,0.00012908995,0.004997207],"genre_scores_gemma":[0.64114976,2.4907538e-7,0.35810727,0.00023515301,0.000025647732,0.000008172726,0.000001421248,0.0000012348044,0.00047109099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999437,0.0000050325375,0.00010350884,0.00017709738,0.00013651539,0.00014087868],"domain_scores_gemma":[0.9997272,0.000009243387,0.000022329903,0.00016149424,0.000035707137,0.00004402017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068841255,0.000055248183,0.00004481086,0.000025475496,0.00018488643,0.000053760665,0.00023564605,0.000014796628,0.000017610206],"category_scores_gemma":[8.9845685e-7,0.000041971944,0.000009853273,0.00021513153,0.00001763018,0.0001786609,0.000035492012,0.00003769583,0.00006502023],"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":[0.0000012543942,0.0001084429,0.000060645423,8.91501e-7,0.0000036389708,0.0000035088742,0.0001553026,0.0006783393,0.00021113026,0.47358358,0.0003814685,0.5248118],"study_design_scores_gemma":[0.00070482754,0.00031642496,0.110302255,0.000050248727,0.000009066231,0.00010982547,0.00033652427,0.71590644,0.04595441,0.05112374,0.07412842,0.0010578057],"about_ca_topic_score_codex":0.0000033576682,"about_ca_topic_score_gemma":0.0000063283364,"teacher_disagreement_score":0.71522814,"about_ca_system_score_codex":0.000023566736,"about_ca_system_score_gemma":0.00006820087,"threshold_uncertainty_score":0.17115662},"labels":[],"label_agreement":null},{"id":"W2036733311","doi":"10.1109/wisp.2007.4447575","title":"A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Genetic programming; Computer science; Wavelet; Contextual image classification; Majority rule; Class (philosophy); Energy (signal processing); Set (abstract data type); Texture (cosmology); Wavelet transform; Image (mathematics); Mathematics; Statistics","score_opus":0.024246397720959607,"score_gpt":0.275863952962709,"score_spread":0.2516175552417494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036733311","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012440572,0.000012666753,0.9963,0.00022436082,0.000008664926,0.00030339067,0.0000025404838,0.000052985284,0.001851371],"genre_scores_gemma":[0.48246467,2.3426176e-7,0.5173619,0.000039662074,0.000014275033,0.000051131843,0.000012011682,0.0000016462064,0.000054450546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927276,0.000010127482,0.0001872933,0.00024323269,0.00015189585,0.00013466239],"domain_scores_gemma":[0.9993239,0.00009908959,0.000080181206,0.00037005258,0.000085962965,0.000040784307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027241523,0.0000602656,0.00009052161,0.00019386617,0.00007935788,0.000022776796,0.00028445924,0.00003506769,0.0000029090704],"category_scores_gemma":[0.00001107336,0.000050069968,0.000099831464,0.000971963,0.000025520605,0.00004044418,0.000016471065,0.000030801275,0.0000010211946],"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":[0.000017405657,0.0012210531,0.004110602,0.000042937285,0.00014254314,3.8399088e-7,0.00014587761,0.020535763,0.0030573204,0.4492973,0.000507412,0.5209214],"study_design_scores_gemma":[0.00010312534,0.00004885066,0.09660052,9.2073753e-7,0.000028125025,2.9073857e-7,0.000020117406,0.90122,0.0005047494,0.00035243886,0.0010631864,0.00005763724],"about_ca_topic_score_codex":0.00001261516,"about_ca_topic_score_gemma":0.000005796906,"teacher_disagreement_score":0.88068426,"about_ca_system_score_codex":0.000018741619,"about_ca_system_score_gemma":0.000025707124,"threshold_uncertainty_score":0.20417939},"labels":[],"label_agreement":null},{"id":"W2037379710","doi":"10.1145/1569901.1570226","title":"Benchmarking coevolutionary teaming under classification problems with large attribute spaces","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Benchmarking; Computer science; Population; Artificial intelligence; Selection (genetic algorithm); Feature (linguistics); Machine learning; Trait; Genetic programming; Metaphor; Data mining; Programming language","score_opus":0.020640128224229705,"score_gpt":0.24580354529586954,"score_spread":0.22516341707163984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037379710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038352725,0.00020745688,0.98024446,0.010261767,0.000046873094,0.00023833456,0.0000040850186,0.00030678028,0.004854944],"genre_scores_gemma":[0.8187232,0.000023677543,0.17984341,0.00048556557,0.000096977106,0.000034640052,0.000034862074,0.0000049262558,0.0007527403],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998773,0.0000274641,0.00018597585,0.00041590777,0.00028335754,0.0003142894],"domain_scores_gemma":[0.99922615,0.000045959823,0.00009517675,0.00042477407,0.0001221729,0.000085780164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021320909,0.00013181973,0.0001035496,0.00008614791,0.00045484837,0.00012467835,0.00041575948,0.00005396103,0.000024967816],"category_scores_gemma":[0.0000036930683,0.0001064146,0.000034254565,0.0006133095,0.000035125016,0.0007218689,0.00006477797,0.00012089859,0.000042861426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017727355,0.0002734719,0.0026798178,0.0000050421254,0.000014824879,0.0000016619582,0.00013780124,0.001699379,0.0010244796,0.98245305,0.0032570858,0.008451641],"study_design_scores_gemma":[0.0004398091,0.00021057352,0.39111987,0.000046403824,0.000010852819,0.000057160807,0.0001732806,0.5697398,0.000092115835,0.020173764,0.017581765,0.00035458113],"about_ca_topic_score_codex":0.000013089903,"about_ca_topic_score_gemma":0.000016177379,"teacher_disagreement_score":0.96227926,"about_ca_system_score_codex":0.00008599161,"about_ca_system_score_gemma":0.000084448766,"threshold_uncertainty_score":0.43394616},"labels":[],"label_agreement":null},{"id":"W2038520478","doi":"10.1016/j.clinbiochem.2008.08.053","title":"Assessing state-of-the-quality of Jaffe creatinine assay results using proficiency testing data","year":2008,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Shared Health","funders":"","keywords":"Interpretability; Overfitting; Fuzzy logic; Artificial intelligence; Computer science; Fuzzy rule; Data mining; Fuzzy set; Mathematics; Machine learning; Algorithm; Artificial neural network","score_opus":0.3562387174778899,"score_gpt":0.46575963967973244,"score_spread":0.10952092220184256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038520478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8998341,0.0000870596,0.09829336,0.00028371997,0.00010328002,0.000105727784,0.0000934248,0.00003999671,0.0011593326],"genre_scores_gemma":[0.7925897,0.0000051709785,0.20718893,0.000024607809,0.00007660565,0.0000017558755,0.000019925217,0.000004035633,0.00008926722],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9975905,0.0001425674,0.0011932612,0.000562145,0.00033836518,0.00017315945],"domain_scores_gemma":[0.9952809,0.001593253,0.0008304128,0.0019333059,0.00028442603,0.00007770012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002290478,0.000102600534,0.00024458324,0.000013100219,0.00021533771,0.000022520077,0.0017082245,0.00008124852,0.0000013645268],"category_scores_gemma":[0.0059460104,0.0000815733,0.0000805904,0.00060510484,0.00048110556,0.00025318755,0.0010663831,0.0002273092,0.0000011211591],"study_design_candidate":"bench_or_experimental","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":[0.000045598605,0.003546168,0.30678052,0.00042389164,0.00009373628,0.00001279765,0.00023753784,0.0009166869,0.5796168,0.0007900204,0.0025007473,0.105035484],"study_design_scores_gemma":[0.0011131972,0.00007812047,0.41434732,0.00032459668,0.000025834153,0.000053537857,0.000049137834,0.39534435,0.1868624,0.0012049954,0.00017694307,0.00041956804],"about_ca_topic_score_codex":0.00003875099,"about_ca_topic_score_gemma":2.8946204e-7,"teacher_disagreement_score":0.39442766,"about_ca_system_score_codex":0.000018895642,"about_ca_system_score_gemma":0.0005487637,"threshold_uncertainty_score":0.7118359},"labels":[],"label_agreement":null},{"id":"W2039519793","doi":"10.1109/wi-iat.2012.33","title":"A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Computer science; Architecture; Task (project management); Set (abstract data type); Artificial intelligence; Quality (philosophy); Hybrid learning; Machine learning; Evolutionary computation; Engineering","score_opus":0.052807967095892486,"score_gpt":0.34034085949843534,"score_spread":0.2875328924025429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039519793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014995731,0.00061887567,0.9755087,0.005384283,0.0011316034,0.00094164524,0.00009179948,0.00040171394,0.0009256731],"genre_scores_gemma":[0.8739976,0.0003680196,0.12222366,0.00068982097,0.0003011008,0.0011516248,0.00010140907,0.000029547993,0.0011372132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970673,0.00013158211,0.0006876672,0.000889565,0.00048197305,0.00074192893],"domain_scores_gemma":[0.9975151,0.0006392961,0.00030862968,0.00070479175,0.0005875972,0.000244626],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000653346,0.0004785373,0.00041503768,0.000817159,0.00043811704,0.00020552172,0.0021919075,0.00026665663,0.00029792127],"category_scores_gemma":[0.00034001181,0.0004212089,0.00019166889,0.0004549549,0.00034821228,0.0005147361,0.0003791128,0.00073773746,0.00019605631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065195476,0.0008733317,0.0024901994,0.000022851254,0.00014545904,0.0000075833473,0.00037548612,0.0025901813,0.002694444,0.5949771,0.0015643369,0.39419383],"study_design_scores_gemma":[0.0005801124,0.0017655756,0.00057324773,0.00023613327,0.00012013129,0.00022757142,0.0009851399,0.4137778,0.15520768,0.05478803,0.37019184,0.0015467393],"about_ca_topic_score_codex":0.000040976753,"about_ca_topic_score_gemma":0.000023442177,"teacher_disagreement_score":0.8590019,"about_ca_system_score_codex":0.0001462597,"about_ca_system_score_gemma":0.00022747107,"threshold_uncertainty_score":0.999824},"labels":[],"label_agreement":null},{"id":"W2041594468","doi":"10.1145/2001858.2002034","title":"Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Genetic programming; Graphics; Computer graphics (images); General-purpose computing on graphics processing units; Net (polyhedron); Graphics processing unit; Artificial intelligence; CUDA; Computer vision; Parallel computing; Mathematics","score_opus":0.07708339317233583,"score_gpt":0.28245169241916546,"score_spread":0.20536829924682964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041594468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051451918,0.00006819402,0.9421309,0.00015599806,0.0000601235,0.00029418967,0.0000013464579,0.0002862995,0.005551021],"genre_scores_gemma":[0.5762417,0.000002848039,0.42348495,0.00013560985,0.000044317316,0.00002561978,0.0000023437135,0.000009508995,0.000053109816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986393,0.000030706135,0.00024110902,0.00037822692,0.00022465941,0.00048602652],"domain_scores_gemma":[0.9992664,0.000018226365,0.000108511274,0.0003475946,0.00014773663,0.00011156926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017704189,0.00013756768,0.00009699339,0.00013480186,0.0006485726,0.00013567726,0.0004783717,0.00004792042,0.000016996237],"category_scores_gemma":[0.000008468891,0.00012829692,0.000034320547,0.0010592005,0.00005556709,0.00032425357,0.00015866142,0.00012841077,0.000008053228],"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":[0.0000033272888,0.00038450462,0.010301586,0.00005940141,0.000045966146,0.000022597036,0.003474506,0.00015507033,0.0014888827,0.42780918,0.00021069887,0.5560443],"study_design_scores_gemma":[0.0003326243,0.00017424274,0.018700154,0.000083272775,0.0000316911,0.00005121072,0.0008618891,0.9625817,0.0020583624,0.0063660955,0.008180396,0.00057839183],"about_ca_topic_score_codex":0.00011955111,"about_ca_topic_score_gemma":0.00002869307,"teacher_disagreement_score":0.9624266,"about_ca_system_score_codex":0.00003574027,"about_ca_system_score_gemma":0.0001334314,"threshold_uncertainty_score":0.52317965},"labels":[],"label_agreement":null},{"id":"W2042336188","doi":"10.1109/cec.2009.4983151","title":"Techniques for evolutionary rule discovery in data mining","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; K-optimal pattern discovery; Business process discovery; Knowledge extraction; Evolutionary computation; Process (computing); Task (project management); Data mining; Evolutionary algorithm; Machine learning; Artificial intelligence; Data science; Work in process; Engineering","score_opus":0.03311982567617482,"score_gpt":0.301686815609515,"score_spread":0.26856698993334016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042336188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093543436,0.00012443514,0.9886997,0.006542278,0.000030375373,0.00020652828,0.00002187929,0.0001633726,0.003276027],"genre_scores_gemma":[0.09376888,0.000015214103,0.90451485,0.0004477351,0.00007468285,0.00004712072,0.000066429726,0.000002439798,0.0010626523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930704,0.0000084757685,0.00014033781,0.0003121813,0.000084631065,0.0001473171],"domain_scores_gemma":[0.99919987,0.00006168585,0.000026432122,0.0006633727,0.00002353268,0.000025129657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001551686,0.000059295457,0.000065458895,0.00006632611,0.000084530206,0.000055112283,0.0009240891,0.000029123621,0.000002777688],"category_scores_gemma":[0.000017218368,0.00005456347,0.000018610272,0.00024320399,0.000014806757,0.0013802322,0.00019509842,0.000037805607,0.000004190103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004325096,0.00034334738,0.0005890297,0.0000050248423,0.0000038980247,0.0000024498677,0.000082139915,0.000040514242,0.0009965859,0.72043616,0.06862295,0.20887356],"study_design_scores_gemma":[0.0003166209,0.000107367196,0.047878522,0.000033364075,0.000003407478,0.00002188556,0.00006418942,0.71518147,0.00081910664,0.16882461,0.066408224,0.00034121625],"about_ca_topic_score_codex":0.000013186224,"about_ca_topic_score_gemma":0.0000066085545,"teacher_disagreement_score":0.71514094,"about_ca_system_score_codex":0.000028467348,"about_ca_system_score_gemma":0.000055568384,"threshold_uncertainty_score":0.22250336},"labels":[],"label_agreement":null},{"id":"W2045697407","doi":"10.1145/2464576.2464652","title":"Label free change detection on streaming data with cooperative multi-objective genetic programming","year":2013,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Streaming data; Data stream; Change detection; Classifier (UML); Retraining; Benchmarking; Artificial intelligence; Concept drift; Machine learning; Data mining; Entropy (arrow of time); Data stream mining","score_opus":0.05146210105270022,"score_gpt":0.2746489021454647,"score_spread":0.2231868010927645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045697407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022244189,0.000038850496,0.97570145,0.00059180637,0.000051481235,0.00090423756,0.000010908855,0.00020175139,0.00025529845],"genre_scores_gemma":[0.46622902,0.000006703159,0.53269804,0.00012731568,0.00008945933,0.0005768105,0.0000126324585,0.000008583949,0.00025145334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901545,0.000029771383,0.000094924064,0.0004948963,0.00016122543,0.0002037158],"domain_scores_gemma":[0.99867994,0.00004995152,0.000050068324,0.0010009081,0.00015073783,0.00006842291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055742483,0.000121777615,0.000081019534,0.000056467536,0.0002710984,0.00015444399,0.0008183532,0.00003431975,0.000013394855],"category_scores_gemma":[0.000016568978,0.00008984784,0.00000933798,0.0004304782,0.000042855296,0.0009127659,0.000373079,0.000105291336,0.00009488315],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023385717,0.0004443864,0.00048336267,0.000004756299,0.000027929531,0.0000026633852,0.00059272384,0.00006752677,0.00040396344,0.003258269,0.00013756496,0.9945745],"study_design_scores_gemma":[0.00068134756,0.00042322016,0.073919855,0.0000231184,0.000007405158,0.00001804859,0.00029636122,0.9218206,0.0015687752,0.00017560029,0.00081193616,0.00025375202],"about_ca_topic_score_codex":0.0010450656,"about_ca_topic_score_gemma":0.00054242974,"teacher_disagreement_score":0.99432075,"about_ca_system_score_codex":0.000041214487,"about_ca_system_score_gemma":0.00002929895,"threshold_uncertainty_score":0.36638886},"labels":[],"label_agreement":null},{"id":"W2046074173","doi":"10.1109/icsmc.2011.6083921","title":"Joint feature selection and hierarchical classifier design","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Artificial intelligence; Feature selection; Computer science; Pattern recognition (psychology); Data mining; Machine learning","score_opus":0.05558979530255956,"score_gpt":0.2335245891294975,"score_spread":0.17793479382693794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046074173","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008480681,0.00002945326,0.9887258,0.0016138032,0.00003404493,0.000083578154,2.0882905e-7,0.00011602493,0.008549058],"genre_scores_gemma":[0.21019371,0.000010930873,0.7877063,0.00020291934,0.000036558824,0.000021295364,4.1427725e-7,0.0000025908719,0.0018252827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955857,0.000026170028,0.00005769681,0.00018800571,0.0000675101,0.0001020633],"domain_scores_gemma":[0.9997492,0.00001743653,0.000015414993,0.00012667292,0.0000285405,0.000062738894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090165515,0.00005090501,0.000044793524,0.00003491069,0.0001225882,0.000028946239,0.00012104866,0.00004235725,0.000035625537],"category_scores_gemma":[0.0000044496146,0.00004026446,0.000015124213,0.00016662959,0.000024696636,0.00018893319,0.00006078848,0.00009669719,0.000025536687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002577759,0.00008016342,0.00025670094,0.000001968925,0.0000074808277,0.0000015084597,0.00035127348,0.0000087826,0.0017271372,0.95661503,0.011859677,0.029087676],"study_design_scores_gemma":[0.0003104939,0.00022808964,0.15510894,0.0000068387667,0.000007204474,0.00019359404,0.000035068082,0.6918325,0.0069812,0.12781212,0.017178299,0.0003056618],"about_ca_topic_score_codex":0.000011503408,"about_ca_topic_score_gemma":0.0000025098861,"teacher_disagreement_score":0.82880294,"about_ca_system_score_codex":0.0000070612746,"about_ca_system_score_gemma":0.00002497066,"threshold_uncertainty_score":0.16419369},"labels":[],"label_agreement":null},{"id":"W2046401452","doi":"10.1145/1389095.1389356","title":"A swarm-based crossover operator for genetic programming","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Crossover; Symbolic regression; Genetic programming; Ant colony optimization algorithms; Computer science; Genetic algorithm; Population; Mathematical optimization; Swarm behaviour; Operator (biology); Selection (genetic algorithm); Genetic operator; Domain (mathematical analysis); Artificial intelligence; Meta-optimization; Mathematics; Machine learning; Biology","score_opus":0.022673445625941906,"score_gpt":0.2614562100317404,"score_spread":0.23878276440579851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046401452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022333298,0.000053906104,0.97594136,0.0009110049,0.000048762362,0.00032846569,0.000002413478,0.00015676847,0.0002240465],"genre_scores_gemma":[0.20803986,0.0000020478244,0.79034567,0.00046415892,0.000055198107,0.0002785808,0.000002405667,0.0000044556864,0.0008076455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993961,0.0000055556375,0.00010571964,0.00022308095,0.00009624616,0.00017331788],"domain_scores_gemma":[0.9995328,0.00003535701,0.000020144013,0.0002686287,0.00008156356,0.00006148631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003981734,0.00006294735,0.00005618448,0.000024957295,0.00031838566,0.000055282006,0.00032013003,0.000024083576,0.000010671215],"category_scores_gemma":[0.0000074860855,0.000054321295,0.00004747353,0.00018886196,0.000039192175,0.0001330289,0.000039244194,0.000028159453,0.000037276543],"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":[0.000037711812,0.0025477707,0.013354449,0.00009646493,0.00008367912,0.000054515713,0.0013840965,0.02451782,0.0041249627,0.638881,0.07543711,0.23948036],"study_design_scores_gemma":[0.0007802686,0.000107821295,0.010967026,0.0000036430129,0.0000028872005,0.000029820201,0.000006192015,0.7603119,0.003402524,0.0010855648,0.22308502,0.000217357],"about_ca_topic_score_codex":0.00001604263,"about_ca_topic_score_gemma":0.0000022966474,"teacher_disagreement_score":0.73579407,"about_ca_system_score_codex":0.000016852257,"about_ca_system_score_gemma":0.0001147284,"threshold_uncertainty_score":0.24487974},"labels":[],"label_agreement":null},{"id":"W2046812432","doi":"10.1111/j.0824-7935.2004.00244.x","title":"Introduction to the Applications of Evolutionary Computation in Computer Security and Cryptography","year":2004,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional; King's College London; University of East Anglia; York University","keywords":"Library science; Computer science; Humanities; Art","score_opus":0.010477644694294994,"score_gpt":0.26256911080717205,"score_spread":0.252091466112877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046812432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004993265,0.00023659322,0.98106325,0.012956,0.000114186565,0.0005258837,0.000009493908,0.000055186447,0.000046165846],"genre_scores_gemma":[0.59921324,0.000018456672,0.40022257,0.00020989784,0.00019380699,0.00011463439,0.000021430305,0.0000040501236,0.0000018994765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986995,0.00004843305,0.00039297526,0.00042329216,0.00027829446,0.00015753564],"domain_scores_gemma":[0.999098,0.000176191,0.00010831183,0.0002639074,0.00028088863,0.000072710594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022663188,0.000119819495,0.0001252856,0.00026669094,0.0001932347,0.000047399917,0.00044964967,0.000041130916,0.0000052780097],"category_scores_gemma":[0.000015170018,0.00010844438,0.000044575252,0.001562586,0.0001472898,0.00028596816,0.00019332804,0.00013866716,0.000037344264],"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":[0.000002207233,0.00010689202,0.00013304525,0.0000074816994,0.000005102887,3.4854622e-7,0.0005788521,0.65008587,0.0000064488363,0.3251434,0.00028605125,0.023644317],"study_design_scores_gemma":[0.00010037389,0.00006677843,0.016264625,0.000017020366,0.0000030383558,0.000037778773,0.000070068876,0.48076466,0.00007549555,0.500683,0.0017881567,0.00012903407],"about_ca_topic_score_codex":0.000058727965,"about_ca_topic_score_gemma":0.000016324724,"teacher_disagreement_score":0.59422,"about_ca_system_score_codex":0.0000684815,"about_ca_system_score_gemma":0.00008691921,"threshold_uncertainty_score":0.44222334},"labels":[],"label_agreement":null},{"id":"W2047089648","doi":"10.1109/nabic.2014.6921896","title":"Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Inference; Genetic programming; Graph; Theoretical computer science; Object-oriented programming; Programming language; Artificial intelligence","score_opus":0.021127722982730027,"score_gpt":0.2563519187251128,"score_spread":0.2352241957423828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047089648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031061205,0.000053190168,0.99530023,0.00050022453,0.00003672343,0.00043371358,0.0000013260311,0.000032098258,0.0005363601],"genre_scores_gemma":[0.6851225,0.0000024298417,0.3147133,0.000068714115,0.000013314556,0.00006797839,0.0000013339354,0.000002535378,0.00000791263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921304,0.00004636324,0.00026971268,0.00016438734,0.00014067271,0.0001658017],"domain_scores_gemma":[0.9987672,0.00041767582,0.0001550557,0.00045295473,0.00018116774,0.000025973028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030021046,0.00007653495,0.00010147254,0.000021849399,0.00023506726,0.000025308187,0.0006233844,0.00002734679,0.0000026091361],"category_scores_gemma":[0.000018776953,0.000042727333,0.00006996212,0.0003726814,0.0001558554,0.00009921916,0.00011625794,0.00006489405,2.9481814e-7],"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":[0.0000016987933,0.000069524394,0.0005212246,0.0000075302487,0.000017220871,2.2219298e-8,0.0004013455,0.09422697,0.00038690786,0.8363466,0.00008597027,0.067934975],"study_design_scores_gemma":[0.000086992884,0.00006950179,0.002997689,0.0000072660023,0.000005681406,0.0000021058227,0.00006944025,0.9705786,0.00009448153,0.025753833,0.00028121477,0.000053204283],"about_ca_topic_score_codex":0.000050293653,"about_ca_topic_score_gemma":0.000014923045,"teacher_disagreement_score":0.8763516,"about_ca_system_score_codex":0.00000395843,"about_ca_system_score_gemma":0.000026795185,"threshold_uncertainty_score":0.18079711},"labels":[],"label_agreement":null},{"id":"W2047588220","doi":"10.1016/j.artmed.2004.01.005","title":"Genetic design of feature spaces for pattern classifiers","year":2004,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics; University of Alberta","funders":"","keywords":"Piecewise; Representation (politics); Piecewise linear function; Computer science; Feature (linguistics); Piecewise linear manifold; Polynomial; Focus (optics); Pattern recognition (psychology); Genetic algorithm; Algorithm; Genetic programming; Artificial intelligence; Mathematics; Machine learning; Mathematical analysis","score_opus":0.070996878088916,"score_gpt":0.3254758482101597,"score_spread":0.25447897012124365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047588220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003693383,0.0002727968,0.98221195,0.013164461,0.00020556575,0.00037165868,0.0000018395367,0.000028080338,0.00005029051],"genre_scores_gemma":[0.7658716,0.000069781825,0.23356166,0.00023389388,0.00015643709,0.00007492509,0.000002020054,0.000005774315,0.00002394489],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900985,0.000025190326,0.00031698408,0.0002663533,0.00018230856,0.00019933001],"domain_scores_gemma":[0.99926174,0.00019993128,0.00009390789,0.00029018044,0.000096882875,0.000057389305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030775942,0.00009722904,0.00016602568,0.00012689031,0.00006401929,0.000013375233,0.00048573856,0.000059844708,0.000010670992],"category_scores_gemma":[0.00009923399,0.00008114375,0.000030802985,0.0005097268,0.0001800844,0.00010709503,0.00003688805,0.00010044348,0.000010698204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022473725,0.00028942464,0.00042857305,0.000041903957,0.000016850427,0.00001403953,0.0038101,0.095253155,0.011344998,0.32741868,0.0009187092,0.5604411],"study_design_scores_gemma":[0.00015126125,0.0005581784,0.0021419344,0.0001678853,0.000011390437,0.000012917843,0.0010071712,0.40616086,0.016817946,0.5721104,0.0006553216,0.00020475032],"about_ca_topic_score_codex":0.00017382539,"about_ca_topic_score_gemma":0.000055954824,"teacher_disagreement_score":0.7621782,"about_ca_system_score_codex":0.000041293948,"about_ca_system_score_gemma":0.0000748307,"threshold_uncertainty_score":0.33089462},"labels":[],"label_agreement":null},{"id":"W2047751098","doi":"10.1145/1569901.1570052","title":"Genetic programming based image segmentation with applications to biomedical object detection","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Western University; Concordia University","funders":"","keywords":"Computer science; Segmentation-based object categorization; Image segmentation; Artificial intelligence; Scale-space segmentation; Segmentation; Computer vision; MATLAB; Genetic programming; Object (grammar); Image (mathematics); Object detection; Process (computing); Image texture; Image processing; Pattern recognition (psychology)","score_opus":0.007487263695768587,"score_gpt":0.254470911652902,"score_spread":0.2469836479571334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047751098","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022430457,0.000009344843,0.99365366,0.0027475234,0.000015594496,0.0007076998,0.0000012374392,0.00030844234,0.00031346775],"genre_scores_gemma":[0.26466277,6.880694e-7,0.734369,0.000436335,0.000050372102,0.00042350896,0.0000065760987,0.0000034636437,0.00004729948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991046,0.000016628857,0.00014451132,0.0003248687,0.00022800679,0.00018136062],"domain_scores_gemma":[0.99939644,0.000022084138,0.00003809087,0.00032369344,0.00008288159,0.00013678907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007596872,0.000089538014,0.00006205584,0.00011993403,0.0002180507,0.00010758261,0.00026964085,0.000028882989,0.000010991071],"category_scores_gemma":[0.0000034175032,0.00007448104,0.000024161136,0.0009740805,0.000027496013,0.00020022271,0.000021750873,0.00005503224,0.00007665361],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042211773,0.00030372763,0.000052499043,0.0000045791007,0.0000048942743,0.000001977499,0.00007626203,0.0003146725,0.05017019,0.0049596443,0.00014269545,0.94396466],"study_design_scores_gemma":[0.0022094166,0.0033283688,0.12365398,0.000045343782,0.00005579234,0.00016408833,0.00025952677,0.67465055,0.1023644,0.0073475535,0.08454737,0.0013736073],"about_ca_topic_score_codex":0.000017176848,"about_ca_topic_score_gemma":0.0000112946145,"teacher_disagreement_score":0.942591,"about_ca_system_score_codex":0.0000546822,"about_ca_system_score_gemma":0.00006027389,"threshold_uncertainty_score":0.30372488},"labels":[],"label_agreement":null},{"id":"W2048368674","doi":"10.1145/1569901.1570216","title":"An evolutionary approach to feature function generation in application to biomedical image patterns","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"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; Canada Research Chairs","keywords":"Feature (linguistics); Computer science; Pattern recognition (psychology); Artificial intelligence; Genetic programming; Image (mathematics); Genetic algorithm; Maximization; Feature vector; Function (biology); Evolutionary algorithm; Mathematics; Machine learning; Mathematical optimization; Biology","score_opus":0.010441436773355003,"score_gpt":0.25097016295539154,"score_spread":0.24052872618203652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048368674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0098670395,0.0000142243125,0.9772185,0.011014243,0.000064308624,0.0005742761,0.000007989662,0.00019027402,0.0010491227],"genre_scores_gemma":[0.5314463,0.000001738278,0.46571788,0.001992766,0.00027886164,0.000202886,0.00022264289,0.0000045696315,0.0001323914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859166,0.00004313048,0.00020347587,0.0006224903,0.0003002875,0.00023897858],"domain_scores_gemma":[0.9990379,0.000008652412,0.000031890682,0.00058487395,0.00009140834,0.0002452671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019220426,0.00012875703,0.00010118485,0.00025971734,0.00014617472,0.00008928126,0.0004978781,0.00009390263,0.00000842448],"category_scores_gemma":[0.000007402713,0.00012316684,0.00002976819,0.0011044242,0.000011278928,0.0006507944,0.000056963403,0.0001199429,0.00011207641],"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":[0.000025914487,0.003517482,0.0009420475,0.000011042919,0.000009368788,0.0000029558803,0.0008729327,0.014534089,0.19125916,0.38312337,0.08718434,0.3185173],"study_design_scores_gemma":[0.00018049868,0.0002603818,0.19296281,0.0000033719914,0.000002429861,0.000012627505,0.000039891725,0.7945813,0.00026043702,0.0023306746,0.009147299,0.0002182929],"about_ca_topic_score_codex":0.000053900472,"about_ca_topic_score_gemma":0.000011247803,"teacher_disagreement_score":0.7800472,"about_ca_system_score_codex":0.00012750234,"about_ca_system_score_gemma":0.00004472646,"threshold_uncertainty_score":0.50225985},"labels":[],"label_agreement":null},{"id":"W2048987585","doi":"10.1145/1569901.1570227","title":"On the evolution of neural networks for pairwise classification using gene expression programming","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pairwise comparison; Gene expression programming; Artificial neural network; Computer science; Artificial intelligence; Classifier (UML); Genetic programming; Machine learning; Binary number; Binary classification; Class (philosophy); Extension (predicate logic); Data mining; Mathematics; Support vector machine","score_opus":0.039585262689449376,"score_gpt":0.2761969989709788,"score_spread":0.23661173628152946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048987585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023132138,0.00006586132,0.97451776,0.0016290861,0.000057542766,0.00043155134,8.4530643e-7,0.00007081523,0.00009437976],"genre_scores_gemma":[0.79244953,0.0000014490768,0.20733075,0.000084793246,0.000060349073,0.000040461997,0.0000033476033,0.0000024663198,0.000026855945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993244,0.000031785225,0.00017328169,0.00020115604,0.00012630165,0.0001430942],"domain_scores_gemma":[0.9993013,0.00011395787,0.000109087654,0.00035331494,0.00009279086,0.0000295091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002038537,0.000069404465,0.00006246112,0.00003713704,0.0002547978,0.000034991393,0.00031551786,0.000039286297,0.0000019543245],"category_scores_gemma":[0.000018817122,0.000046300313,0.000054881577,0.00027561584,0.00002504402,0.00019092226,0.000027669448,0.0000560253,6.4161156e-7],"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":[0.000010091775,0.00020017115,0.000076800825,0.0000029517544,0.0000031806035,9.971045e-8,0.00006630945,0.044525787,0.03484424,0.83983034,0.0006074549,0.07983257],"study_design_scores_gemma":[0.00009272379,0.000084431595,0.002428811,0.000009519037,0.0000030004971,0.000001833096,0.00003124785,0.98424417,0.0018547177,0.011087949,0.00010369161,0.00005790036],"about_ca_topic_score_codex":0.000008403461,"about_ca_topic_score_gemma":5.448286e-7,"teacher_disagreement_score":0.93971837,"about_ca_system_score_codex":0.000065120286,"about_ca_system_score_gemma":0.000024697847,"threshold_uncertainty_score":0.19597246},"labels":[],"label_agreement":null},{"id":"W2049574661","doi":"10.1145/1388969.1389039","title":"Using feature-based fitness evaluation in symbolic regression with added noise","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Symbolic regression; Computer science; Noise (video); Feature (linguistics); Artificial intelligence; Regression; Pattern recognition (psychology); Regression analysis; Symbolic data analysis; Machine learning; Statistics; Mathematics; Genetic programming; Theoretical computer science","score_opus":0.05281059689207678,"score_gpt":0.30403271175052543,"score_spread":0.25122211485844864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049574661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24568148,0.00008767646,0.75192374,0.0009969587,0.000040195388,0.0002972523,8.8266046e-7,0.00008204895,0.0008897867],"genre_scores_gemma":[0.8025365,0.0000030875526,0.19710338,0.000120034165,0.00002763093,0.000049835122,0.000008117685,0.0000045121683,0.00014693962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990576,0.00005785116,0.00010776397,0.0002786413,0.00034865297,0.00014951664],"domain_scores_gemma":[0.9993729,0.000035384506,0.000052532854,0.00036341787,0.00012665363,0.00004913986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018711126,0.00009138045,0.00009017219,0.00011395468,0.00018888165,0.000022150225,0.0002532038,0.00004507519,0.000020752386],"category_scores_gemma":[0.000010322304,0.00006595118,0.000020777516,0.000706492,0.00003920294,0.00030818683,0.000038293096,0.00008582855,0.000006356445],"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":[0.00016536536,0.0030974795,0.10203745,0.00008611858,0.000053179832,0.00018791347,0.0033918326,0.6612221,0.039501354,0.097508244,0.011259996,0.08148897],"study_design_scores_gemma":[0.0006036448,0.000022324395,0.032000426,0.000037989386,0.0000032233804,0.000030554467,0.0000095711,0.96458685,0.001940794,0.0005318751,0.00012114665,0.00011161246],"about_ca_topic_score_codex":0.00007310063,"about_ca_topic_score_gemma":0.000025565103,"teacher_disagreement_score":0.55685496,"about_ca_system_score_codex":0.00008218269,"about_ca_system_score_gemma":0.0002650363,"threshold_uncertainty_score":0.2689411},"labels":[],"label_agreement":null},{"id":"W2049615251","doi":"10.1109/cec.2008.4630825","title":"Linear Genetic Programming GPGPU on Microsoft's Xbox 360","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"General-purpose computing on graphics processing units; Computer science; Benchmarking; Graphics processing unit; Graphics; Central processing unit; Implementation; CUDA; Flexibility (engineering); Genetic programming; Parallel computing; Computer graphics (images); Operating system; Artificial intelligence; Software engineering","score_opus":0.01943988956565462,"score_gpt":0.24381080872537178,"score_spread":0.22437091915971716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049615251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047793247,0.00012866722,0.945273,0.0019647,0.00010409075,0.00021676549,0.0000012180355,0.00036076517,0.0041575725],"genre_scores_gemma":[0.200738,0.000030529263,0.7951508,0.00053233036,0.00014330207,0.00005117909,0.000002705187,0.0000070236265,0.003344138],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992241,0.0000113830065,0.00012953702,0.00028440455,0.00015046984,0.00020006194],"domain_scores_gemma":[0.9994012,0.000031906715,0.00004092438,0.00040150157,0.00004759284,0.000076838274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004115203,0.00008454292,0.000066918605,0.00004156308,0.00027717452,0.000030619496,0.00044009637,0.000033350112,0.000021280266],"category_scores_gemma":[0.000007662334,0.00007341074,0.00004412562,0.00028891044,0.00004980719,0.00011401339,0.00009552585,0.00007833391,0.000445312],"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":[0.000008247943,0.0018334731,0.007446708,0.000024734807,0.00005047132,0.00015384669,0.0013656834,0.0029234176,0.005958607,0.3664641,0.031831864,0.58193886],"study_design_scores_gemma":[0.00091898366,0.0005495567,0.11214185,0.000028273571,0.000008762354,0.00063994015,0.000039018363,0.30087307,0.009607048,0.0063223285,0.56793004,0.00094113674],"about_ca_topic_score_codex":0.000025904976,"about_ca_topic_score_gemma":0.0000021028966,"teacher_disagreement_score":0.5809977,"about_ca_system_score_codex":0.000019829107,"about_ca_system_score_gemma":0.000046195702,"threshold_uncertainty_score":0.57237333},"labels":[],"label_agreement":null},{"id":"W2049783455","doi":"10.1162/evco_a_00016","title":"Classification as Clustering: A Pareto Cooperative-Competitive GP Approach","year":2010,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Benchmarking; Computer science; Machine learning; Cluster analysis; Artificial intelligence; Pareto principle; Benchmark (surveying); Coevolution; Task (project management); Population; Evolutionary algorithm; Genetic programming; Multi-objective optimization; Mathematical optimization; Mathematics","score_opus":0.01837479317535437,"score_gpt":0.2662653856347845,"score_spread":0.24789059245943015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049783455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009073727,0.000063085994,0.9621566,0.0022999693,0.00054236915,0.00049848825,0.000012718225,0.00045667417,0.024896361],"genre_scores_gemma":[0.76604706,0.0000074477603,0.23269528,0.000201824,0.00024395596,0.00018217327,0.00023045646,0.000014012671,0.00037775817],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808,0.00011484996,0.0003780498,0.0006986448,0.00042545903,0.00030299273],"domain_scores_gemma":[0.9985918,0.00014691627,0.00017537782,0.00047680316,0.00045032063,0.00015881009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025196193,0.00022644008,0.00017401678,0.00018498454,0.0006412557,0.00015580085,0.00062030216,0.00013274515,0.00004069699],"category_scores_gemma":[0.00005849807,0.00023792764,0.00008430666,0.0007868877,0.00015900351,0.0010222527,0.00019855138,0.00039002692,0.00041205285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000104213295,0.00041936606,0.00053992035,0.000011592202,0.000023398663,0.000003124413,0.00053367706,0.014443957,0.0033638342,0.96995425,0.0031663268,0.0075301016],"study_design_scores_gemma":[0.0003496135,0.00007327253,0.08481483,0.000007450159,0.000006268199,0.00013480705,0.00016054366,0.89080656,0.000030537743,0.0151918195,0.008151725,0.0002725754],"about_ca_topic_score_codex":0.00003459875,"about_ca_topic_score_gemma":0.00001252654,"teacher_disagreement_score":0.95476246,"about_ca_system_score_codex":0.00011604035,"about_ca_system_score_gemma":0.0002645208,"threshold_uncertainty_score":0.9702408},"labels":[],"label_agreement":null},{"id":"W2049965644","doi":"10.1145/1570256.1570419","title":"Accelerating evolutionary computation with graphics processing units","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; SIMD; Graphics; Stream processing; Parallelism (grammar); Parallel computing; General-purpose computing on graphics processing units; CUDA; Graphics processing unit; Computation; Computer graphics; Real-time computer graphics; Computer architecture; Computer graphics (images); 3D computer graphics; Programming language","score_opus":0.030276365555942947,"score_gpt":0.261478675763429,"score_spread":0.23120231020748602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049965644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060496423,0.00009448568,0.98388416,0.0030915372,0.000016315647,0.000118424854,5.839923e-7,0.00032422907,0.006420604],"genre_scores_gemma":[0.6431523,0.0000030280617,0.35621664,0.00048173286,0.000035377478,0.0000074031245,0.000008671661,0.0000024902308,0.000092373426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992412,0.000016666221,0.0001361447,0.00024187197,0.00020610567,0.00015803825],"domain_scores_gemma":[0.99942994,0.000025030218,0.00006118706,0.00015326252,0.00027351663,0.0000570646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073588424,0.000088386776,0.00006563162,0.00007798064,0.00041891471,0.000121562574,0.000261023,0.000029739966,0.0000031311927],"category_scores_gemma":[0.000005396988,0.000072044,0.000012743251,0.0012212398,0.000027991613,0.00080892624,0.000030458277,0.000092158036,0.000008537781],"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":[0.0000026778002,0.00013961343,0.0004615627,0.0000061713054,0.0000051517304,0.000004497345,0.0002788981,0.0065050037,0.00024206906,0.82961607,0.00169046,0.16104785],"study_design_scores_gemma":[0.0001800478,0.00010720812,0.037714,0.000019496087,0.000002729792,0.000050429575,0.00004178554,0.9386059,0.00006713072,0.022368299,0.0006873121,0.00015568196],"about_ca_topic_score_codex":0.000007773121,"about_ca_topic_score_gemma":0.0000021558237,"teacher_disagreement_score":0.9321009,"about_ca_system_score_codex":0.000023580204,"about_ca_system_score_gemma":0.00013088083,"threshold_uncertainty_score":0.32219955},"labels":[],"label_agreement":null},{"id":"W2050412782","doi":"10.1145/1389095.1389352","title":"Measuring rate of evolution in genetic programming using amino acid to synonymous substitution ratio <i> k <sub>a</sub> /k <sub>s</sub> </i>","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Substitution (logic); Genetic programming; Rate of evolution; Amino acid substitution; Synonymous substitution; Amino acid; Directed evolution; Biology; Evolutionary biology; Genetics; Computer science; Mutation; Gene; Phylogenetics; Artificial intelligence; Genome; Codon usage bias","score_opus":0.029509577000700494,"score_gpt":0.22534541714416914,"score_spread":0.19583584014346864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050412782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50611144,0.00016674356,0.4929949,0.00007793726,0.0000920999,0.00042339807,0.000001922789,0.000091640446,0.00003994091],"genre_scores_gemma":[0.8987727,0.0000613074,0.10084764,0.000065701555,0.00009516813,0.00012630198,0.0000061927544,0.000017194589,0.000007778392],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975275,0.00012670903,0.0006785613,0.0006898809,0.000423925,0.000553436],"domain_scores_gemma":[0.998647,0.000054275908,0.00020821032,0.00061101746,0.00029710267,0.00018239762],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000431154,0.00026887207,0.00029334714,0.00037493126,0.00044521573,0.00007119586,0.0005306816,0.00012064103,0.000001068013],"category_scores_gemma":[0.00005608657,0.00029054814,0.00010427611,0.0017164364,0.00013236611,0.00079575146,0.00022413704,0.00019100617,0.00005720395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000753956,0.000210981,0.0013474027,0.000023054052,0.000010073438,0.00001684015,0.00024456077,0.024680713,0.9522914,0.00548044,0.00002042399,0.01566653],"study_design_scores_gemma":[0.0004107967,0.00009318256,0.02855034,0.00008485748,0.000014827417,0.0001445246,0.000036851547,0.16542226,0.8042911,0.00053470704,0.000053382602,0.00036312695],"about_ca_topic_score_codex":0.0001604952,"about_ca_topic_score_gemma":0.00014633889,"teacher_disagreement_score":0.3926613,"about_ca_system_score_codex":0.00040626054,"about_ca_system_score_gemma":0.0003400274,"threshold_uncertainty_score":0.99995464},"labels":[],"label_agreement":null},{"id":"W2050510386","doi":"10.1145/1102256.1102342","title":"Making soccer kicks better","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Particle swarm optimization; Simulation; Biomechanics; Movement (music); Sports biomechanics; Machine learning","score_opus":0.026960072771231757,"score_gpt":0.28530613971212604,"score_spread":0.2583460669408943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050510386","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014295817,0.000031512256,0.92791295,0.02341465,0.000037944068,0.000037448444,3.2177542e-7,0.00014687194,0.046988722],"genre_scores_gemma":[0.4193225,0.0000015641143,0.57309103,0.0046598013,0.0002047396,0.000012714246,5.507969e-7,0.0000023621233,0.002704742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958193,0.000005305363,0.00007276574,0.00014428496,0.00008292733,0.00011278284],"domain_scores_gemma":[0.9996897,0.000014158579,0.00001476354,0.00023799817,0.000019130524,0.000024273468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045222485,0.00004074903,0.00003219992,0.000022488612,0.000094042196,0.000047654055,0.00031177507,0.000018233834,0.00019646558],"category_scores_gemma":[0.0000012595033,0.000034755984,0.000024172645,0.000114365735,0.0000123432355,0.00029645415,0.00009915083,0.000043658834,0.0006120277],"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":[1.4739179e-7,0.000048434416,0.00043934834,8.5470134e-7,0.000004292983,8.0238544e-7,0.00011510204,0.00014988735,0.0002425738,0.6450602,0.03499516,0.3189432],"study_design_scores_gemma":[0.00011672091,0.000008986118,0.011852183,0.0000033586534,0.000001615248,0.000016006343,0.000008594867,0.3038958,0.00053870335,0.017008439,0.666394,0.00015557368],"about_ca_topic_score_codex":0.00000291597,"about_ca_topic_score_gemma":0.000002946314,"teacher_disagreement_score":0.63139886,"about_ca_system_score_codex":0.000016127467,"about_ca_system_score_gemma":0.000011513615,"threshold_uncertainty_score":0.7866582},"labels":[],"label_agreement":null},{"id":"W2051556969","doi":"10.1007/s10470-014-0425-7","title":"Introduction to the special issue on “High performance analog circuits and design methodologies”","year":2014,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Comparator; Analogue electronics; Computer science; Electronic engineering; Very-large-scale integration; Electronic circuit; Mixed-signal integrated circuit; CMOS; Electrical engineering; Offset (computer science); Engineering; Voltage","score_opus":0.03306112860287096,"score_gpt":0.2650650940384443,"score_spread":0.23200396543557333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051556969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009617753,0.00019659556,0.9817147,0.006661746,0.00018193592,0.00024957999,0.0000030011274,0.00010210015,0.0012725536],"genre_scores_gemma":[0.98001236,0.00008477343,0.013885159,0.0012851798,0.004352191,0.000049334834,0.000009757756,0.000013576702,0.00030766524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827605,0.00023827425,0.00027525803,0.00064563233,0.00024148075,0.00032332813],"domain_scores_gemma":[0.9990038,0.00024662048,0.00011835777,0.00028956306,0.00022054669,0.00012109699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011377234,0.00022073131,0.00023015196,0.00016457244,0.0008521072,0.00036898334,0.0004547017,0.00009416393,0.00003410067],"category_scores_gemma":[0.000118208314,0.00014968993,0.000026086513,0.0008580506,0.00013111235,0.0004645926,0.000077047014,0.0003249888,0.000033324435],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004952213,0.00002580565,0.000048742106,0.000014304137,0.000012683359,0.0000010507999,0.0004349605,0.0028135383,0.0012325898,0.011209019,0.0046585565,0.9795438],"study_design_scores_gemma":[0.00077165704,0.001874582,0.025937771,0.00024906316,0.000089776746,0.00023090323,0.0006485615,0.77292997,0.009223358,0.03368383,0.15318279,0.001177767],"about_ca_topic_score_codex":0.00003828146,"about_ca_topic_score_gemma":0.0000064328065,"teacher_disagreement_score":0.978366,"about_ca_system_score_codex":0.000040606137,"about_ca_system_score_gemma":0.00007978418,"threshold_uncertainty_score":0.65538055},"labels":[],"label_agreement":null},{"id":"W2054685944","doi":"10.1109/cec.2007.4424951","title":"Irreducible complexity in a genetic algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Fitness function; Function (biology); Algorithm; Computer science; Genetic algorithm; Evolving systems; Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Evolutionary biology; Engineering; Biology","score_opus":0.040284693371039154,"score_gpt":0.2903149014944848,"score_spread":0.2500302081234457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054685944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056263926,0.00007552844,0.9810202,0.000766606,0.000072880786,0.00009872773,9.325447e-7,0.000100125486,0.012238582],"genre_scores_gemma":[0.102146655,0.000005211816,0.8970263,0.00021304817,0.00004733446,0.000009610862,0.000001486132,0.0000026708612,0.0005476562],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992364,0.000009790044,0.0001665784,0.00024179216,0.00012166632,0.00022377152],"domain_scores_gemma":[0.9995086,0.000036070604,0.000021699958,0.00033536833,0.00002940755,0.00006882433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023059448,0.000059774797,0.000063829146,0.0000922132,0.00006616329,0.000029669272,0.00040748715,0.000026337397,0.000041128395],"category_scores_gemma":[0.000003532219,0.000057155983,0.000023086252,0.00056328386,0.000040824358,0.00015116224,0.00011271277,0.00006817756,0.000112807546],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.9388565e-7,0.00031874081,0.0017706553,0.0000027762699,0.0000038626995,0.000024062105,0.00021876527,0.00014932401,0.00023479912,0.47854662,0.0017156017,0.517014],"study_design_scores_gemma":[0.0002760787,0.000029518798,0.45822126,0.000004195829,8.2532443e-7,0.00004698484,0.000043550423,0.44392288,0.000524661,0.087394916,0.009355196,0.0001799243],"about_ca_topic_score_codex":0.00020699644,"about_ca_topic_score_gemma":0.00008277334,"teacher_disagreement_score":0.5168341,"about_ca_system_score_codex":0.00003688675,"about_ca_system_score_gemma":0.000032697197,"threshold_uncertainty_score":0.23307534},"labels":[],"label_agreement":null},{"id":"W2055239208","doi":"10.1145/2001576.2001765","title":"Rethinking multilevel selection in genetic programming","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic programming; Computer science; Selection (genetic algorithm); Consistency (knowledge bases); Genetic representation; Genetic algorithm; Class (philosophy); Operator (biology); Evolutionary algorithm; Genetic operator; Evolutionary programming; Artificial intelligence; Theoretical computer science; Machine learning; Mathematical optimization; Mathematics; Meta-optimization","score_opus":0.04628301425624819,"score_gpt":0.25121230617425383,"score_spread":0.20492929191800563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055239208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025911918,0.00002047556,0.9711381,0.000085740954,0.00003184504,0.00011842843,6.356078e-8,0.00014626881,0.0025471274],"genre_scores_gemma":[0.46168867,0.0000016781241,0.5381114,0.000031470998,0.000011186947,0.000029022784,1.4746549e-7,0.0000016303245,0.00012481709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994922,0.000013947043,0.000109105786,0.0001814154,0.00007229349,0.00013099055],"domain_scores_gemma":[0.9998033,0.000011827818,0.000023085473,0.00010731495,0.000027652755,0.000026790394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092090544,0.00004476357,0.000037993555,0.00005955215,0.00007040575,0.000021896554,0.00022969022,0.000028320059,0.00001947243],"category_scores_gemma":[0.000005226543,0.000042313488,0.000014705143,0.00028780027,0.000011486927,0.00019289665,0.00005836243,0.00006923965,0.000028751581],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001323637,0.00027846757,0.02005095,0.000006428725,0.000005146598,0.0000056069443,0.0057854066,0.0001353188,0.00082828145,0.29775843,0.00011748813,0.67502713],"study_design_scores_gemma":[0.00014172246,0.000034181543,0.3762623,0.000008348805,0.0000010722569,0.000021786454,0.00002238182,0.56639224,0.00092257885,0.055007465,0.0010584875,0.00012741637],"about_ca_topic_score_codex":0.00025900526,"about_ca_topic_score_gemma":0.00007624272,"teacher_disagreement_score":0.67489976,"about_ca_system_score_codex":0.000024077437,"about_ca_system_score_gemma":0.000023189488,"threshold_uncertainty_score":0.1725494},"labels":[],"label_agreement":null},{"id":"W2056698664","doi":"10.1109/cec.2010.5586092","title":"Differential evolutionary approach guided by the Functional Constraint Network to solve program synthesis problem","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tallinna Tehnikaülikool; Ryerson University","keywords":"Constraint (computer-aided design); Computer science; Differential evolution; Constraint satisfaction; Constraint satisfaction problem; Evolutionary algorithm; Program synthesis; Constraint satisfaction dual problem; Theoretical computer science; Evolutionary computation; Mathematical optimization; Constraint logic programming; Artificial intelligence; Mathematics","score_opus":0.016515025690731683,"score_gpt":0.23523758487437024,"score_spread":0.21872255918363856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056698664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015809492,0.000021614407,0.97119987,0.00836623,0.00031528433,0.0010058751,0.00001581534,0.000420739,0.017073648],"genre_scores_gemma":[0.14210707,0.000002052069,0.85350996,0.0005317699,0.0005589879,0.0021533456,0.00003790298,0.000011671192,0.0010872626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983908,0.000032795793,0.0002814304,0.00051040424,0.0003732773,0.00041129434],"domain_scores_gemma":[0.99881256,0.00017114711,0.00006764226,0.00062670535,0.0001462648,0.00017565828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021761353,0.00017731081,0.00013092291,0.000035805588,0.0006729266,0.00017615368,0.0008826825,0.00008989872,0.00030381753],"category_scores_gemma":[0.00002350556,0.00011941104,0.00010079661,0.00047855306,0.00017897954,0.00019886783,0.00032704326,0.0002826342,0.00010095808],"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":[0.000002851191,0.00044324851,0.0001570244,0.0000033860665,0.00003178135,2.8170825e-7,0.0000343641,0.0007629361,0.0010502568,0.5753799,0.38043183,0.041702118],"study_design_scores_gemma":[0.00043444603,0.00013843222,0.024038406,0.000014900831,0.00004298311,0.0002964622,0.00008339949,0.6471314,0.0004929883,0.03869139,0.28782812,0.00080711354],"about_ca_topic_score_codex":0.000037883045,"about_ca_topic_score_gemma":0.0000066774674,"teacher_disagreement_score":0.64636844,"about_ca_system_score_codex":0.000031479718,"about_ca_system_score_gemma":0.00012614399,"threshold_uncertainty_score":0.5175675},"labels":[],"label_agreement":null},{"id":"W2057394292","doi":"10.1007/s10710-015-9242-8","title":"Controlling code growth by dynamically shaping the genotype size distribution","year":2015,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Genetic programming; Overfitting; Code (set theory); Mathematical optimization; Probabilistic logic; Symbolic regression; Range (aeronautics); Evolutionary algorithm; Artificial intelligence; Machine learning; Mathematics; Programming language","score_opus":0.012907622848355477,"score_gpt":0.23404573563309522,"score_spread":0.22113811278473974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057394292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040365484,0.0052841455,0.94973314,0.0038725252,0.00012750698,0.00029818868,0.000023186652,0.0001926495,0.0001031912],"genre_scores_gemma":[0.87027895,0.00008617067,0.12902896,0.00015351104,0.00011281738,0.00010860465,0.000038149035,0.000010372657,0.0001824595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887943,0.000055972945,0.0002086536,0.00033182435,0.00021173398,0.0003124033],"domain_scores_gemma":[0.999211,0.00013117139,0.00007594569,0.00028265512,0.00015508085,0.00014413454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003675392,0.00014207684,0.00013204709,0.000011437489,0.00043806477,0.00028255658,0.00044862344,0.000049924023,0.0000018250065],"category_scores_gemma":[0.000109209475,0.00010133291,0.000037147514,0.00026943313,0.00009536616,0.00013187523,0.00017267832,0.000119730365,0.00000972696],"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":[0.00004763442,0.00044632697,0.020601235,0.00008112409,0.00014394322,0.000010721701,0.0012327883,0.0038344278,0.0021333117,0.058936253,0.013094625,0.8994376],"study_design_scores_gemma":[0.0005418739,0.00012649922,0.005625546,0.000013862578,0.000023061015,0.00004195388,0.000046562036,0.9537446,0.00003414732,0.016485477,0.02308891,0.00022749654],"about_ca_topic_score_codex":0.0002677581,"about_ca_topic_score_gemma":0.00001606065,"teacher_disagreement_score":0.94991016,"about_ca_system_score_codex":0.000036415982,"about_ca_system_score_gemma":0.000054659053,"threshold_uncertainty_score":0.4132236},"labels":[],"label_agreement":null},{"id":"W2057680553","doi":"10.1109/cibcb.2014.6845519","title":"Shape control of side effect machines for DNA classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Finite-state machine; Computer science; Task (project management); String (physics); Population; Artificial intelligence; State (computer science); Side effect (computer science); Machine learning; Algorithm; Mathematics; Engineering","score_opus":0.011210802458322,"score_gpt":0.2536865594442309,"score_spread":0.2424757569859089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057680553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006707917,0.00001785167,0.98770714,0.0020147627,0.000052103576,0.00026779558,0.0000039032743,0.00006932988,0.003159224],"genre_scores_gemma":[0.93395406,8.2160585e-7,0.06552048,0.00014536318,0.000058233123,0.00011976918,0.000004745881,0.0000029360383,0.00019361061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995181,0.000030467447,0.00013174888,0.00015853188,0.00007627451,0.00008486994],"domain_scores_gemma":[0.9991893,0.0003870099,0.000060983348,0.00026617246,0.00006695406,0.000029564708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024900358,0.000053634034,0.000092242015,0.000029951025,0.00006791166,0.000016470787,0.00028508797,0.000024392533,0.000007137828],"category_scores_gemma":[0.00004701676,0.000041181505,0.000050342176,0.000101423495,0.000021404065,0.000118349104,0.000021331118,0.000021732829,0.00001460287],"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":[0.0000036576814,0.000037421734,0.0005499011,0.000013254204,0.0000065258455,1.38147405e-8,0.000012206691,0.00005052305,0.019472355,0.85612106,0.0005637027,0.12316937],"study_design_scores_gemma":[0.00038551574,0.0001041734,0.021381598,0.0000026351422,0.000004720688,9.927396e-7,9.151152e-7,0.95887375,0.0025339678,0.012794613,0.003866417,0.000050713206],"about_ca_topic_score_codex":0.0000067073024,"about_ca_topic_score_gemma":0.0000019534568,"teacher_disagreement_score":0.9588232,"about_ca_system_score_codex":0.0000068474737,"about_ca_system_score_gemma":0.000011266576,"threshold_uncertainty_score":0.1679333},"labels":[],"label_agreement":null},{"id":"W2059072591","doi":"","title":"Hyperspectral Image Analysis Using Genetic Programming","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":44,"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; Universities Space Research Association","keywords":"Hyperspectral imaging; Cuprite; Computer science; Remote sensing; Artificial intelligence; Alunite; Shortwave; Pattern recognition (psychology); Genetic programming; Computer vision; Geology; Materials science; Optics","score_opus":0.022910327105113024,"score_gpt":0.2562158108756366,"score_spread":0.23330548377052357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059072591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019033099,0.00014249243,0.977675,0.00050359557,0.000017644164,0.00006355961,4.2524354e-7,0.00013484128,0.0024293123],"genre_scores_gemma":[0.18460533,0.0000063488255,0.81482184,0.000044952034,0.00003415337,0.000007108785,4.6941088e-7,0.000002296974,0.00047750055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937826,0.000010945537,0.00010277809,0.00022444896,0.00011301331,0.00017054578],"domain_scores_gemma":[0.9995571,0.000011368222,0.000025448428,0.00031294025,0.000037116195,0.00005605454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000036046662,0.000058089758,0.0000709646,0.00010271474,0.00014326857,0.000105440115,0.00030265853,0.00001705692,0.00015643255],"category_scores_gemma":[0.0000025168354,0.000053099062,0.000082434075,0.0012098242,0.000026260712,0.00021098767,0.0000612836,0.00003932296,0.00006866275],"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":[0.0000011236689,0.0016900916,0.011599114,0.000019043286,0.0010966075,0.00009773629,0.0030587295,0.02768801,0.021578733,0.42386857,0.0028786538,0.5064236],"study_design_scores_gemma":[0.00004193492,0.000008463725,0.003909328,4.907858e-7,0.000038214534,0.000012804133,0.000022500604,0.99435,0.00013607451,0.00047484855,0.0009213462,0.000083988154],"about_ca_topic_score_codex":0.000044822817,"about_ca_topic_score_gemma":0.0000032174632,"teacher_disagreement_score":0.966662,"about_ca_system_score_codex":0.000026197207,"about_ca_system_score_gemma":0.000005957262,"threshold_uncertainty_score":0.2165317},"labels":[],"label_agreement":null},{"id":"W2059208361","doi":"10.1109/cimsivp.2014.7013278","title":"A comparison of genetic programming feature extraction languages for image classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"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; Genetic programming; Artificial intelligence; Feature extraction; Contextual image classification; Pattern recognition (psychology); Feature (linguistics); Extraction (chemistry); Image (mathematics); Natural language processing; Linguistics","score_opus":0.025762826474082255,"score_gpt":0.34723351948536874,"score_spread":0.3214706930112865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059208361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003939013,0.00006744679,0.99321073,0.0014021342,0.000035954185,0.00024328275,0.0000012702741,0.00008858242,0.0010115555],"genre_scores_gemma":[0.44394857,0.0000012957529,0.5556942,0.000013570644,0.00004011172,0.00006722476,0.00000667025,0.0000022944382,0.00022601246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995099,0.000015826068,0.00012909305,0.0001661921,0.000087962486,0.00009108133],"domain_scores_gemma":[0.9994623,0.00007537499,0.000097327305,0.00024199132,0.000096462354,0.000026550648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010029057,0.00005020863,0.00007675893,0.000036165984,0.00008227623,0.000036755497,0.00020471832,0.00003627934,0.0000029538546],"category_scores_gemma":[0.00002092025,0.000044396624,0.00003600842,0.00014249592,0.000021429283,0.00015745783,0.000021711938,0.000041671185,0.000005974476],"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":[0.0000028247562,0.00028429859,0.0023923302,0.000044181295,0.000008739638,8.1545046e-8,0.00029455798,0.00008863795,0.052138343,0.2532774,0.005175296,0.6862933],"study_design_scores_gemma":[0.00018292056,0.00010044883,0.07920714,0.000007541632,0.000008233405,0.000005014075,0.00020699188,0.86818594,0.007254857,0.0020118752,0.042724825,0.0001042368],"about_ca_topic_score_codex":0.000008304576,"about_ca_topic_score_gemma":0.000005254058,"teacher_disagreement_score":0.8680973,"about_ca_system_score_codex":0.000011498373,"about_ca_system_score_gemma":0.000012587765,"threshold_uncertainty_score":0.18104418},"labels":[],"label_agreement":null},{"id":"W2060396181","doi":"10.1109/iccse.2014.6926455","title":"A framework of design weakness detection through machine health monitoring for the evolutionary design optimization of multi-domain systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Genetic programming; Engineering design process; Domain (mathematical analysis); Process (computing); Evolutionary computation; Computer-automated design; Evolutionary algorithm; Design process; Systems design; Evolutionary programming; Control engineering; Industrial engineering; Artificial intelligence; Work in process; Software engineering; Engineering","score_opus":0.04951176620425373,"score_gpt":0.2998775211468886,"score_spread":0.25036575494263485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060396181","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022074491,0.0011933337,0.99655455,0.0006619348,0.00036885688,0.0011027056,0.000005481972,0.00008013027,0.000010950094],"genre_scores_gemma":[0.30094236,0.00006335656,0.69863015,0.00001562874,0.000080027436,0.00023707616,0.0000015495331,0.000007759913,0.000022113045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867344,0.00025436477,0.0004170934,0.00025228405,0.00021766669,0.00018516241],"domain_scores_gemma":[0.99785966,0.0011531961,0.00031228724,0.00041430807,0.00022128395,0.000039288236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009973614,0.00011409799,0.00019187923,0.00005555742,0.00037422834,0.000025224357,0.00043164607,0.00007109905,0.0000015516038],"category_scores_gemma":[0.00008266702,0.0000852611,0.000062854,0.00045865157,0.0000565153,0.00028106457,0.00006282942,0.00008622162,0.0000010217934],"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":[0.000011772935,0.00009922842,0.000040501945,0.000043929245,0.000018328612,1.9286592e-8,0.00024560594,0.93071437,0.00029730887,0.0635297,0.000047948815,0.004951261],"study_design_scores_gemma":[0.00025610244,0.00018115553,0.0004297229,0.00006111882,0.000006805965,0.000005220816,0.000110517685,0.9916889,0.0012272972,0.0057854876,0.0001633445,0.000084319065],"about_ca_topic_score_codex":0.00030624057,"about_ca_topic_score_gemma":0.0000014164292,"teacher_disagreement_score":0.30092028,"about_ca_system_score_codex":0.00007174632,"about_ca_system_score_gemma":0.00008763256,"threshold_uncertainty_score":0.34768468},"labels":[],"label_agreement":null},{"id":"W2061082504","doi":"10.1145/2330163.2330250","title":"Depictions of genotypic space for evaluating the suitability of different recombination operators","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Subspace topology; Operator (biology); Recombination; Genetic algorithm; Computer science; Space (punctuation); Genotype; Algorithm; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Machine learning; Genetics; Biology","score_opus":0.0462892064301885,"score_gpt":0.3273232280451655,"score_spread":0.281034021614977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061082504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39248782,0.000056860634,0.6058819,0.0009149521,0.000104865896,0.0002819476,0.0000029640803,0.00001599897,0.00025263886],"genre_scores_gemma":[0.92755014,0.0000033333486,0.07218871,0.000012839523,0.000021704422,0.00009932322,0.000002318263,0.0000021075123,0.00011954651],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99942815,0.000051983832,0.0001885826,0.000099647455,0.00012185702,0.00010975571],"domain_scores_gemma":[0.99911416,0.000282377,0.00008351858,0.00031617275,0.00017596586,0.00002778424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005271512,0.00004700886,0.00007399394,0.00002408843,0.00014682926,0.000008045343,0.00024701472,0.00002034122,0.000015192261],"category_scores_gemma":[0.000078137564,0.000030119323,0.00005023766,0.0001814981,0.00003304228,0.00019364603,0.000070782815,0.000032601583,0.0000014189499],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016955426,0.00030694273,0.013090871,0.000014766957,0.000011437176,1.0635124e-9,0.0005539487,0.0001744235,0.0065409346,0.9668632,0.00012196798,0.012319823],"study_design_scores_gemma":[0.0003445818,0.00024275441,0.58441293,0.000008663319,0.000025536685,0.0000015922079,0.00025740665,0.3453384,0.028742366,0.040183738,0.00032111947,0.00012089884],"about_ca_topic_score_codex":0.00002722621,"about_ca_topic_score_gemma":0.0000074629784,"teacher_disagreement_score":0.92667943,"about_ca_system_score_codex":0.00002767034,"about_ca_system_score_gemma":0.000028446957,"threshold_uncertainty_score":0.12282304},"labels":[],"label_agreement":null},{"id":"W2061732391","doi":"10.1109/tmag.2011.2172921","title":"Comparison of Evolutionary and Rule-Based Strategies for Electromagnetic Device Optimization","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Process (computing); Evolutionary algorithm; Evolutionary computation; Induction motor; Electromagnetics; Design process; Control engineering; Artificial intelligence; Electronic engineering; Electrical engineering; Work in process; Voltage","score_opus":0.024938526818554168,"score_gpt":0.2875490835401459,"score_spread":0.2626105567215917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061732391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005766747,0.00056336296,0.99249905,0.00041276697,0.00014889057,0.00031748324,0.000026535752,0.00007745426,0.00018772902],"genre_scores_gemma":[0.61356664,0.000021003721,0.38621202,0.00004100852,0.000021105967,0.00007975661,0.000005030651,0.000006702307,0.000046715337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912655,0.00004140832,0.00025223749,0.0001878739,0.00016171033,0.0002301869],"domain_scores_gemma":[0.9992275,0.00021723293,0.00008291605,0.00025550515,0.00013021626,0.00008661702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009862347,0.00012220068,0.00013978705,0.00011768919,0.00021502166,0.000036168058,0.00019441551,0.00007026271,0.000023783026],"category_scores_gemma":[0.0000029838404,0.00013050134,0.0000524522,0.00030110186,0.0000819964,0.0003216612,0.0000018386133,0.000097014025,0.00000362846],"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":[0.000037609152,0.0017951002,0.00026304752,0.000113328315,0.000030689884,1.6464807e-7,0.0005495289,0.9239222,0.0068361755,0.027964562,0.00056941493,0.0379182],"study_design_scores_gemma":[0.00042289592,0.0005349991,0.0015484701,0.000011756999,0.00004224436,0.00000541244,0.00009263199,0.989768,0.005855918,0.0009533423,0.0006000604,0.00016421787],"about_ca_topic_score_codex":0.000006305842,"about_ca_topic_score_gemma":0.000003873843,"teacher_disagreement_score":0.6077999,"about_ca_system_score_codex":0.000028711513,"about_ca_system_score_gemma":0.000096039206,"threshold_uncertainty_score":0.532169},"labels":[],"label_agreement":null},{"id":"W2063309933","doi":"10.1145/2330163.2330249","title":"An empirical approach to the measurement of interchromosomal distances in the genetic algorithm","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hamming distance; Traverse; Genetic distance; Genetic algorithm; Measure (data warehouse); Computer science; Distance measures; Algorithm; Tree traversal; Population; Distance matrix; Distance measurement; Mutation; Point (geometry); Crossover; Data mining; Mathematics; Artificial intelligence; Genetic diversity; Machine learning; Biology; Genetics","score_opus":0.052107589972097336,"score_gpt":0.2978454653580389,"score_spread":0.24573787538594158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063309933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009194937,0.00018540754,0.9850682,0.002133499,0.00005427084,0.00024371404,0.0000019330662,0.000017532053,0.0031005258],"genre_scores_gemma":[0.7178093,0.000002271759,0.28166932,0.00032759638,0.00008436612,0.00009597221,7.134632e-7,0.000001773206,0.000008654643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902785,0.00010124716,0.00016120746,0.00015602169,0.00037304696,0.00018062213],"domain_scores_gemma":[0.99932724,0.000033613498,0.000026834856,0.0005091814,0.000048681904,0.00005447276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067066366,0.00006434583,0.000064821514,0.000026502832,0.0000833979,0.00003511753,0.0010867065,0.00001743778,0.0000040419536],"category_scores_gemma":[0.0000059645945,0.000031689862,0.000027615433,0.00039898884,0.000040950374,0.0001779505,0.00008952515,0.00007068767,0.000010922577],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008873547,0.006628276,0.016903933,0.000024312538,0.000044598495,0.0000014026776,0.048395228,0.0035652055,0.00066572975,0.28172746,0.013687932,0.62834704],"study_design_scores_gemma":[0.00016456528,0.00014817352,0.4535726,0.000008354873,0.0000069363045,0.000026401627,0.0018332159,0.5179319,0.00033110753,0.0024713525,0.023313098,0.0001923278],"about_ca_topic_score_codex":0.000037285146,"about_ca_topic_score_gemma":0.000017950102,"teacher_disagreement_score":0.7086144,"about_ca_system_score_codex":0.000028665483,"about_ca_system_score_gemma":0.000026805239,"threshold_uncertainty_score":0.20193893},"labels":[],"label_agreement":null},{"id":"W2063595231","doi":"10.1145/2247569.2247586","title":"Interdisciplinary teaching and learning in computing science","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Computer science; Human-centered computing; Data science; Mathematics education; Management science; Engineering ethics; Information science; Psychology; Engineering","score_opus":0.013371540828691904,"score_gpt":0.300043532964328,"score_spread":0.2866719921356361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063595231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38180158,0.00007823235,0.6011537,0.0009190234,0.000063276275,0.00004166563,3.1240834e-8,0.00009108978,0.015851425],"genre_scores_gemma":[0.7950418,7.433529e-7,0.20473503,0.00005302466,0.000035055167,0.0000016883049,1.3897191e-7,0.0000013860353,0.0001311493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936897,0.000040427665,0.00009318049,0.00016685274,0.0001007993,0.00022975873],"domain_scores_gemma":[0.99968415,0.000091405855,0.000022619359,0.00011718884,0.000012375768,0.00007224968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001294691,0.00004508093,0.00004613471,0.000107970336,0.0006714687,0.00007087761,0.0002810418,0.000011832274,0.0000019061874],"category_scores_gemma":[0.000032274922,0.00004022789,0.0000079366955,0.00029398018,0.00007900255,0.0009727352,0.0010784896,0.00026286757,0.000010308081],"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":[3.6133625e-7,0.000098789744,0.050720867,0.0000043382406,0.0000011293121,8.1038087e-7,0.008473316,0.00043424347,0.0019250286,0.6320828,0.000060067854,0.3061982],"study_design_scores_gemma":[0.000081435515,0.000016430295,0.3210489,0.000014238156,4.5516316e-7,0.000035718836,0.000752413,0.6751269,0.00006394716,0.0020648094,0.0006917763,0.00010297455],"about_ca_topic_score_codex":0.00001923465,"about_ca_topic_score_gemma":0.0000016229894,"teacher_disagreement_score":0.6746927,"about_ca_system_score_codex":0.00003127758,"about_ca_system_score_gemma":0.000020631816,"threshold_uncertainty_score":0.5164462},"labels":[],"label_agreement":null},{"id":"W2064889600","doi":"10.1002/cpe.1604","title":"Evolvable hardware design based on a novel simulated annealing in an embedded system","year":2010,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Evolvable hardware; Field-programmable gate array; Scalability; Simulated annealing; Digital electronics; Computer architecture; Electronic circuit; Embedded system; Circuit design; Gate array; Computer engineering; Algorithm; Electrical engineering","score_opus":0.03562519764943902,"score_gpt":0.3304635492285391,"score_spread":0.2948383515791001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064889600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10792071,0.000090905276,0.89089245,0.0003591435,0.00018855125,0.00022830287,0.0000027396172,0.000102922495,0.00021427986],"genre_scores_gemma":[0.89418983,0.000005480068,0.105473705,0.00024191925,0.000027204132,0.000045513185,0.000007282792,0.0000045690826,0.000004490376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887234,0.000089518246,0.00023520445,0.00045367336,0.00017323818,0.00017605441],"domain_scores_gemma":[0.9988274,0.00053078,0.00012635774,0.00022671075,0.00017247039,0.000116275485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003430254,0.00012819938,0.000120766505,0.00009819085,0.00031189187,0.00023957326,0.00020993402,0.00006581852,0.0000030973424],"category_scores_gemma":[0.00013088218,0.00012700005,0.000013197028,0.00037663092,0.00006727111,0.0016483602,0.00004577959,0.00021821284,0.000004273011],"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":[0.00021528236,0.001753051,0.0020068223,0.00016319494,0.000020457699,0.00007507003,0.04205471,0.5395292,0.01698975,0.23323289,0.00015346281,0.16380614],"study_design_scores_gemma":[0.00053164037,0.00013886705,0.0010606145,0.000040889245,0.0000038712396,0.00002898332,0.0015040315,0.99567723,0.0002167217,0.00022057873,0.00041264814,0.00016390446],"about_ca_topic_score_codex":0.000036617814,"about_ca_topic_score_gemma":0.000002013421,"teacher_disagreement_score":0.7862691,"about_ca_system_score_codex":0.000016067472,"about_ca_system_score_gemma":0.00009065047,"threshold_uncertainty_score":0.5178912},"labels":[],"label_agreement":null},{"id":"W2066320437","doi":"10.1145/1570256.1570356","title":"Deployment of CPU and GPU-based genetic programming on heterogeneous devices","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Benchmark (surveying); Graphics; Genetic programming; Implementation; CUDA; Software deployment; General-purpose computing on graphics processing units; Computer architecture; Symmetric multiprocessor system; Central processing unit; Hardware acceleration; Graphics processing unit; Parallel computing; Embedded system; Computer hardware; Field-programmable gate array; Operating system; Artificial intelligence; Software engineering","score_opus":0.01309308493188346,"score_gpt":0.24719778840001194,"score_spread":0.23410470346812848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066320437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25044078,0.00035999285,0.7464684,0.0019647633,0.000021759197,0.00024111633,8.324881e-7,0.00012084591,0.0003814729],"genre_scores_gemma":[0.74676394,0.0000046204914,0.25282323,0.0003686019,0.000009095284,0.000008498786,5.064356e-7,0.0000015324339,0.000019950612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948597,0.00001068325,0.0001110746,0.00017774836,0.00011133497,0.00010320695],"domain_scores_gemma":[0.99965996,0.000024753237,0.000036134203,0.00020695436,0.000025527448,0.000046654448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040207375,0.00005846482,0.000059577054,0.00003536144,0.000059721468,0.000028247114,0.00017869876,0.000016736329,0.0000033984809],"category_scores_gemma":[0.0000016635445,0.00004861997,0.000021000082,0.000117972435,0.000018108143,0.000044229935,0.000022469241,0.00002333311,0.000004246869],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040165964,0.0005122334,0.0020361051,0.000014406742,0.000010673178,0.0000065979716,0.00008549979,0.0050975084,0.0014080302,0.03552064,0.00011203519,0.95519227],"study_design_scores_gemma":[0.0008190405,0.0018705545,0.17071441,0.00005985749,0.000017401677,0.000049640486,0.00001836631,0.7699981,0.03464982,0.0065477784,0.014789678,0.00046539915],"about_ca_topic_score_codex":0.000010675663,"about_ca_topic_score_gemma":0.0000048905313,"teacher_disagreement_score":0.9547269,"about_ca_system_score_codex":0.00000875606,"about_ca_system_score_gemma":0.000017397264,"threshold_uncertainty_score":0.19826649},"labels":[],"label_agreement":null},{"id":"W2066790509","doi":"10.1155/2010/568375","title":"Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology","year":2010,"lang":"en","type":"article","venue":"Journal of Artificial Evolution and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evolvability; Evolutionary algorithm; Evolutionary computation; Human-based evolutionary computation; Computer science; Modern evolutionary synthesis; Computation; Evolutionary developmental biology; Interactive evolutionary computation; Artificial intelligence; Evolutionary programming; Evolutionary biology; Biology; Algorithm","score_opus":0.018196221574497944,"score_gpt":0.29362645588265945,"score_spread":0.2754302343081615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066790509","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74304837,0.00088408677,0.2505736,0.003786593,0.00023750369,0.00065688475,0.00002564071,0.00001751104,0.0007698031],"genre_scores_gemma":[0.9357553,0.00020564426,0.063933425,0.000013565808,0.0000598972,0.000018161229,0.0000029438888,0.0000034654154,0.0000075698104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99860924,0.000053752483,0.00084066263,0.00020526303,0.0001521587,0.00013889145],"domain_scores_gemma":[0.9988734,0.0001010648,0.00040711722,0.00021083246,0.00032577102,0.00008182292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005555906,0.00009039362,0.00023616035,0.00035964142,0.00007032045,0.000010654975,0.00026872233,0.000094130846,0.000009837359],"category_scores_gemma":[0.00007326055,0.00008589899,0.000036343765,0.0007940335,0.00019073887,0.00027100553,0.00010102695,0.00024098731,0.0000011754463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000325507,0.0011971018,0.063992746,0.000032005722,0.000017657665,9.184339e-7,0.00045914954,0.00015546194,0.11308757,0.71996886,0.00007928482,0.100976676],"study_design_scores_gemma":[0.0006906974,0.00013043094,0.7882175,0.000037758455,0.000011462713,0.00006278688,0.00022139336,0.024291445,0.002786263,0.16964369,0.013707037,0.0001995514],"about_ca_topic_score_codex":0.00006078327,"about_ca_topic_score_gemma":0.000094946365,"teacher_disagreement_score":0.72422475,"about_ca_system_score_codex":0.00006587373,"about_ca_system_score_gemma":0.00023733116,"threshold_uncertainty_score":0.3502859},"labels":[],"label_agreement":null},{"id":"W2066959433","doi":"10.1016/j.neunet.2009.06.043","title":"Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming","year":2009,"lang":"en","type":"article","venue":"Neural Networks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial neural network; Computer science; Genetic programming; Artificial intelligence; Biological neuron model; Representation (politics); A priori and a posteriori; Set (abstract data type); Fitness function; Function (biology); Evolutionary computation; Computation; Genetic algorithm; Machine learning; Algorithm","score_opus":0.030335719353274667,"score_gpt":0.24682649595986822,"score_spread":0.21649077660659355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066959433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031692978,0.0005900296,0.96621394,0.000371701,0.00018071104,0.00057159027,0.0000020624382,0.00032385133,0.00005310719],"genre_scores_gemma":[0.61184156,0.000011119754,0.38711968,0.00057991233,0.00035553405,0.000033517026,0.000030635223,0.000017401677,0.000010639935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974142,0.00013050105,0.0004406304,0.0008632798,0.00040702557,0.00074436853],"domain_scores_gemma":[0.9987719,0.000112951944,0.00019043584,0.0005404737,0.00015180843,0.00023240224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014289455,0.00035982762,0.0002676288,0.000093325776,0.00052968715,0.0003325441,0.00090304273,0.00008583906,0.0000018325517],"category_scores_gemma":[0.000005532967,0.00032363558,0.000112611204,0.00079947984,0.00011775898,0.0007190098,0.00017865095,0.0005049022,0.0000011460426],"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":[0.000013185486,0.00012708908,0.00027309376,0.0000025560926,0.0000080432865,0.00002150212,0.0000354149,0.8571272,0.0000028789218,0.0026574277,0.000050759296,0.13968085],"study_design_scores_gemma":[0.00023404368,0.00019620187,0.0026932575,0.000011061014,0.000015431915,0.00046403377,0.0000075245175,0.9941712,0.000002144203,0.0017876007,0.00004140163,0.00037606954],"about_ca_topic_score_codex":0.000037766862,"about_ca_topic_score_gemma":0.0000027401472,"teacher_disagreement_score":0.5801486,"about_ca_system_score_codex":0.000060324674,"about_ca_system_score_gemma":0.000040390365,"threshold_uncertainty_score":0.99992156},"labels":[],"label_agreement":null},{"id":"W2067050690","doi":"10.1007/s10710-010-9102-5","title":"Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms","year":2010,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Speedup; Implementation; Parallel computing; Video game; Genetic programming; Artificial intelligence; Multimedia; Programming language","score_opus":0.014990666441981625,"score_gpt":0.2570535366019504,"score_spread":0.24206287015996877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067050690","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8105397,0.0014756256,0.18685699,0.00018230811,0.00016217784,0.00060948264,0.000003399959,0.00012990205,0.00004040359],"genre_scores_gemma":[0.4531089,0.00006886727,0.54654825,0.000031051226,0.000077394725,0.00008942855,0.0000028566444,0.0000142631,0.000059002927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840826,0.000018002083,0.00036964702,0.0005295962,0.00024879255,0.0004257099],"domain_scores_gemma":[0.9990242,0.00007266907,0.00015675901,0.00046855328,0.00008813623,0.00018968113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020453485,0.00024169305,0.00025083637,0.00011615652,0.00032014563,0.00014035753,0.00033430776,0.000098746976,0.000004842576],"category_scores_gemma":[0.000023954077,0.00020392645,0.000054953907,0.00027861985,0.00021335877,0.00013392935,0.0002422392,0.00021869877,0.000003711232],"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":[0.00003435098,0.0004179252,0.025368063,0.00014144911,0.0000604604,0.000011976894,0.0006906282,0.0033210618,0.003345305,0.0066516898,0.000031081097,0.959926],"study_design_scores_gemma":[0.0016675263,0.00053571584,0.020295672,0.000085951935,0.000056680372,0.00027886944,0.00006899942,0.95734024,0.000788201,0.008201698,0.010138689,0.00054173014],"about_ca_topic_score_codex":0.00024183498,"about_ca_topic_score_gemma":0.00003214538,"teacher_disagreement_score":0.95938426,"about_ca_system_score_codex":0.000012858384,"about_ca_system_score_gemma":0.0000641105,"threshold_uncertainty_score":0.8315879},"labels":[],"label_agreement":null},{"id":"W2067190192","doi":"10.5555/338219.338265","title":"A practical algorithm for recovering the best supported edges of an evolutionary tree (extended abstract)","year":2000,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Tree (set theory); Algorithm; Evolutionary algorithm; Algorithm design; Theoretical computer science; Mathematical optimization; Mathematics; Artificial intelligence; Combinatorics","score_opus":0.031765176822524915,"score_gpt":0.302588497937053,"score_spread":0.2708233211145281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067190192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004719715,0.000058987353,0.98159385,0.0056327656,0.00009537403,0.0004828752,0.000044213626,0.0001300242,0.0072421874],"genre_scores_gemma":[0.06302251,0.0000455529,0.93387246,0.00015748956,0.00017210362,0.0001665165,0.0000348087,0.000010457098,0.0025181111],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998917,0.000025545687,0.0002907696,0.00032176217,0.00021274439,0.00023218246],"domain_scores_gemma":[0.9988873,0.0002625573,0.000080799786,0.00055141025,0.0001305055,0.00008741652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024300283,0.00011113297,0.00012082622,0.00004053353,0.00025379917,0.00004804566,0.0005117778,0.000059493483,0.0002581429],"category_scores_gemma":[0.000021092885,0.00008190279,0.0000859915,0.00025448928,0.000091453854,0.00078249496,0.000054006694,0.00010584228,0.000046986104],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067565074,0.00054772216,0.000008543696,0.0000041529784,0.000020168287,0.0000024090116,0.00006809043,0.00008854538,0.00024298072,0.024114132,0.0033866803,0.9715098],"study_design_scores_gemma":[0.0005098649,0.00034007838,0.017403204,0.000011477702,0.000023269564,0.00016289947,0.00019589245,0.9157203,0.0019122849,0.018042667,0.04543376,0.00024433795],"about_ca_topic_score_codex":0.00007701671,"about_ca_topic_score_gemma":0.000013303744,"teacher_disagreement_score":0.9712655,"about_ca_system_score_codex":0.000029541556,"about_ca_system_score_gemma":0.00016122631,"threshold_uncertainty_score":0.3339899},"labels":[],"label_agreement":null},{"id":"W2067667602","doi":"10.3758/bf03192772","title":"NUANCE 3.0: Using genetic programming to model variable relationships","year":2006,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"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; University of Alberta","funders":"Government of Canada","keywords":"Computer science; Genetic programming; Set (abstract data type); Artificial intelligence; Machine learning; Human–computer interaction; Cognitive science; Programming language; Psychology","score_opus":0.37795273108280475,"score_gpt":0.5393659313395897,"score_spread":0.1614132002567849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067667602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015145172,0.0001541248,0.9828353,0.00031690364,0.000051524843,0.0007449378,0.0000035515893,0.00013559664,0.00061291707],"genre_scores_gemma":[0.016993066,0.0000022347672,0.9809486,0.000014247317,0.00009268836,0.00073354604,0.000003399779,0.000017078319,0.0011951326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972024,0.00080321287,0.0002874548,0.00054167135,0.00056256494,0.00060268247],"domain_scores_gemma":[0.9982089,0.00036198474,0.000042448184,0.00080375414,0.0003947498,0.0001881559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038930415,0.00011317578,0.0001245808,0.000279072,0.00092885847,0.00023915853,0.00087665796,0.00008123996,0.000008980934],"category_scores_gemma":[0.00014450497,0.00011815763,0.00004406043,0.002109257,0.00008164969,0.00032691055,0.00042179239,0.00044865557,0.000033807035],"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":[0.0000053660174,0.0009061234,0.0069606635,0.000023370834,0.00000754489,0.00001946536,0.00041193413,0.13141628,0.11123176,0.51498216,0.0010226271,0.23301274],"study_design_scores_gemma":[0.00010431953,0.000035491776,0.020386806,0.000016488893,0.0000068167783,0.000024501294,0.000027592456,0.93416154,0.0013244922,0.037221603,0.006491153,0.00019920536],"about_ca_topic_score_codex":0.0003586187,"about_ca_topic_score_gemma":0.00000635833,"teacher_disagreement_score":0.8027453,"about_ca_system_score_codex":0.00018245722,"about_ca_system_score_gemma":0.00027815413,"threshold_uncertainty_score":0.7144122},"labels":[],"label_agreement":null},{"id":"W2068310942","doi":"10.1007/s10015-005-0346-8","title":"Chemical Genetic Programming – evolutionary optimization of the genotype-to-phenotype translation set","year":2005,"lang":"en","type":"article","venue":"Artificial Life and Robotics","topic":"Evolutionary Algorithms and Applications","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":"Simon Fraser University","funders":"National Institute of Information and Communications Technology","keywords":"Genetic programming; Translation (biology); Computer science; Set (abstract data type); Genotype; Genetic algorithm; Phenotype; Mathematical optimization; Artificial intelligence; Genetics; Mathematics; Biology; Machine learning; Programming language; Gene","score_opus":0.02389712210003538,"score_gpt":0.2468132428227845,"score_spread":0.2229161207227491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068310942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00755435,0.0003018592,0.98115736,0.010564598,0.00008488858,0.00023379635,0.000003600384,0.00003968459,0.00005986127],"genre_scores_gemma":[0.44271034,0.000014348125,0.5569627,0.00015220135,0.0001337889,0.000007706287,0.000004261666,0.0000040871832,0.00001055312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992923,0.000023386368,0.00022089804,0.00017896538,0.00015607955,0.00012837055],"domain_scores_gemma":[0.999542,0.00003439979,0.000057121983,0.00021847864,0.00007528852,0.000072740084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000675654,0.00006981126,0.00007467692,0.000031784322,0.00017685926,0.00002050731,0.00021629198,0.000048329406,0.0000036988388],"category_scores_gemma":[0.00002019106,0.000056958987,0.00003278744,0.00034474448,0.0000525722,0.00011912253,0.0000755571,0.00005891548,0.000008510202],"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":[0.0000036178917,0.00006761603,0.00019125642,0.0000049269306,0.0000064161504,8.960009e-8,0.0003037473,0.9057023,0.0011738784,0.02972848,0.00012789489,0.062689774],"study_design_scores_gemma":[0.00006697895,0.000020991696,0.0038903125,0.0000065983345,0.000012571899,0.0000032697124,0.000014712742,0.99167395,0.00038082368,0.002111705,0.0017227156,0.00009539825],"about_ca_topic_score_codex":0.000007102254,"about_ca_topic_score_gemma":0.000004847079,"teacher_disagreement_score":0.435156,"about_ca_system_score_codex":0.000015369116,"about_ca_system_score_gemma":0.000065197404,"threshold_uncertainty_score":0.232272},"labels":[],"label_agreement":null},{"id":"W2068466712","doi":"10.1145/1830483.1830694","title":"Interday foreign exchange trading using linear genetic programming","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Foreign exchange market; Profitability index; Currency; Genetic programming; Profit (economics); Foreign exchange; Genetic algorithm; Trading strategy; Algorithmic trading; Computer science; Value (mathematics); Econometrics; Mathematical optimization; Business; Economics; Monetary economics; Financial economics; Artificial intelligence; Microeconomics; Mathematics; Machine learning; Finance","score_opus":0.028386266137864602,"score_gpt":0.27504591806854506,"score_spread":0.24665965193068046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068466712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05953167,0.000031927833,0.93672496,0.00026130714,0.00015987638,0.00015271993,5.1804795e-7,0.00016946188,0.0029675737],"genre_scores_gemma":[0.40032908,0.0000012579695,0.59928787,0.00006595281,0.00014951907,0.000019012248,7.018839e-7,0.000004660399,0.00014194955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930614,0.000009343643,0.00013268326,0.00023955692,0.00010752166,0.00020476768],"domain_scores_gemma":[0.9994945,0.000024576218,0.0000366723,0.00032509892,0.000040314302,0.00007885942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010952768,0.000079247686,0.000065131615,0.000059837264,0.00017973609,0.000080589474,0.00044928837,0.000040969106,0.00006202052],"category_scores_gemma":[0.0000067922897,0.00007071102,0.00004201021,0.00026081453,0.000033822253,0.00025192514,0.0001356226,0.00013257525,0.00002356706],"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":[0.0000010490771,0.00020698654,0.0016465645,0.000024775329,0.000018709212,0.000012905653,0.000875034,0.00013141685,0.030389046,0.42522597,0.0006928304,0.5407747],"study_design_scores_gemma":[0.00008863579,0.000022424474,0.0008657013,0.0000051896877,0.000003068255,0.000070296104,0.000025577378,0.97845757,0.0012387998,0.0057289475,0.013367489,0.00012631192],"about_ca_topic_score_codex":0.00003274361,"about_ca_topic_score_gemma":0.0000129468335,"teacher_disagreement_score":0.97832614,"about_ca_system_score_codex":0.0000138517835,"about_ca_system_score_gemma":0.0000276057,"threshold_uncertainty_score":0.28835118},"labels":[],"label_agreement":null},{"id":"W2068717754","doi":"10.1145/1569901.1596275","title":"Design &amp; Implementation of Real-time Parallel GA Operators on the IBM Cell Processor","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","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":"Brock University","funders":"Brock University","keywords":"Computer science; Parallel computing; Crossover; Scalability; SIMD; Population; Knapsack problem; Genetic algorithm; Algorithm; Operating system; Artificial intelligence","score_opus":0.0259628656432935,"score_gpt":0.28696804086064576,"score_spread":0.2610051752173523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068717754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021293461,0.000013213551,0.96419203,0.00686216,0.000015595404,0.00047533074,0.0000023786008,0.000088092034,0.0070577427],"genre_scores_gemma":[0.6005562,0.00003952659,0.39615458,0.00077263825,0.000034965968,0.00007955962,0.000008075166,0.0000049318037,0.0023495161],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931675,0.000042421565,0.00016671358,0.00018324463,0.00016531459,0.00012554781],"domain_scores_gemma":[0.9994318,0.00007473415,0.00006609591,0.00032161488,0.000072594,0.000033185886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020819747,0.00007281812,0.00006557984,0.000030371672,0.00013854858,0.000037219535,0.00042777578,0.000019758283,0.00013353096],"category_scores_gemma":[0.000003086566,0.00004697597,0.0000251503,0.00027367176,0.000016336744,0.00016726939,0.000027420005,0.00003931069,0.0001302249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015804704,0.0008495454,0.0001573878,0.000012912195,0.000021792048,0.0000011551759,0.0025417013,0.011702336,0.089339994,0.765373,0.09947385,0.030510524],"study_design_scores_gemma":[0.0031567558,0.0026918848,0.059960835,0.000060522238,0.000056593206,0.000022929731,0.0010990743,0.3858053,0.37797844,0.1475956,0.019890668,0.0016813693],"about_ca_topic_score_codex":0.00003717191,"about_ca_topic_score_gemma":0.0000025287345,"teacher_disagreement_score":0.6177774,"about_ca_system_score_codex":0.00001652427,"about_ca_system_score_gemma":0.000069859365,"threshold_uncertainty_score":0.19156244},"labels":[],"label_agreement":null},{"id":"W2069152178","doi":"10.1145/1569901.1570119","title":"Soft memory for stock market analysis using linear and developmental genetic programming","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Genetic programming; Long memory; Profit (economics); Econometrics; Parallel computing; Economics; Microeconomics; Artificial intelligence","score_opus":0.021146752539593153,"score_gpt":0.27160162002449556,"score_spread":0.2504548674849024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069152178","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032503005,0.000082529805,0.9665547,0.00030923667,0.00001160136,0.00021536002,0.0000013397702,0.00006360695,0.00025863977],"genre_scores_gemma":[0.09446853,0.0000028862648,0.9048765,0.00015056854,0.000029376395,0.000018583009,0.0000025661252,0.0000021558874,0.00044886136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939364,0.000008265726,0.00012691278,0.00023830793,0.00008511453,0.00014775326],"domain_scores_gemma":[0.9997102,0.00003550304,0.000030114288,0.00012456317,0.00004197745,0.00005761872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000096004485,0.00006797386,0.00008540957,0.0000859367,0.00020306332,0.000058955193,0.00016619726,0.000023112556,0.000012732116],"category_scores_gemma":[0.00000654265,0.00006285219,0.000044021657,0.00045285356,0.00001650758,0.00013684675,0.000050407536,0.00002562665,0.0000013100148],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004533502,0.00016738598,0.0025972112,0.000010988033,0.00015634739,0.0000023810157,0.00035003317,0.0020996474,0.0008185331,0.0033854393,0.0008548001,0.9895527],"study_design_scores_gemma":[0.000114358714,0.000027490032,0.022534674,0.0000016308226,0.000040087576,0.000012890197,0.00003587848,0.97474116,0.00008784389,0.0006144196,0.0016822033,0.00010736374],"about_ca_topic_score_codex":0.000008615957,"about_ca_topic_score_gemma":0.0000050137214,"teacher_disagreement_score":0.9894453,"about_ca_system_score_codex":0.000024782965,"about_ca_system_score_gemma":0.00004253075,"threshold_uncertainty_score":0.2563038},"labels":[],"label_agreement":null},{"id":"W2069303594","doi":"10.1007/s10710-010-9114-1","title":"Developments in Cartesian Genetic Programming: self-modifying CGP","year":2010,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Genetic programming; Iterated function; Sequence (biology); Cartesian coordinate system; Theoretical computer science; Adder; Mathematical optimization; Graph; Algorithm; Artificial intelligence; Mathematics","score_opus":0.007554399215886767,"score_gpt":0.2345407968960176,"score_spread":0.22698639768013082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069303594","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85740685,0.0025507712,0.13565081,0.000857244,0.0006066837,0.0012686036,0.0000028810475,0.0008265301,0.0008296134],"genre_scores_gemma":[0.4668539,0.00003611616,0.53268355,0.00003415176,0.00007068235,0.00022529229,0.0000043624027,0.00001310565,0.00007883038],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.997999,0.000039942952,0.0003834834,0.00067447266,0.00026054482,0.0006425231],"domain_scores_gemma":[0.9990605,0.000046445984,0.00009577592,0.00050497893,0.00007948659,0.00021280971],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028181094,0.00026399994,0.00021854392,0.00018789117,0.0004098576,0.0003557913,0.00062343944,0.0001183478,0.0000045999764],"category_scores_gemma":[0.000022520284,0.00025116355,0.00004741302,0.000673224,0.00008015221,0.00019800305,0.0002896026,0.0003129878,0.000020795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017465387,0.00022888395,0.120935425,0.000052339812,0.00001760124,0.000018237546,0.00086296594,0.00010206677,0.00046469795,0.0020195856,0.000035481033,0.87526095],"study_design_scores_gemma":[0.001242134,0.0001825518,0.61936486,0.0000672879,0.000033073466,0.00036945794,0.000111117464,0.24895456,0.00018773852,0.004784314,0.12364548,0.001057404],"about_ca_topic_score_codex":0.00031854672,"about_ca_topic_score_gemma":0.00022743408,"teacher_disagreement_score":0.87420356,"about_ca_system_score_codex":0.000030448913,"about_ca_system_score_gemma":0.0001303508,"threshold_uncertainty_score":0.99999404},"labels":[],"label_agreement":null},{"id":"W2070535092","doi":"10.1145/2001858.2001963","title":"Bloat control in genetic programming with a histogram-based accept-reject method","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Compute Canada","keywords":"Genetic programming; Histogram; Tree (set theory); Computer science; Population; Mathematical optimization; Genetic algorithm; Histogram equalization; Control (management); Dynamic programming; Artificial intelligence; Machine learning; Algorithm; Mathematics; Image (mathematics)","score_opus":0.020192687049978682,"score_gpt":0.25021413935087194,"score_spread":0.23002145230089327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070535092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016732953,0.000065154694,0.99502903,0.0003686308,0.00002384147,0.0003958189,6.7618095e-7,0.00017514719,0.002268414],"genre_scores_gemma":[0.34074453,5.6229607e-7,0.65869635,0.0002457914,0.000010848753,0.00020904829,6.5867454e-7,0.000005131162,0.00008707735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902165,0.000060095274,0.00016925327,0.00034422037,0.00014784312,0.0002569106],"domain_scores_gemma":[0.9993356,0.000061923725,0.000054372664,0.0004150211,0.00005599175,0.0000770901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018513459,0.00011213295,0.00012926821,0.000106570595,0.00007840849,0.00004235773,0.0004968902,0.00003603341,0.000058681402],"category_scores_gemma":[0.000005025897,0.00008620963,0.000039981813,0.0005484489,0.00003489597,0.00015798019,0.00003885212,0.0000909454,0.000030200126],"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":[0.00006256336,0.0020038283,0.03815665,0.0000321976,0.00006114477,0.000093784794,0.001759915,0.002382678,0.000946538,0.14113747,0.0006647354,0.8126985],"study_design_scores_gemma":[0.0028139541,0.0004519092,0.06373738,0.000025127823,0.000026519718,0.000055277604,0.00007696274,0.91251695,0.0014210775,0.0039820303,0.014326668,0.0005661601],"about_ca_topic_score_codex":0.0005204788,"about_ca_topic_score_gemma":0.0001689236,"teacher_disagreement_score":0.91013426,"about_ca_system_score_codex":0.000042128766,"about_ca_system_score_gemma":0.00010067832,"threshold_uncertainty_score":0.35155267},"labels":[],"label_agreement":null},{"id":"W2071988634","doi":"10.1145/1102256.1102262","title":"Generalized benchmark generation for dynamic combinatorial problems","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Sequence (biology); Test case; Mathematical optimization; Optimization problem; Theoretical computer science; Algorithm; Machine learning; Mathematics","score_opus":0.01602578299514038,"score_gpt":0.2577925186605712,"score_spread":0.2417667356654308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071988634","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00723614,0.00008977128,0.98534924,0.0051931725,0.0004155047,0.00043117668,0.000004525133,0.00014430702,0.0011361893],"genre_scores_gemma":[0.32115313,0.000017867396,0.67563903,0.00034744892,0.0005674278,0.00037129808,0.00007236705,0.000006624181,0.00182482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992836,0.000013963389,0.00016775372,0.00026125947,0.00011532144,0.0001580766],"domain_scores_gemma":[0.99951446,0.000024111603,0.00004228075,0.0002804158,0.00008833977,0.000050394232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013714717,0.000076532895,0.00007278973,0.000036594796,0.00021004803,0.000079300975,0.00031577967,0.000041401785,0.000030157189],"category_scores_gemma":[0.0000060746756,0.000069725735,0.000049230093,0.00015241237,0.00001410529,0.00035911877,0.00005120239,0.000034911627,0.00003169898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.331641e-7,0.00008741065,0.000006216732,0.0000022340253,0.0000053291483,4.3801336e-8,0.000043620985,0.002003965,0.0076293913,0.964391,0.010699355,0.01513071],"study_design_scores_gemma":[0.00051489787,0.000034061086,0.00010463897,0.0000010323068,0.0000024043065,0.0000024521428,0.0000011717868,0.89571327,0.0006480454,0.025710203,0.077167384,0.000100412784],"about_ca_topic_score_codex":0.000010547888,"about_ca_topic_score_gemma":0.000022697026,"teacher_disagreement_score":0.93868077,"about_ca_system_score_codex":0.000053861546,"about_ca_system_score_gemma":0.000043786607,"threshold_uncertainty_score":0.2843333},"labels":[],"label_agreement":null},{"id":"W2072164538","doi":"10.1142/s0218213006002990","title":"AGGREGATION OF MULTIPLE REINFORCEMENT LEARNING ALGORITHMS","year":2006,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reinforcement learning; Learning classifier system; Robustness (evolution); Artificial intelligence; Instance-based learning; Machine learning; Algorithm; Unsupervised learning; Robot learning; Fault tolerance; Architecture; Distributed computing; Robot","score_opus":0.041193865031591316,"score_gpt":0.30338059112739435,"score_spread":0.262186726095803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072164538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013635508,0.00008961613,0.9837059,0.0010368731,0.0006533962,0.000083120656,0.000002740369,0.000021346203,0.00077150285],"genre_scores_gemma":[0.930924,0.00004378688,0.0683669,0.00003385989,0.00048074,0.000004604373,0.000008367887,0.0000052453233,0.0001324677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980469,0.000039440296,0.000948406,0.00014331574,0.0006836814,0.0001382981],"domain_scores_gemma":[0.99770665,0.00023001699,0.00073800544,0.00014649602,0.0011312667,0.00004754671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044117574,0.00009900195,0.0001504648,0.00023649378,0.00007737794,0.00013813261,0.00094678183,0.000045548957,0.00004966975],"category_scores_gemma":[0.00019839963,0.00009316202,0.00014147424,0.00026207414,0.00006921451,0.0009092891,0.0001053584,0.00018108726,0.000029471987],"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":[0.000026840853,0.00021335656,0.00039564885,0.000003038711,0.000047308687,0.000017250215,0.00022208938,0.24839415,0.0072434032,0.2960344,0.00017265498,0.44722986],"study_design_scores_gemma":[0.00012237697,0.00024437002,0.0014584538,0.000088689674,0.000011245119,0.00010881361,0.00020626545,0.713887,0.17904927,0.09989379,0.004738632,0.0001910883],"about_ca_topic_score_codex":0.000088116634,"about_ca_topic_score_gemma":0.0000082842935,"teacher_disagreement_score":0.91728854,"about_ca_system_score_codex":0.000091059854,"about_ca_system_score_gemma":0.00010076495,"threshold_uncertainty_score":0.3799037},"labels":[],"label_agreement":null},{"id":"W2072396808","doi":"10.1038/nrg1921","title":"From artificial evolution to computational evolution: a research agenda","year":2006,"lang":"en","type":"review","venue":"Nature Reviews Genetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":153,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Memorial University of Newfoundland","funders":"Francis Crick Institute","keywords":"Biology; Evolutionary biology; Computational biology","score_opus":0.1358435689049677,"score_gpt":0.4411245088382254,"score_spread":0.30528093993325767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072396808","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.500369e-7,0.65268993,0.34450176,0.00030418945,0.0005450755,0.0012866796,0.00017703556,0.00008382433,0.00041125016],"genre_scores_gemma":[0.0000035916335,0.80605805,0.18974902,0.000095316456,0.0024248546,0.0006045139,0.0005787693,0.000041126907,0.0004447343],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940736,0.0010126961,0.0014949698,0.001477604,0.0012521432,0.000688987],"domain_scores_gemma":[0.99646163,0.00047555022,0.00047458452,0.0016752016,0.00064990175,0.0002631562],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015200771,0.0005860284,0.0014785668,0.00061678275,0.000585165,0.00027684594,0.0024245253,0.0010393508,0.000031794487],"category_scores_gemma":[0.00016953124,0.0005188991,0.00067129853,0.003913251,0.000111364534,0.0001511413,0.00077375333,0.002097185,0.0021198597],"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":[4.914955e-7,0.000114906514,5.982899e-7,0.00082297117,0.00002686672,0.000004728992,0.000021130887,0.00020874449,7.247067e-7,0.04406506,0.086724654,0.86800915],"study_design_scores_gemma":[0.000045868983,0.000050790706,0.00002947568,0.002265711,0.00012224496,0.00002428489,0.000002218996,0.0025650528,2.688889e-7,0.026815912,0.96759236,0.0004858338],"about_ca_topic_score_codex":0.000057838526,"about_ca_topic_score_gemma":0.0000316856,"teacher_disagreement_score":0.88086766,"about_ca_system_score_codex":0.001076461,"about_ca_system_score_gemma":0.0011991009,"threshold_uncertainty_score":0.99972624},"labels":[],"label_agreement":null},{"id":"W2072513289","doi":"10.1109/cec.2010.5586297","title":"Fast and effective predictability filters for stock price series using linear genetic programming","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Predictability; Computer science; Genetic programming; Profit (economics); Time series; Linear programming; Econometrics; Machine learning; Statistics; Mathematics; Economics; Algorithm","score_opus":0.010676467769720017,"score_gpt":0.2600443149090939,"score_spread":0.2493678471393739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072513289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25016603,0.000012374254,0.74861556,0.0002669069,0.00007628309,0.00072760915,0.000004877983,0.00008493052,0.000045412562],"genre_scores_gemma":[0.2162718,8.464194e-7,0.7833603,0.000024248951,0.000073441115,0.00020357435,0.0000015006475,0.000004040035,0.000060234033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993583,0.000013588408,0.00010600575,0.00028705603,0.000074979944,0.00016004933],"domain_scores_gemma":[0.99945337,0.000104890314,0.00003639054,0.00024889156,0.000087630426,0.00006883323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001304186,0.00007793729,0.00007337239,0.00002384,0.00023880179,0.00006525153,0.0001843525,0.000038581587,0.0000031371364],"category_scores_gemma":[0.00003405695,0.000067861416,0.000026749365,0.00014036409,0.00008714193,0.00030273953,0.000120449804,0.00008129022,9.439505e-7],"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":[0.000032264088,0.00055472617,0.036690753,0.00024324538,0.00007341343,0.0000020283585,0.0024862322,0.0018106851,0.044482738,0.084364615,0.0002550099,0.8290043],"study_design_scores_gemma":[0.00025298944,0.00018445216,0.07275566,0.0000060893317,0.000009179408,0.000043182827,0.000049641323,0.9184553,0.0018190594,0.0027285207,0.0035271714,0.000168786],"about_ca_topic_score_codex":0.000024025892,"about_ca_topic_score_gemma":0.000015954354,"teacher_disagreement_score":0.9166446,"about_ca_system_score_codex":0.000012927548,"about_ca_system_score_gemma":0.000035143155,"threshold_uncertainty_score":0.2767308},"labels":[],"label_agreement":null},{"id":"W2073442062","doi":"10.4028/www.scientific.net/amr.201-203.2536","title":"Some Improvements of Genetic Programming in Data Fitting","year":2011,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Association of Emergency Physicians","funders":"","keywords":"Crossover; Genetic programming; Mutation; Genetic algorithm; Computer science; Constant (computer programming); Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Machine learning","score_opus":0.1372211397897137,"score_gpt":0.3915060183106332,"score_spread":0.2542848785209195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073442062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95842266,0.00055948267,0.039022993,0.00019688894,0.00024856467,0.0011106269,0.000047362853,0.00007965043,0.00031178075],"genre_scores_gemma":[0.63021433,0.000093543924,0.36945802,0.00000721017,0.000042265077,0.000114489725,0.000011212309,0.000006308547,0.000052636726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986075,0.00008910562,0.00030254247,0.00037199195,0.0002835539,0.000345294],"domain_scores_gemma":[0.9987549,0.000059044756,0.00006706134,0.0009703887,0.00010410042,0.00004449489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009719775,0.00006111302,0.00011231857,0.00012937833,0.00008229217,0.000035917896,0.0014940393,0.00002841302,0.000016664517],"category_scores_gemma":[0.00007776237,0.00005877548,0.000008115971,0.00039318283,0.000069978065,0.0006810669,0.0012803855,0.00007963938,0.00001973148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016539088,0.00034509954,0.00054396584,0.00009469243,0.000008566158,0.000012536777,0.00051581085,0.000017461145,0.7657884,0.034366984,0.00004087452,0.19824907],"study_design_scores_gemma":[0.0011640635,0.00033130197,0.046727404,0.00017233608,0.000003038278,0.00000801118,0.00025073643,0.006991698,0.8511213,0.08892987,0.0039373394,0.00036289098],"about_ca_topic_score_codex":0.00021583727,"about_ca_topic_score_gemma":0.00000956813,"teacher_disagreement_score":0.33043504,"about_ca_system_score_codex":0.000024707198,"about_ca_system_score_gemma":0.00006290246,"threshold_uncertainty_score":0.27763218},"labels":[],"label_agreement":null},{"id":"W2075980255","doi":"10.1145/1389095.1389111","title":"Enhanced generalized ant programming (EGAP)","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Ant colony optimization algorithms; Heuristic; Artificial intelligence; Context (archaeology); Ant colony; Mathematical optimization; Mathematics","score_opus":0.021783328372327267,"score_gpt":0.2533766446284753,"score_spread":0.23159331625614804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075980255","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03490676,0.000051981235,0.95904505,0.0009352286,0.0000671673,0.00011818085,3.20246e-7,0.0002862255,0.004589069],"genre_scores_gemma":[0.42424932,0.000028241991,0.5726932,0.0001958107,0.00005984966,0.00005747742,0.0000020290806,0.0000028003733,0.0027112407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993797,0.000010848964,0.00011113841,0.00021130197,0.00012337018,0.00016361641],"domain_scores_gemma":[0.99955136,0.000015173122,0.000026213665,0.0003006298,0.000046587935,0.000060041835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003881685,0.000060852894,0.000063699285,0.000028680784,0.00023217332,0.000026184218,0.0003416797,0.000021241645,0.000029936009],"category_scores_gemma":[0.0000043981818,0.00005101517,0.000036545098,0.00027913298,0.000033136774,0.00022277373,0.00008579723,0.000041213665,0.00011197618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011816807,0.00020413246,0.00007335198,0.0000028560085,0.000011048919,0.000015773723,0.00046349986,0.00009049367,0.011390974,0.8458984,0.0045547853,0.13729352],"study_design_scores_gemma":[0.002034367,0.00024512128,0.016880482,0.000017603194,0.000009465438,0.00064678985,0.00006941468,0.40274152,0.09349804,0.026178919,0.45643342,0.0012448379],"about_ca_topic_score_codex":0.000031606123,"about_ca_topic_score_gemma":0.0000024243748,"teacher_disagreement_score":0.8197195,"about_ca_system_score_codex":0.000015554746,"about_ca_system_score_gemma":0.000042424377,"threshold_uncertainty_score":0.20803382},"labels":[],"label_agreement":null},{"id":"W2076222444","doi":"10.1007/s10710-011-9151-4","title":"Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces","year":2011,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","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":"Dalhousie University; University of Waterloo","funders":"","keywords":"Computer science; Genetic programming; Artificial intelligence; Machine learning; Classifier (UML); Benchmarking; Linear subspace; Subspace topology; Preprocessor; Data mining; Mathematics","score_opus":0.022636277193069976,"score_gpt":0.24684379850308896,"score_spread":0.22420752131001898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076222444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43366534,0.0069538024,0.5564885,0.00031135106,0.0003660924,0.0014595353,0.000010008149,0.00057141227,0.00017391275],"genre_scores_gemma":[0.67155,0.00005666574,0.32771736,0.000047220736,0.00013768335,0.00027127998,0.000012083219,0.000022020355,0.0001856853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99715835,0.0001308663,0.00047621623,0.00095362065,0.00040837683,0.0008725675],"domain_scores_gemma":[0.99855864,0.00006888191,0.0001769307,0.00075938343,0.000160382,0.00027579902],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044316018,0.00038544185,0.00032777546,0.00019224023,0.0009535263,0.0003377041,0.00076309254,0.0001102902,0.000033536577],"category_scores_gemma":[0.000018833216,0.00035348712,0.000099110366,0.0008117052,0.00014839804,0.00027920984,0.00061580096,0.00023210685,0.000031450294],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001823814,0.0033397994,0.67301893,0.000120316035,0.00027223103,0.000084054125,0.0043088286,0.00039762046,0.000068298345,0.0071973735,0.0004435551,0.31073076],"study_design_scores_gemma":[0.0014523001,0.0014174911,0.7797883,0.00007662844,0.00019115712,0.00032385797,0.0013114442,0.19899948,0.000021926468,0.0073917555,0.007974736,0.0010509136],"about_ca_topic_score_codex":0.000803592,"about_ca_topic_score_gemma":0.00015046915,"teacher_disagreement_score":0.30967984,"about_ca_system_score_codex":0.000052245014,"about_ca_system_score_gemma":0.0001013156,"threshold_uncertainty_score":0.9998917},"labels":[],"label_agreement":null},{"id":"W2076309380","doi":"10.1007/s00366-012-0282-x","title":"A novel engineering tool for creative design of fluid systems","year":2012,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Evolutionary Algorithms and Applications","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 Guelph","funders":"","keywords":"Computer science; Computational fluid dynamics; Engineering design process; Process (computing); Fluid dynamics; Industrial engineering; Computation; Computational model; Domain (mathematical analysis); System dynamics; New product development; Simulation; Engineering; Mechanical engineering; Artificial intelligence; Aerospace engineering; Mathematics","score_opus":0.013563772146586784,"score_gpt":0.20010273082926125,"score_spread":0.18653895868267448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076309380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017969962,0.00023680268,0.996905,0.000035179895,0.0004028281,0.00041819733,0.0000063602047,0.00019115185,0.000007443181],"genre_scores_gemma":[0.28642398,0.0000019260765,0.7132786,0.000006468124,0.00012133193,0.00013673985,0.0000024688911,0.000013457681,0.000015061984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992273,0.000005567368,0.0001783387,0.00017172356,0.0001357793,0.0002813097],"domain_scores_gemma":[0.9992421,0.00027796277,0.00005522459,0.00027120666,0.000074333904,0.00007912159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019219419,0.00014096203,0.00016717387,0.0001002107,0.00004205878,0.0000338602,0.0003223884,0.000033359807,3.6151798e-7],"category_scores_gemma":[0.000019064271,0.00012888336,0.000038553615,0.00027807328,0.000010568851,0.0003499523,0.000055820597,0.00005780049,0.0000017614608],"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":[0.0000025425102,0.000048626105,0.000024334859,0.00005029876,0.00004211014,2.3601062e-7,0.00023984008,0.9439899,0.0063329684,0.048850663,0.00014620507,0.0002722764],"study_design_scores_gemma":[0.00028493322,0.00008160081,0.0016470903,0.00008983673,0.00000783418,0.000029570476,0.0000053507483,0.99545777,0.0010884572,0.0000035128264,0.0011300407,0.00017398882],"about_ca_topic_score_codex":0.0000071378913,"about_ca_topic_score_gemma":1.477235e-8,"teacher_disagreement_score":0.284627,"about_ca_system_score_codex":0.000047316324,"about_ca_system_score_gemma":0.000026288903,"threshold_uncertainty_score":0.5255711},"labels":[],"label_agreement":null},{"id":"W2076857376","doi":"10.1145/2463372.2463489","title":"Benchmarking pareto archiving heuristics in the presence of concept drift","year":2013,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Computer science; Pareto principle; Heuristic; Genetic programming; Bottleneck; Multi-objective optimization; Scope (computer science); Genetic algorithm; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.013512755751674599,"score_gpt":0.23644282077051193,"score_spread":0.22293006501883733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076857376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016767291,0.00005357536,0.97209454,0.0019129117,0.000049507238,0.00021620198,0.0000011463701,0.000024010627,0.008880798],"genre_scores_gemma":[0.90472424,0.000005815878,0.094958425,0.00013628422,0.000032189055,0.00004071659,0.0000010711127,0.0000013475927,0.000099894474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939275,0.00004433108,0.00015314194,0.00013741424,0.00014842735,0.00012391314],"domain_scores_gemma":[0.99919194,0.00036855033,0.000041270912,0.00033850758,0.00003819639,0.000021555747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013856558,0.00004646989,0.000056810768,0.000027432618,0.000063032654,0.000044114426,0.0007430112,0.000013189002,0.000028587507],"category_scores_gemma":[0.000029110475,0.000031407148,0.000019394485,0.0002436632,0.00006288449,0.0001971411,0.00013391777,0.0000752387,0.000008874644],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0710335e-7,0.00012274102,0.008978734,0.000008073343,0.0000038868493,0.0000016552008,0.003436516,0.0017199956,0.0003038809,0.92643815,0.009504714,0.04948144],"study_design_scores_gemma":[0.00008049755,0.00003243832,0.19251691,0.000021669046,0.0000011790409,0.000006958246,0.0002894909,0.7657318,0.00021757433,0.038993903,0.0020094926,0.00009806766],"about_ca_topic_score_codex":0.00026265465,"about_ca_topic_score_gemma":0.000016620535,"teacher_disagreement_score":0.887957,"about_ca_system_score_codex":0.000004996158,"about_ca_system_score_gemma":0.000023187651,"threshold_uncertainty_score":0.1380712},"labels":[],"label_agreement":null},{"id":"W2077218694","doi":"10.1142/s021800140300271x","title":"A Methodology for Feature Selection Using Multiobjective Genetic Algorithms for Handwritten Digit String Recognition","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"","keywords":"Computer science; Classifier (UML); Pattern recognition (psychology); Feature selection; Artificial intelligence; Novelty; String (physics); Genetic algorithm; Machine learning; NIST; Selection (genetic algorithm); Data mining; Speech recognition; Mathematics","score_opus":0.22335852436796474,"score_gpt":0.37945373956079675,"score_spread":0.156095215192832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077218694","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04750274,0.00008445846,0.95053893,0.0005387165,0.00084796816,0.0003349426,0.00012000303,0.00001971827,0.0000125039],"genre_scores_gemma":[0.34005252,0.00008150132,0.6589364,0.00023008103,0.0005816406,0.000055367684,0.00003453587,0.000013120631,0.000014872668],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99868155,0.00011388834,0.0005231079,0.0002823717,0.00021351653,0.00018559354],"domain_scores_gemma":[0.9974339,0.0004899586,0.00044234417,0.00006651052,0.0014835796,0.00008370398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052159716,0.00013906285,0.0001856918,0.00027406306,0.00018861167,0.00019798773,0.00025467185,0.0000948817,0.000019608677],"category_scores_gemma":[0.00035397755,0.00013875788,0.00014373116,0.0001670201,0.000049675444,0.0005246415,0.000028520108,0.0001548996,0.0000046866016],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000459465,0.00012891661,0.00010828682,0.000009698952,0.00008938335,0.0000033244307,0.00028412128,0.00036889227,0.0056475773,0.0010822365,0.000025440035,0.99220616],"study_design_scores_gemma":[0.00062019535,0.000620186,0.00062642,0.00018684544,0.00009651576,0.0010961931,0.0007097646,0.2750097,0.24187613,0.47743046,0.0012606274,0.00046696546],"about_ca_topic_score_codex":0.000020836751,"about_ca_topic_score_gemma":0.000015645675,"teacher_disagreement_score":0.9917392,"about_ca_system_score_codex":0.00009128413,"about_ca_system_score_gemma":0.00008732557,"threshold_uncertainty_score":0.5658382},"labels":[],"label_agreement":null},{"id":"W2077371739","doi":"10.1109/iros.2005.1545150","title":"Enhanced learning classifier system for robot navigation","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sonar; Mobile robot; Obstacle avoidance; Computer science; Artificial intelligence; Robot; Classifier (UML); Maxima and minima; Mobile robot navigation; Obstacle; Robot learning; Machine learning; Robot control; Mathematics","score_opus":0.016427195279683816,"score_gpt":0.2598579548441792,"score_spread":0.24343075956449536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077371739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014307145,0.000023852714,0.98641247,0.001359154,0.00007348079,0.00016294749,5.2232105e-7,0.000283489,0.010253359],"genre_scores_gemma":[0.6722453,9.858486e-7,0.32423693,0.000041439464,0.00013070686,0.00010158553,0.000006409997,0.0000030175845,0.0032335592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994655,0.000012153429,0.00012267656,0.00018995562,0.00008610785,0.00012355653],"domain_scores_gemma":[0.9996322,0.00004733398,0.000043814805,0.00016413924,0.000074779775,0.00003772998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104127015,0.000051734645,0.000053804622,0.000022799717,0.00020640958,0.000051543953,0.00021310216,0.00003055421,0.0000072316616],"category_scores_gemma":[0.00000497756,0.000047317568,0.00003522625,0.00013770397,0.000009312443,0.00031389628,0.00003541344,0.000051463787,0.00010698635],"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":[0.000001375539,0.00003889505,0.0000137821,0.000017429118,0.0000067304727,1.3485656e-7,0.0001756948,0.015627703,0.015380092,0.8153476,0.0010942131,0.15229633],"study_design_scores_gemma":[0.00019209189,0.00002811856,0.00033034012,0.0000145000695,0.0000022917216,0.000005663716,0.00007404945,0.9445164,0.019628156,0.0006780245,0.034429647,0.000100706035],"about_ca_topic_score_codex":0.0000034382958,"about_ca_topic_score_gemma":0.0000014203298,"teacher_disagreement_score":0.92888874,"about_ca_system_score_codex":0.000056337023,"about_ca_system_score_gemma":0.000022261107,"threshold_uncertainty_score":0.19295545},"labels":[],"label_agreement":null},{"id":"W2080638133","doi":"10.1007/s11081-011-9155-1","title":"Planned tournament selection","year":2011,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Evolutionary Algorithms and Applications","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 Guelph","funders":"","keywords":"Tournament; Tournament selection; Selection (genetic algorithm); Mathematical optimization; Computer science; Population; Variety (cybernetics); Fitness proportionate selection; Fitness function; Mathematics; Artificial intelligence; Genetic algorithm","score_opus":0.010765354826782527,"score_gpt":0.18214762195242906,"score_spread":0.17138226712564653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080638133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008343246,0.00002443135,0.99788177,0.00009166006,0.000051481333,0.0000338354,2.1302255e-7,0.000115795905,0.00096646923],"genre_scores_gemma":[0.18273805,0.000042164582,0.8170529,0.000028816825,0.000022612834,0.000012363627,0.0000018779851,0.0000034812267,0.000097725795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997628,0.000002242198,0.000054514912,0.000083100225,0.00003508138,0.00006230677],"domain_scores_gemma":[0.9998796,0.000003977278,0.000012201069,0.000053666667,0.000015946429,0.000034603574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000029670528,0.000036873982,0.000027876953,0.00003830589,0.00005278996,0.000021799819,0.000058009937,0.000015864476,0.000019637868],"category_scores_gemma":[0.0000025372933,0.000037653015,0.0000070639753,0.00012317153,0.000002713301,0.00018308569,0.00002054694,0.000026823433,0.0000028318477],"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":[8.6881334e-7,0.000027509004,0.00025969962,0.000004364745,0.0000065325244,5.760797e-7,0.00034560362,0.9372227,0.00019845812,0.059474524,0.00015174641,0.0023073931],"study_design_scores_gemma":[0.000056263107,0.000016041722,0.0021813102,0.000003059862,0.0000010761044,0.000009908647,0.0000051696757,0.9967233,0.00017857841,0.000055293596,0.00072152645,0.00004845653],"about_ca_topic_score_codex":0.0000034024797,"about_ca_topic_score_gemma":1.9478426e-7,"teacher_disagreement_score":0.18190372,"about_ca_system_score_codex":0.000010079303,"about_ca_system_score_gemma":0.000004375545,"threshold_uncertainty_score":0.15354455},"labels":[],"label_agreement":null},{"id":"W2080991325","doi":"10.1109/cec.2013.6557615","title":"Generative representations for artificial architecture and passive solar performance","year":2013,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Modularity (biology); Computer science; Hierarchy; Generative grammar; Set (abstract data type); Block (permutation group theory); Genetic programming; Generative Design; Artificial intelligence; Evolutionary algorithm; Reuse; Domain (mathematical analysis); Machine learning; Theoretical computer science; Programming language; Engineering; Mathematics","score_opus":0.015860950982682837,"score_gpt":0.25254184844901395,"score_spread":0.2366808974663311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080991325","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024776649,0.0000148855615,0.96387553,0.010076828,0.000041744515,0.00038606135,0.000004138847,0.000053893924,0.0007702595],"genre_scores_gemma":[0.38059726,0.0000105747395,0.6169387,0.00040315764,0.0001399925,0.0006537517,0.000009470461,0.000003997105,0.0012430662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995067,0.000009810936,0.000095872456,0.00020966692,0.00006234069,0.00011562074],"domain_scores_gemma":[0.99957854,0.0000655852,0.000028383858,0.0001836734,0.00009495607,0.000048876704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026427953,0.000056627792,0.000050446444,0.000031278898,0.0002994269,0.000100324454,0.00014885547,0.000021944184,0.000023182389],"category_scores_gemma":[0.000009928967,0.00004556449,0.000020211473,0.000112172325,0.000039492155,0.00032886065,0.000062080486,0.000045249966,0.000027551956],"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":[0.000001843838,0.00008494204,0.00031405542,0.000010291976,0.000022020593,2.6605676e-7,0.0011578328,0.0016461144,0.0071368665,0.62651163,0.014484328,0.34862983],"study_design_scores_gemma":[0.00013178472,0.00007093354,0.010562645,0.0000027210324,0.0000036155304,0.000010443891,0.000102624355,0.8669561,0.0083218515,0.10944574,0.004248913,0.00014260983],"about_ca_topic_score_codex":0.000021965381,"about_ca_topic_score_gemma":0.0000062939557,"teacher_disagreement_score":0.86531,"about_ca_system_score_codex":0.0000069645885,"about_ca_system_score_gemma":0.000023746372,"threshold_uncertainty_score":0.23029798},"labels":[],"label_agreement":null},{"id":"W2085695879","doi":"10.1007/s00371-011-0597-4","title":"Automatic and interactive evolution of vector graphics images with genetic algorithms","year":2011,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Computer graphics; Vector graphics; Artificial intelligence; Computer vision; Computer graphics (images); Graphics; Pixel; Geometric primitive; Algorithm","score_opus":0.014187524661601702,"score_gpt":0.2512205860698633,"score_spread":0.2370330614082616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085695879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18645242,0.00011384792,0.8129633,0.00013448409,0.00006334373,0.00015180187,0.0000020254781,0.00006636064,0.000052411604],"genre_scores_gemma":[0.78395563,0.0000076233455,0.21590525,0.000045059496,0.000049791022,0.000017793272,6.108468e-7,0.0000055404557,0.000012690732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992774,0.00006351668,0.00015901183,0.00021563239,0.00014966883,0.00013475401],"domain_scores_gemma":[0.99936825,0.000090733534,0.00010577849,0.00028862432,0.00010321952,0.000043410215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010681938,0.00010641509,0.00011474535,0.00007462034,0.00012525757,0.00003167205,0.00038061006,0.000025422984,0.0000074077234],"category_scores_gemma":[0.0000023191083,0.00006761091,0.000029768007,0.00032034898,0.00017792602,0.00023215284,0.00020659938,0.000095437674,0.0000077747145],"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":[0.000070587725,0.0019284787,0.009668378,0.00017649605,0.0005333725,0.00003233356,0.01823079,0.00036159746,0.0019754819,0.3803837,0.0015813861,0.5850574],"study_design_scores_gemma":[0.00016769758,0.0003694565,0.3124503,0.000026709446,0.000013916712,0.00005706792,0.000032010692,0.68185854,0.00045853612,0.0044239187,0.0000394826,0.00010235538],"about_ca_topic_score_codex":0.00010151651,"about_ca_topic_score_gemma":0.0000029314704,"teacher_disagreement_score":0.6814969,"about_ca_system_score_codex":0.000019176627,"about_ca_system_score_gemma":0.00003473641,"threshold_uncertainty_score":0.2757093},"labels":[],"label_agreement":null},{"id":"W2086026794","doi":"10.4018/jcini.2011010102","title":"Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines","year":2011,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Mitacs","keywords":"Particle swarm optimization; Computer science; Multi-swarm optimization; Trajectory; Position (finance); Dimension (graph theory); Mathematical optimization; Algorithm; Mathematics; Physics","score_opus":0.023190668675210593,"score_gpt":0.2960848738202672,"score_spread":0.27289420514505663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086026794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50253755,0.00050457823,0.49647468,0.00006965156,0.00011927809,0.00008835881,0.00007354798,0.00000780884,0.00012453922],"genre_scores_gemma":[0.9687908,0.00022500788,0.030817075,0.00009744809,0.00003199685,0.000004895561,0.000009111474,0.0000028560469,0.000020758793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990393,0.000015424195,0.0005512215,0.00007189882,0.00022921355,0.00009293534],"domain_scores_gemma":[0.99723166,0.00056370924,0.00045721652,0.000045084857,0.0016424175,0.000059925398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024299766,0.00009091157,0.00019178173,0.00023559832,0.000055217726,0.00004826782,0.0002931903,0.000029832696,0.000011445188],"category_scores_gemma":[0.00023082738,0.00007141896,0.00009891799,0.00029434948,0.00017569645,0.0008203286,0.00007474046,0.00010088483,0.0000015145878],"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":[0.00064539653,0.00077844394,0.019893685,0.00007642577,0.008216431,0.000024083662,0.059286054,0.0002457245,0.0010384216,0.27190402,0.00004972247,0.6378416],"study_design_scores_gemma":[0.00065483426,0.0006424262,0.06265836,0.00026840423,0.00067116774,0.00016455851,0.0027543125,0.87800235,0.02568413,0.028162694,0.00002461192,0.00031217874],"about_ca_topic_score_codex":0.000017895203,"about_ca_topic_score_gemma":0.000005819421,"teacher_disagreement_score":0.8777566,"about_ca_system_score_codex":0.0000112421285,"about_ca_system_score_gemma":0.000038527498,"threshold_uncertainty_score":0.29123807},"labels":[],"label_agreement":null},{"id":"W2088252348","doi":"10.1016/j.procs.2010.04.111","title":"Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Computer science; Crossover; Computation; Evolutionary computation; Evolutionary algorithm; Benchmark (surveying); Fitness function; Exploit; Architecture; Class (philosophy); Genetic architecture; Genetic algorithm; Theoretical computer science; Artificial intelligence; Gene; Machine learning; Algorithm; Biology; Genetics","score_opus":0.010310407754457884,"score_gpt":0.21874303848188967,"score_spread":0.2084326307274318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088252348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083707795,0.00010956788,0.9153885,0.00019283668,0.00014558165,0.00030760255,0.000018056417,0.000112478716,0.000017581371],"genre_scores_gemma":[0.49541184,0.000004178071,0.50450736,0.000017468896,0.000011357969,0.000029327113,0.0000026577977,0.0000046146156,0.000011195488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820447,0.000018953859,0.0003429265,0.0005942643,0.0005454233,0.00029396836],"domain_scores_gemma":[0.99821043,0.00013902936,0.00024873044,0.0006562451,0.00061117276,0.00013437381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035241368,0.00016187126,0.00019487324,0.00022132312,0.00030562817,0.0000490436,0.0015735957,0.000051235027,0.0000011395309],"category_scores_gemma":[0.00001674474,0.0001446745,0.000038424623,0.001058598,0.00094068516,0.00088075176,0.00034329563,0.00014175056,0.0000029050425],"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":[0.000008003785,0.00049306464,0.0009245739,0.00004590221,0.000015189757,6.966064e-7,0.0007814712,0.7364667,0.1943881,0.036150064,0.0003247392,0.030401459],"study_design_scores_gemma":[0.0002190043,0.00009587513,0.005236419,0.000015282536,0.000004753917,0.000026228035,0.0000047395974,0.9847611,0.004818879,0.004631025,0.000015497775,0.0001711484],"about_ca_topic_score_codex":0.000013392531,"about_ca_topic_score_gemma":0.000004105883,"teacher_disagreement_score":0.41170403,"about_ca_system_score_codex":0.00004383357,"about_ca_system_score_gemma":0.0008207867,"threshold_uncertainty_score":0.58996546},"labels":[],"label_agreement":null},{"id":"W2090163351","doi":"10.1007/s00500-011-0717-0","title":"Stock trading strategy creation using GP on GPU","year":2011,"lang":"en","type":"article","venue":"Soft Computing","topic":"Evolutionary Algorithms and Applications","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":"Carleton University","funders":"","keywords":"Speedup; Computer science; CUDA; General-purpose computing on graphics processing units; Parallel computing; Genetic programming; Graphics processing unit; Graphics; Coprocessor; Operating system; Artificial intelligence","score_opus":0.07605389984430529,"score_gpt":0.2920604816094839,"score_spread":0.21600658176517862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090163351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16187319,0.000043115953,0.83020204,0.000042623713,0.00013502731,0.00010941619,7.653953e-7,0.00020932131,0.007384481],"genre_scores_gemma":[0.8462018,7.858954e-7,0.15353921,0.000074334384,0.00012939455,0.0000025516936,0.0000019527174,0.000007762216,0.00004220936],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990582,0.000038212333,0.00020266364,0.0003254814,0.0001362757,0.00023919402],"domain_scores_gemma":[0.9993922,0.00008332311,0.00010531317,0.00029680703,0.00005449359,0.000067848996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019526205,0.000112917005,0.00009901981,0.000081805585,0.00041295768,0.000074401134,0.00039873738,0.000043782722,0.000014810699],"category_scores_gemma":[0.00001275665,0.0001163307,0.00004803729,0.00033301144,0.000028875189,0.00026740663,0.00009389982,0.00012989486,0.000027090802],"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":[0.000006381613,0.0003866555,0.0023251136,0.000017994851,0.000032382097,0.000017689612,0.003412776,0.017892104,0.0017624403,0.5417447,0.00031061395,0.43209118],"study_design_scores_gemma":[0.00014397634,0.00005892697,0.0070234267,0.000027173402,0.0000039162474,0.000025301506,0.000059571958,0.98085123,0.00047160685,0.011041729,0.00014616578,0.0001469535],"about_ca_topic_score_codex":0.000086395354,"about_ca_topic_score_gemma":0.0000011604711,"teacher_disagreement_score":0.9629592,"about_ca_system_score_codex":0.00005948338,"about_ca_system_score_gemma":0.000045062,"threshold_uncertainty_score":0.47438285},"labels":[],"label_agreement":null},{"id":"W2091525225","doi":"10.3758/bf03192745","title":"NUANCE: Naturalistic University of Alberta Nonlinear Correlation Explorer","year":2006,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Government of Canada","keywords":"Naturalism; Computer science; Correlation; Nonlinear system; Value (mathematics); Space (punctuation); Java; Selection (genetic algorithm); Artificial intelligence; Machine learning; Mathematics; Programming language; Epistemology; Operating system; Physics","score_opus":0.1288585428441985,"score_gpt":0.46415939054078387,"score_spread":0.33530084769658536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091525225","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025654864,0.00012414406,0.9718016,0.00043824833,0.000093629424,0.00027235673,0.0000053272747,0.00004054537,0.0015692802],"genre_scores_gemma":[0.07910639,0.000016831998,0.9175525,0.00000400915,0.000052158255,0.000013879804,0.00002212413,0.000006145009,0.0032259705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9984481,0.00049408356,0.00014630746,0.00028878942,0.00038389736,0.00023882039],"domain_scores_gemma":[0.9984238,0.00061210233,0.000055761386,0.00048695004,0.00036771587,0.00005367033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012158959,0.000068316745,0.0001069744,0.00014143159,0.00026939664,0.000027779164,0.0006553092,0.00006837442,0.000066900226],"category_scores_gemma":[0.00007879367,0.00007062499,0.00005604389,0.00083694415,0.00018237131,0.00029286122,0.0002376771,0.00026995715,0.000050188068],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003413096,0.0016940596,0.017756049,0.00004732122,0.000019282184,0.000046333436,0.0016759577,0.00066723814,0.041263524,0.74084187,0.007189946,0.1887643],"study_design_scores_gemma":[0.0010597552,0.00022557091,0.5030967,0.00005861969,0.00003474585,0.000035420086,0.00053917215,0.37708342,0.007465663,0.0339404,0.075912476,0.0005480806],"about_ca_topic_score_codex":0.0019738628,"about_ca_topic_score_gemma":0.000052539388,"teacher_disagreement_score":0.70690143,"about_ca_system_score_codex":0.000076029624,"about_ca_system_score_gemma":0.000103956496,"threshold_uncertainty_score":0.29839033},"labels":[],"label_agreement":null},{"id":"W2091822809","doi":"10.1109/iat.2006.63","title":"Evaluating Different Genetic Operators in the Testing for Unwanted Emergent Behavior Using Evolutionary Learning of Behavior","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Genetic algorithm; Multi-agent system; Quality (philosophy); Machine learning; Action (physics); Human–computer interaction","score_opus":0.08123059256605626,"score_gpt":0.34896618936410717,"score_spread":0.2677355967980509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091822809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7365862,0.000115505616,0.2624851,0.00006236576,0.000066702625,0.0006057457,0.000003641932,0.000036311936,0.000038410188],"genre_scores_gemma":[0.7209284,9.032013e-7,0.27856168,0.00001121986,0.000047323807,0.000393564,0.000007991718,0.0000067065916,0.000042247964],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986204,0.00010188146,0.00044481395,0.0002939184,0.00029956896,0.0002394546],"domain_scores_gemma":[0.9991939,0.00019724235,0.00013179403,0.00026732753,0.00018409394,0.000025647525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031234478,0.00012358153,0.0001282873,0.0000956096,0.0003546644,0.000036266385,0.00045334303,0.0000384176,0.000010442692],"category_scores_gemma":[0.00004957527,0.000092572445,0.00006452382,0.00054773915,0.000038950795,0.00013150695,0.00011490939,0.000113641996,0.0000010580527],"study_design_candidate":"observational","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":[0.0000041355825,0.0016488974,0.69926476,0.000031505355,0.000009593547,0.000006484799,0.00040561488,0.11728639,0.13646257,0.01756923,0.000112764064,0.027198043],"study_design_scores_gemma":[0.00017403439,0.0000879873,0.41811407,0.000012742292,0.000020495687,0.000013859908,0.00008215121,0.58064926,0.00047995243,0.00026795935,0.000012042856,0.00008546439],"about_ca_topic_score_codex":0.0003129733,"about_ca_topic_score_gemma":0.00002183573,"teacher_disagreement_score":0.46336287,"about_ca_system_score_codex":0.000071157774,"about_ca_system_score_gemma":0.000079261954,"threshold_uncertainty_score":0.3774995},"labels":[],"label_agreement":null},{"id":"W2095333339","doi":"10.1109/ccece.2008.4564513","title":"An architecture exploration framework for DSP applications","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digital signal processing; Computer science; Design space exploration; Computer architecture; System on a chip; Speedup; Embedded system; Architecture; Frame (networking); Electronic system-level design and verification; Design methods; Computer engineering; Computer hardware; Parallel computing; Engineering","score_opus":0.0233007341320795,"score_gpt":0.22653351228494012,"score_spread":0.20323277815286062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095333339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026091025,0.000047466583,0.9937288,0.0025142522,0.000062561114,0.00054294075,0.000010570922,0.00024974305,0.0002345214],"genre_scores_gemma":[0.76827234,0.00006413531,0.23040713,0.00034782052,0.00027289946,0.00057963835,0.000015804222,0.000014828855,0.00002542699],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984537,0.000005897672,0.00021917911,0.0006333951,0.00017231333,0.0005155673],"domain_scores_gemma":[0.99871147,0.00007137891,0.00005996104,0.00023239818,0.00032228525,0.0006025321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000080444195,0.00024790538,0.00020735661,0.00029583185,0.00046461922,0.00030322405,0.0006664538,0.00014792503,0.0000060785624],"category_scores_gemma":[0.0000185814,0.00025439035,0.000045344637,0.0005535777,0.00005003107,0.00055049267,0.00003922119,0.00033807012,0.000008923974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023998746,0.000036977122,0.000072118986,0.000013428388,0.000008334939,0.0000014573523,0.00040719734,0.00042500507,0.00019320984,0.94680804,0.00013484743,0.051897004],"study_design_scores_gemma":[0.0001450094,0.00028395333,0.0013059219,0.00003215704,0.000004992685,0.000049782742,0.000012530876,0.9269089,0.00013831489,0.06472973,0.006037491,0.0003512361],"about_ca_topic_score_codex":0.00021317437,"about_ca_topic_score_gemma":0.00007849614,"teacher_disagreement_score":0.92648387,"about_ca_system_score_codex":0.00008483177,"about_ca_system_score_gemma":0.0003557871,"threshold_uncertainty_score":0.9999908},"labels":[],"label_agreement":null},{"id":"W2095377095","doi":"10.1007/s10710-005-7617-y","title":"A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise","year":2005,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Differential evolution; Noise (video); Constant (computer programming); Outcome (game theory); Quadratic equation; Fitness function; Range (aeronautics); Mathematical optimization; Function (biology); Quadratic function; Applied mathematics; Mathematics; Algorithm; Artificial intelligence; Machine learning; Genetic algorithm; Mathematical economics","score_opus":0.02830246072266629,"score_gpt":0.2700299575110928,"score_spread":0.24172749678842653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095377095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003263474,0.0012358394,0.99406534,0.00072767644,0.000038211158,0.00044557388,0.0000036267395,0.00007628919,0.00014397295],"genre_scores_gemma":[0.27918065,0.0000127761705,0.72015214,0.000021508153,0.00014173375,0.00009361751,0.000009968859,0.000006204248,0.00038141542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907297,0.00002539507,0.00026987484,0.0002784247,0.00014049123,0.00021281924],"domain_scores_gemma":[0.9994098,0.00005852851,0.000117201875,0.0002687856,0.00008303788,0.00006262761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002458772,0.00011369003,0.000116603675,0.00004907705,0.00031475784,0.00010043766,0.0003054832,0.00004693326,0.0000041109033],"category_scores_gemma":[0.000017668912,0.00008400207,0.00004972942,0.00026076485,0.0000463364,0.00016899772,0.000084647494,0.000069418966,9.4393744e-7],"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":[0.0000047186986,0.000066758395,0.004434359,0.00003189354,0.000013373808,3.6952745e-8,0.00012807305,0.7587156,0.000041530424,0.00832789,0.0001497522,0.22808596],"study_design_scores_gemma":[0.00030175323,0.00006241395,0.0073987083,0.0000064623987,0.000022269025,0.000015050508,0.000063672844,0.98931414,0.000013461175,0.0021531086,0.0005542297,0.00009475505],"about_ca_topic_score_codex":0.00020190173,"about_ca_topic_score_gemma":0.000009600127,"teacher_disagreement_score":0.27591717,"about_ca_system_score_codex":0.000023971945,"about_ca_system_score_gemma":0.000066881556,"threshold_uncertainty_score":0.3425505},"labels":[],"label_agreement":null},{"id":"W2096347398","doi":"10.1145/1276958.1277275","title":"Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic programming; Subroutine; Computer science; Genetic algorithm; Mathematical optimization; Ant colony optimization algorithms; Ant colony; Artificial intelligence; Mathematics; Machine learning; Programming language","score_opus":0.04826897940238829,"score_gpt":0.31094135366282327,"score_spread":0.262672374260435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096347398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06833414,0.00007495608,0.86375076,0.049041115,0.00016992312,0.0008144764,0.0000046433543,0.00011847905,0.017691523],"genre_scores_gemma":[0.97538173,0.0000075250114,0.022862934,0.00083009526,0.00015614905,0.000114194685,0.0000033128765,0.000005805578,0.00063822715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989126,0.00006376137,0.00024510158,0.00023862638,0.00029628736,0.00024363965],"domain_scores_gemma":[0.99886686,0.00051311095,0.00005203005,0.0004710466,0.00006280612,0.000034144887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015200947,0.00008438344,0.00006283568,0.00005530805,0.0005212322,0.00010310532,0.00070756464,0.00003212681,0.00006200411],"category_scores_gemma":[0.00003325237,0.000045424516,0.000039951818,0.0007164673,0.00006764741,0.00016609539,0.00009700829,0.00016709321,0.000087385946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002278549,0.00018430187,0.00014217015,0.0000014430094,0.000004936377,0.0000015665047,0.0010935824,0.0015017666,0.0023270494,0.9418343,0.0040620835,0.04884454],"study_design_scores_gemma":[0.00023891687,0.00007918218,0.05601439,0.000035176326,0.000008569933,0.000019025681,0.0010981774,0.7733827,0.0051407553,0.15245725,0.011228693,0.000297161],"about_ca_topic_score_codex":0.00007213817,"about_ca_topic_score_gemma":0.0004866416,"teacher_disagreement_score":0.9070476,"about_ca_system_score_codex":0.00004873729,"about_ca_system_score_gemma":0.00009237635,"threshold_uncertainty_score":0.4008949},"labels":[],"label_agreement":null},{"id":"W2097055892","doi":"10.1145/2576768.2598211","title":"Passive solar building design using genetic programming","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"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":"Solar gain; Computer science; Passive solar building design; Window (computing); Energy consumption; Architectural engineering; Building design; Roof; Genetic algorithm; Cooling load; Ranging; Simulation; Solar energy; Mechanical engineering; Engineering; Civil engineering; Operating system; Electrical engineering; Telecommunications","score_opus":0.026683607272708583,"score_gpt":0.2615564645842622,"score_spread":0.2348728573115536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097055892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022887206,0.000030990297,0.9967893,0.00037466708,0.000059433234,0.0001464723,9.7101754e-8,0.00015400168,0.00015635985],"genre_scores_gemma":[0.18772332,0.0000015067301,0.81203693,0.00009154028,0.0000740727,0.000020437425,1.899406e-7,0.000004379711,0.000047616388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992779,0.000043088283,0.000117297044,0.000242185,0.00011872613,0.00020078752],"domain_scores_gemma":[0.999485,0.00006680233,0.00004366047,0.00028903576,0.000050895917,0.00006458148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013507271,0.00007216571,0.00006409418,0.000042008687,0.00024733762,0.00011233957,0.00038185157,0.000027850052,0.0000061759324],"category_scores_gemma":[0.000014828226,0.000065246524,0.000029250352,0.00024555356,0.000022795044,0.00020777746,0.00011643734,0.00005238243,0.000021712794],"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":[5.113829e-7,0.000073958625,0.00016938416,0.0000060122516,0.000011832938,0.0000027000742,0.00012874267,0.025672756,0.006674422,0.27260283,0.0002735505,0.69438326],"study_design_scores_gemma":[0.00006683074,0.000026526204,0.00078416965,0.000005570785,0.0000032645644,0.000019806806,0.0000057654697,0.9788542,0.002125501,0.013190582,0.004810942,0.00010685983],"about_ca_topic_score_codex":0.00002201148,"about_ca_topic_score_gemma":3.9495004e-7,"teacher_disagreement_score":0.95318145,"about_ca_system_score_codex":0.000025712681,"about_ca_system_score_gemma":0.00003421406,"threshold_uncertainty_score":0.2660676},"labels":[],"label_agreement":null},{"id":"W2097695714","doi":"10.1109/isscs.2007.4292680","title":"Multiplierless Evolutionary Filter Design","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Infinite impulse response; Adder; Computer science; Evolvable hardware; 2D Filters; Network topology; Evolutionary algorithm; Digital filter; Filter (signal processing); Genetic algorithm; Field-programmable gate array; Finite impulse response; Algorithm; Computer hardware; Artificial intelligence","score_opus":0.027229640049426258,"score_gpt":0.25887742703841227,"score_spread":0.231647786988986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097695714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045676195,0.00007909935,0.9888321,0.0008948233,0.00011054206,0.00013126039,8.207135e-7,0.00024007108,0.009254513],"genre_scores_gemma":[0.20972069,0.0000039109445,0.78756934,0.00033846012,0.00008203681,0.00001658031,0.0000021717096,0.000003865402,0.0022629544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992039,0.00001569027,0.00014930034,0.00024194356,0.00016057408,0.00022855053],"domain_scores_gemma":[0.99929833,0.00014779126,0.00002788651,0.00037113877,0.000065421766,0.00008941909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029746958,0.00007458341,0.00005554287,0.00006541398,0.00017546887,0.00002967803,0.00048721908,0.000039748047,0.000063169595],"category_scores_gemma":[0.0000086025775,0.00006595017,0.000035493733,0.00031026278,0.000033332148,0.00031328533,0.00012300994,0.00006586659,0.00033297238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005947043,0.00027464802,0.0006980294,0.0000032142138,0.00001397819,0.000016737466,0.00025327748,0.00114073,0.0023574005,0.88315564,0.03727418,0.07480619],"study_design_scores_gemma":[0.00057703035,0.00007984434,0.1198294,0.000008866507,0.0000046892524,0.00011020471,0.000059763322,0.71151835,0.0051495163,0.0590482,0.10309687,0.0005172899],"about_ca_topic_score_codex":0.000011506096,"about_ca_topic_score_gemma":0.0000017147754,"teacher_disagreement_score":0.82410747,"about_ca_system_score_codex":0.00003842256,"about_ca_system_score_gemma":0.000037040863,"threshold_uncertainty_score":0.42797974},"labels":[],"label_agreement":null},{"id":"W2098316496","doi":"10.1109/csac.2005.23","title":"Evolving Successful Stack Overflow Attacks for Vulnerability Testing","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Exploit; Computer science; Vulnerability (computing); Focus (optics); Stack (abstract data type); Buffer overflow; Process (computing); Call stack; Variety (cybernetics); Distributed computing; Computer security; Operating system; Artificial intelligence","score_opus":0.025714270869447026,"score_gpt":0.27970787398639063,"score_spread":0.25399360311694363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098316496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019203635,0.00005624992,0.9667746,0.0010270699,0.000089849986,0.00026767605,0.000008656348,0.00028387073,0.0122884],"genre_scores_gemma":[0.50811446,3.0770988e-7,0.4906515,0.000075779935,0.00015145748,0.0000728941,0.000006103342,0.0000046767295,0.0009228201],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898976,0.00001778148,0.00021806645,0.0003750143,0.00014673042,0.00025263464],"domain_scores_gemma":[0.9987897,0.00042336006,0.000058086913,0.00045199625,0.00022931087,0.00004749919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026179512,0.000096662814,0.00009463105,0.000032994452,0.0003534204,0.00014516528,0.00046028217,0.00003693135,0.000030718802],"category_scores_gemma":[0.00008811478,0.00008836569,0.000051522115,0.00036317558,0.000032249274,0.00054904097,0.00012289277,0.000067220644,0.000021205073],"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":[0.0000034861885,0.00061856146,0.10900145,0.00006927362,0.00001507219,0.0000022209406,0.000070566675,0.010812661,0.0038758218,0.7778431,0.055019923,0.04266787],"study_design_scores_gemma":[0.00020546037,0.00003531682,0.07564157,0.0000060249167,0.000002782775,0.000004633332,0.000007913077,0.86298805,0.0007079262,0.054020934,0.0062096524,0.00016972994],"about_ca_topic_score_codex":0.00031769375,"about_ca_topic_score_gemma":0.000042133852,"teacher_disagreement_score":0.8521754,"about_ca_system_score_codex":0.00005545951,"about_ca_system_score_gemma":0.00006372298,"threshold_uncertainty_score":0.36034483},"labels":[],"label_agreement":null},{"id":"W2098879935","doi":"10.1007/978-1-4419-7747-2_3","title":"The Rubik Cube and GP Temporal Sequence Learning: An Initial Study","year":2010,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Cube (algebra); Sequence (biology); Computer science; Artificial intelligence; Mathematics; Combinatorics; Chemistry","score_opus":0.026318604988066065,"score_gpt":0.2756210176767801,"score_spread":0.24930241268871403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098879935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09185114,0.009472063,0.8597739,0.0035382982,0.0018757557,0.0038410034,0.000081140744,0.00093201344,0.028634692],"genre_scores_gemma":[0.87750494,0.0007517892,0.09726296,0.00013086306,0.0008619157,0.0001171276,0.00023707312,0.000059438436,0.023073876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982737,0.00008327912,0.0003725665,0.00067232945,0.00037099485,0.0002271434],"domain_scores_gemma":[0.99890137,0.00016081125,0.0002226629,0.00035798314,0.00019974081,0.00015744515],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00020859226,0.00028363048,0.00019593442,0.000100622965,0.0014980604,0.00022023109,0.000389417,0.00019459845,0.000009084428],"category_scores_gemma":[0.000011452177,0.00024735616,0.000042721393,0.00007196554,0.00035255734,0.00028335175,0.0003166707,0.00053796783,0.000024936111],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005967308,0.00050728506,0.004362176,0.000056826684,0.00026388702,0.00009228637,0.0035964947,0.010756901,0.00018484758,0.33073616,0.001780221,0.6476032],"study_design_scores_gemma":[0.0007015617,0.0011530201,0.10114684,0.00003257446,0.0000786246,0.00045851554,0.00019051942,0.48259005,0.000001605271,0.35196507,0.06087181,0.00080980244],"about_ca_topic_score_codex":0.000049041522,"about_ca_topic_score_gemma":0.00003331355,"teacher_disagreement_score":0.7856538,"about_ca_system_score_codex":0.000042550026,"about_ca_system_score_gemma":0.00018784308,"threshold_uncertainty_score":0.99999785},"labels":[],"label_agreement":null},{"id":"W2100208842","doi":"10.1504/ijhpsa.2008.024207","title":"Genetic programming on GPUs for image processing","year":2008,"lang":"en","type":"article","venue":"International Journal of High Performance Systems Architecture","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Graphics; Genetic programming; Task (project management); Image processing; General-purpose computing on graphics processing units; Computer graphics; CUDA; Enhanced Data Rates for GSM Evolution; Noise (video); Image (mathematics); Artificial intelligence; Parallel computing; Computer graphics (images)","score_opus":0.010072496811260363,"score_gpt":0.24811495840420747,"score_spread":0.23804246159294712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100208842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32527393,0.00026248078,0.6722582,0.0010391211,0.0008392455,0.00020512797,0.000006209262,0.000031858945,0.000083852276],"genre_scores_gemma":[0.8229072,0.000040193678,0.17586012,0.00006641056,0.0009733233,0.00003947579,0.0000022118782,0.000009570774,0.00010147246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861884,0.000021577813,0.00043920882,0.00017477326,0.00056061166,0.0001849971],"domain_scores_gemma":[0.9986483,0.000055494438,0.0004004892,0.00016197127,0.00066141505,0.00007231901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017699838,0.00012567433,0.00016389605,0.00023168015,0.00022712864,0.00011923285,0.00096965727,0.000043115906,0.0000017470717],"category_scores_gemma":[0.000021648659,0.00009710914,0.00009036653,0.00015219636,0.00005319668,0.00038248312,0.00005700278,0.00020937849,0.0000065748663],"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":[0.0002299773,0.000668678,0.0024107555,0.0002436388,0.00028010222,0.00028987686,0.0029982103,0.19400226,0.0034069195,0.012409925,0.00309323,0.7799664],"study_design_scores_gemma":[0.0058349315,0.003039844,0.09655906,0.0017521253,0.000049427705,0.039640564,0.00016588105,0.62215537,0.005223137,0.0044252966,0.2198719,0.0012824715],"about_ca_topic_score_codex":0.000007989077,"about_ca_topic_score_gemma":4.683482e-7,"teacher_disagreement_score":0.77868396,"about_ca_system_score_codex":0.00008175001,"about_ca_system_score_gemma":0.00014929699,"threshold_uncertainty_score":0.39599958},"labels":[],"label_agreement":null},{"id":"W2101709153","doi":"10.1109/icsmc.1998.728123","title":"A new technique for reinforcement learning for control","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Ball (mathematics); Control theory (sociology); Swing; Piecewise; Task (project management); Inverted pendulum; Control (management); Artificial intelligence; Nonlinear system; Mathematics; Engineering","score_opus":0.01849185421854255,"score_gpt":0.24957618969214648,"score_spread":0.23108433547360394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101709153","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.4263714e-7,0.000034622444,0.9922621,0.0038516605,0.00002666169,0.0009425313,5.4824557e-7,0.00012567548,0.0027552515],"genre_scores_gemma":[0.087451115,0.0000051798233,0.88206875,0.0003785497,0.00009610074,0.0013470721,0.0000024390488,0.000004536838,0.02864623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955064,0.0000037606035,0.00010612477,0.0001472517,0.00005563246,0.00013658596],"domain_scores_gemma":[0.9996273,0.00008821012,0.000032759715,0.00015301912,0.000049280658,0.000049455055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008894269,0.000049830192,0.00005714964,0.000026731479,0.00015595544,0.0000332053,0.00022522974,0.000024418086,0.000054709697],"category_scores_gemma":[0.000015955284,0.0000443648,0.000051493684,0.000084191764,0.0000052330943,0.00013182843,0.000023407285,0.000031523658,0.000016383126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023991777,0.000025856833,0.000010261339,0.0000070368933,0.000010282032,8.744713e-8,0.000064997395,0.0064868527,0.0022972876,0.9036456,0.053190384,0.034258984],"study_design_scores_gemma":[0.0004216035,0.00010762011,0.000010604775,0.0000022976774,0.0000021908947,0.0000021043966,0.0000030210892,0.8090583,0.00072751055,0.00765749,0.18194689,0.00006037367],"about_ca_topic_score_codex":0.000008528585,"about_ca_topic_score_gemma":6.753859e-7,"teacher_disagreement_score":0.8959881,"about_ca_system_score_codex":0.000017646098,"about_ca_system_score_gemma":0.000014710974,"threshold_uncertainty_score":0.1809144},"labels":[],"label_agreement":null},{"id":"W2101804758","doi":"10.1145/1276958.1277058","title":"Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Genetic programming; Computer science; Population; Artificial intelligence; Classifier (UML); Pareto principle; Machine learning; Computation; Overhead (engineering); Pareto optimal; Bidding; Evolutionary computation; Class (philosophy); Mathematical optimization; Multi-objective optimization; Algorithm; Mathematics","score_opus":0.03475190511847994,"score_gpt":0.3155368064473615,"score_spread":0.28078490132888156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101804758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0088582365,0.000098166194,0.9879728,0.0012255344,0.000071475944,0.0009805551,0.0000028282113,0.0001970604,0.00059333583],"genre_scores_gemma":[0.39540315,0.00000600355,0.60402894,0.000061461484,0.000034022123,0.00032602216,0.000025727586,0.0000060117527,0.00010867748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986822,0.000021534135,0.0003764235,0.00041800205,0.00015798151,0.00034384395],"domain_scores_gemma":[0.99927723,0.000111172616,0.00009212525,0.00030183,0.00013480679,0.000082820814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041564635,0.000113982336,0.000095522126,0.00016214399,0.00020232529,0.000056132918,0.00037161884,0.00007929597,0.0000024637686],"category_scores_gemma":[0.000010490743,0.000115557545,0.000053829874,0.0005324984,0.00003844397,0.0003480544,0.000062051935,0.00008421784,0.000019599613],"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":[0.000022427059,0.0016380766,0.016897969,0.00006383896,0.000019087365,0.000006404912,0.0004798383,0.0021889615,0.010252687,0.5749457,0.001408958,0.39207605],"study_design_scores_gemma":[0.00060517964,0.00006889497,0.26868343,0.000016872278,0.0000036972501,0.000020129199,0.00008822901,0.7118645,0.00032357735,0.010573724,0.0075448444,0.00020697374],"about_ca_topic_score_codex":0.00003675994,"about_ca_topic_score_gemma":0.00014977467,"teacher_disagreement_score":0.7096755,"about_ca_system_score_codex":0.00017018669,"about_ca_system_score_gemma":0.00006569063,"threshold_uncertainty_score":0.47122997},"labels":[],"label_agreement":null},{"id":"W2102391083","doi":"10.1109/tsmcb.2005.856724","title":"Analysis of a master-slave architecture for distributed evolutionary computations","year":2006,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Workstation; Computation; Architecture; Computer science; Mainstream; Distributed computing; Beagle; Master/slave; Parallel computing; Programming language; Operating system; Geography; Archaeology; Ecology; Biology","score_opus":0.018192874081714584,"score_gpt":0.23260940997893226,"score_spread":0.21441653589721768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102391083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002439609,0.0005079609,0.965954,0.024774887,0.00092066196,0.0013731184,0.005733103,0.00018728896,0.0003049988],"genre_scores_gemma":[0.73464257,0.00096229935,0.121227935,0.043310598,0.005707338,0.0072826655,0.018391768,0.00074602145,0.067728795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99592036,0.00022093786,0.0012356082,0.0012062887,0.0007648551,0.000651929],"domain_scores_gemma":[0.99671,0.0007185796,0.000651811,0.0012588518,0.00048469516,0.00017604386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020410938,0.00067812146,0.001075842,0.0009690161,0.00043964427,0.0002396893,0.0008773222,0.0007552042,0.000020283504],"category_scores_gemma":[0.0000041922185,0.0006387115,0.00067056,0.0015012817,0.00032487352,0.000114315815,0.000019403768,0.00095876143,0.000021367252],"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":[0.000020297915,0.00069304905,0.00006308008,0.00056918856,0.002969286,0.00002967969,0.00044152007,0.5363961,0.00003197727,0.004339492,0.45038527,0.0040610293],"study_design_scores_gemma":[0.0008302557,0.00034671446,0.00084139215,0.00025160075,0.00233,0.000041823772,0.000043771513,0.52262324,0.000053963264,0.0018054295,0.46985286,0.0009789646],"about_ca_topic_score_codex":0.0002835331,"about_ca_topic_score_gemma":0.00009725126,"teacher_disagreement_score":0.8447261,"about_ca_system_score_codex":0.00015586686,"about_ca_system_score_gemma":0.00014758781,"threshold_uncertainty_score":0.99960643},"labels":[],"label_agreement":null},{"id":"W2105746865","doi":"10.1145/1570256.1570409","title":"Statistical analysis for evolutionary computation","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","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":"Government of Canada; University of Guelph","funders":"","keywords":"Computer science; Evolutionary computation; Human-based evolutionary computation; Computation; Interactive evolutionary computation; Evolutionary programming; Theoretical computer science; Artificial intelligence; Algorithm","score_opus":0.017024873417838354,"score_gpt":0.2923516767246442,"score_spread":0.2753268033068058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105746865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032817887,0.000027098602,0.99396163,0.0036160913,0.000027058904,0.00013286277,0.000014699695,0.0001375035,0.0017548815],"genre_scores_gemma":[0.4385026,0.0000011305431,0.5609757,0.00025389213,0.000024226765,0.000014467059,0.000053798856,8.176165e-7,0.00017335638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939835,0.000012033813,0.00013364872,0.00021747184,0.00011533513,0.00012317997],"domain_scores_gemma":[0.9995475,0.00011568533,0.000028293565,0.00015970408,0.0000936505,0.00005520155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076882956,0.000053410986,0.00008160425,0.000102160484,0.00014976037,0.00003830405,0.0001932846,0.000022293094,0.000018073553],"category_scores_gemma":[0.0000109051325,0.000049693797,0.000063012136,0.0006324947,0.000015020791,0.0001913551,0.000018109156,0.00002662903,0.000022454939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001144442,0.00007980974,0.00030261575,6.4425404e-7,0.000026432637,2.9896802e-7,0.000015206419,0.005566422,0.000025698111,0.96725464,0.006973741,0.019753365],"study_design_scores_gemma":[0.00008387451,0.000044752353,0.18479744,3.1244235e-7,0.00002039855,0.0000015123061,0.000003282176,0.68786323,0.0000073360475,0.1255492,0.001572122,0.000056563655],"about_ca_topic_score_codex":0.000005873504,"about_ca_topic_score_gemma":0.000001259777,"teacher_disagreement_score":0.84170544,"about_ca_system_score_codex":0.000026394839,"about_ca_system_score_gemma":0.000029934215,"threshold_uncertainty_score":0.20264542},"labels":[],"label_agreement":null},{"id":"W2105802878","doi":"10.1145/1389095.1389162","title":"Managing team-based problem solving with symbiotic bid-based genetic programming","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Artificial intelligence; Machine learning; Computer science; Genetic programming; Population; Classifier (UML); Support vector machine","score_opus":0.011802766465999207,"score_gpt":0.21245482052258552,"score_spread":0.20065205405658632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105802878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009801222,0.00007422029,0.9849938,0.001818461,0.000024245028,0.0004155251,4.6519725e-7,0.00049628766,0.0023757787],"genre_scores_gemma":[0.4327508,0.0000018742871,0.5667368,0.00022469329,0.000021721175,0.000056488047,0.000002422654,0.000009048284,0.00019618575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987121,0.000024047633,0.0001884615,0.00043970358,0.0002757696,0.00035992256],"domain_scores_gemma":[0.9991764,0.00005468756,0.00006933014,0.00050443516,0.00008438613,0.00011079775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009457637,0.00015095298,0.00011430102,0.00012195646,0.00047628913,0.000092944385,0.0005171194,0.00003385059,0.000014128222],"category_scores_gemma":[0.0000033524695,0.00012329785,0.00004602191,0.0006756904,0.00009337677,0.00020351612,0.000062503546,0.00010410738,0.000039645005],"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":[0.00003912857,0.003406917,0.08404761,0.000577054,0.00017570645,0.00051422743,0.001799563,0.42824787,0.0044966866,0.09841405,0.0061901645,0.37209103],"study_design_scores_gemma":[0.0005022998,0.00015511671,0.007991885,0.000056112774,0.000008804385,0.000064020074,0.00002473697,0.9869877,0.0013035925,0.0007016913,0.0018951783,0.000308856],"about_ca_topic_score_codex":0.000065083535,"about_ca_topic_score_gemma":0.000013877388,"teacher_disagreement_score":0.55873984,"about_ca_system_score_codex":0.000051733354,"about_ca_system_score_gemma":0.0002237325,"threshold_uncertainty_score":0.502794},"labels":[],"label_agreement":null},{"id":"W2106804910","doi":"10.1109/cicybs.2011.5949387","title":"Exploring the state space of an application protocol: A case study of SMTP","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Protocol (science); Computer science; Payload (computing); Neighbor Discovery Protocol; Internet protocol suite; Reverse Address Resolution Protocol; Process (computing); Simple (philosophy); Resource Reservation Protocol; The Internet; State (computer science); Computer network; Server; Internet Protocol; Distributed computing; Network packet; World Wide Web; Operating system","score_opus":0.1279647109716423,"score_gpt":0.3103044317031506,"score_spread":0.18233972073150828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106804910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61921054,5.783509e-7,0.36164254,0.000035220353,0.000009049498,0.018544892,8.165938e-7,0.000044484867,0.0005119097],"genre_scores_gemma":[0.8959379,2.5749438e-7,0.042144943,0.000004882558,0.000008211172,0.06185792,1.6047265e-7,0.000003213908,0.00004247805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939233,0.000040201,0.00019077845,0.00017327769,0.0001256016,0.000077827215],"domain_scores_gemma":[0.9990684,0.000024286304,0.00010933289,0.00066784513,0.00009791128,0.000032255804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020858982,0.000053768894,0.00006749893,0.00003556128,0.00008450588,0.000008483145,0.00041879565,0.000006718516,0.0000039756374],"category_scores_gemma":[0.0000024387475,0.000035765814,0.000016535445,0.0003317188,0.000031283773,0.00041544912,0.00011967825,0.00004040353,0.0000028875822],"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":[0.00005729533,0.015268162,0.005264526,0.00011435809,0.000109077264,0.00010552816,0.16503431,0.0019036594,0.0057702963,0.36906007,0.00010820723,0.4372045],"study_design_scores_gemma":[0.0035665832,0.005055862,0.11525349,0.000033552256,0.000048701593,0.0008787633,0.053732578,0.70175797,0.07786264,0.03849255,0.0024607845,0.0008565171],"about_ca_topic_score_codex":0.0030085205,"about_ca_topic_score_gemma":0.00023457817,"teacher_disagreement_score":0.6998543,"about_ca_system_score_codex":0.0000064143715,"about_ca_system_score_gemma":0.000022312835,"threshold_uncertainty_score":0.4548003},"labels":[],"label_agreement":null},{"id":"W2107854615","doi":"10.1109/wescan.1997.627163","title":"Design of a parallel genetic algorithm for the Internet","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Unix; Genetic algorithm; Asynchronous communication; Parallel computing; Parallel algorithm; Ideal (ethics); Population; The Internet; Fault tolerance; Distributed computing; Algorithm; Computer network; Operating system","score_opus":0.03952380449017121,"score_gpt":0.2441717524855651,"score_spread":0.2046479479953939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107854615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008073789,0.00044316048,0.99760187,0.0012884735,0.00004189343,0.00027787537,0.000001289651,0.000034025325,0.00030335726],"genre_scores_gemma":[0.008795752,0.000055027078,0.9878635,0.00015132615,0.00003154215,0.00014888668,2.9460512e-7,0.0000027006024,0.0029509973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957764,0.000012524036,0.00011357752,0.00012569102,0.000074850774,0.00009568852],"domain_scores_gemma":[0.9994373,0.00018879025,0.00003415873,0.00027441108,0.000044351375,0.000021043974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074214215,0.000045647535,0.000049910126,0.000016736709,0.00005346828,0.000020222631,0.00053717353,0.00001615351,0.00006189509],"category_scores_gemma":[0.0000037636455,0.000029089606,0.000033737782,0.0001190115,0.000031996857,0.00006350889,0.00006139339,0.00002444822,0.000029032719],"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":[6.7524195e-7,0.00014032597,0.00001060562,0.0000030038018,0.00002797272,4.6458612e-7,0.0003450112,0.006883501,0.000067421905,0.095581524,0.04026207,0.8566774],"study_design_scores_gemma":[0.000115205046,0.00004752724,0.00029381062,0.0000013124313,0.0000031461561,0.000006338437,0.000006836961,0.98853254,0.00011009831,0.0046635503,0.0061784512,0.000041176565],"about_ca_topic_score_codex":0.0000148440595,"about_ca_topic_score_gemma":3.7054514e-7,"teacher_disagreement_score":0.98164904,"about_ca_system_score_codex":0.00000581352,"about_ca_system_score_gemma":0.00000691872,"threshold_uncertainty_score":0.11862397},"labels":[],"label_agreement":null},{"id":"W2108346947","doi":"10.1109/tsmcb.2002.999814","title":"Dynamic page based crossover in linear genetic programming","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Crossover; Genetic programming; Pairwise comparison; Generalization; Computer science; Population; Linear programming; Tree (set theory); Block (permutation group theory); Constant (computer programming); Algorithm; Combinatorics; Mathematics; Artificial intelligence; Programming language; Medicine","score_opus":0.017164744051282526,"score_gpt":0.2353421798431268,"score_spread":0.2181774357918443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108346947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040758286,0.0016796392,0.9533853,0.00062490435,0.0009316855,0.0010508391,0.000045613902,0.00030298505,0.0012207489],"genre_scores_gemma":[0.9745068,0.00037614658,0.01930517,0.00015067944,0.000064291606,0.00031376095,0.0000044063054,0.000043883803,0.0052348766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731255,0.00013066625,0.0006708766,0.00081218604,0.0004725257,0.0006012054],"domain_scores_gemma":[0.998485,0.00014252798,0.0001537973,0.00085473375,0.0000978143,0.00026609143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002076852,0.00037397756,0.00034683626,0.00027199328,0.000307906,0.00030812042,0.0005513288,0.00020622078,0.000061976156],"category_scores_gemma":[0.000003896349,0.00039081008,0.00012276883,0.000655306,0.00018000876,0.00017897498,0.000011138811,0.00040920085,0.00022523673],"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":[0.000101347156,0.009402896,0.002207347,0.0009174444,0.0004068015,0.0004541274,0.0066398545,0.52547216,0.002272969,0.024483785,0.004949085,0.4226922],"study_design_scores_gemma":[0.0010655478,0.00021489729,0.0018673181,0.00013575586,0.000028696277,0.000058651032,0.00005434502,0.9721097,0.00017978066,0.0001725705,0.023609012,0.00050374557],"about_ca_topic_score_codex":0.00018767962,"about_ca_topic_score_gemma":0.0001929564,"teacher_disagreement_score":0.9340801,"about_ca_system_score_codex":0.00011818578,"about_ca_system_score_gemma":0.00003740088,"threshold_uncertainty_score":0.9998544},"labels":[],"label_agreement":null},{"id":"W2108587684","doi":"10.1109/icsmc.2007.4414009","title":"Multi-objective competitive coevolution for efficient GP classifier problem decomposition","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Scalability; Genetic programming; Classifier (UML); Artificial intelligence; Pareto principle; Machine learning; Binary classification; Theoretical computer science; Mathematical optimization; Mathematics; Support vector machine","score_opus":0.016706957161604398,"score_gpt":0.291026138436937,"score_spread":0.27431918127533256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108587684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002522569,0.00003506414,0.9886521,0.0006931051,0.00015649933,0.0008376497,0.00001330947,0.00022542641,0.006864271],"genre_scores_gemma":[0.3769623,0.0000017931222,0.62230605,0.00012699928,0.00006395449,0.00011351442,0.000021595375,0.0000059317445,0.00039787867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988339,0.000023182734,0.00024620022,0.00040813847,0.00017420106,0.0003143584],"domain_scores_gemma":[0.9990686,0.00018923696,0.00008950885,0.0002465252,0.00030830043,0.000097831035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040959858,0.00012291342,0.00010808745,0.00010815066,0.00037745907,0.00005149938,0.00028888622,0.00006738211,0.000009277255],"category_scores_gemma":[0.000010511254,0.00011432077,0.00008146061,0.00036005036,0.00006292685,0.00020257973,0.00008703976,0.00008735648,0.000057107452],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001704303,0.0006100182,0.00015208097,0.000009696536,0.000019935564,0.0000011942788,0.00042597987,0.002159855,0.007617575,0.98087436,0.0008917392,0.007220504],"study_design_scores_gemma":[0.0010343251,0.00016756981,0.03590276,0.000020731479,0.000008168144,0.000020725041,0.00030587797,0.9445187,0.0055936165,0.005110169,0.0070320982,0.0002852905],"about_ca_topic_score_codex":0.000029073302,"about_ca_topic_score_gemma":0.000053477415,"teacher_disagreement_score":0.9757642,"about_ca_system_score_codex":0.00019518576,"about_ca_system_score_gemma":0.000056863224,"threshold_uncertainty_score":0.46618658},"labels":[],"label_agreement":null},{"id":"W2108679901","doi":"10.1109/foci.2007.372151","title":"Opposition-Based Differential Evolution (ODE) with Variable Jumping Rate","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Jumping; Ode; Benchmark (surveying); Differential evolution; Suite; Opposition (politics); Computer science; Test suite; Mathematics; Mathematical optimization; Applied mathematics; Statistics; Test case; Geology; Geodesy; Law","score_opus":0.008114385425783906,"score_gpt":0.22177405956695617,"score_spread":0.21365967414117226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108679901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053508976,0.000009268123,0.98768336,0.0007040741,0.000096498836,0.0001588928,0.0000020764178,0.0002455899,0.005749354],"genre_scores_gemma":[0.7518529,3.5872367e-7,0.24746497,0.00020888528,0.00007759422,0.000019873854,0.000012551933,0.000005156982,0.00035766233],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990822,0.00002349257,0.00015796008,0.0002952461,0.00017950952,0.00026160717],"domain_scores_gemma":[0.99932945,0.00009462765,0.000053367054,0.00033539915,0.000091408874,0.000095763695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025425904,0.000100599485,0.00007703006,0.00009094026,0.00031066922,0.00008620905,0.00030527002,0.000042187217,0.00006699195],"category_scores_gemma":[0.000004033109,0.00008243269,0.000027891807,0.0005024398,0.0000365479,0.0003407854,0.000048181788,0.000083198625,0.000045542416],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008637665,0.0001421551,0.00041776273,0.0000055051883,0.000010046879,0.0000029877372,0.000018987892,0.0023109813,0.008818819,0.98720795,0.00045978985,0.0005963644],"study_design_scores_gemma":[0.0007120192,0.000112120586,0.023953127,0.00002754711,0.000012825763,0.000015132144,0.000023647348,0.94703186,0.0047917482,0.021739502,0.0012811339,0.00029932754],"about_ca_topic_score_codex":0.000093615294,"about_ca_topic_score_gemma":0.000019245083,"teacher_disagreement_score":0.96546847,"about_ca_system_score_codex":0.00012745487,"about_ca_system_score_gemma":0.000115359224,"threshold_uncertainty_score":0.33615077},"labels":[],"label_agreement":null},{"id":"W2109042184","doi":"","title":"DEAP: evolutionary algorithms made easy","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1670,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Documentation; License; Computation; Evolutionary algorithm; Theoretical computer science; Algorithm; Artificial intelligence; Programming language; Operating system","score_opus":0.018625969444637635,"score_gpt":0.2539693013165884,"score_spread":0.23534333187195078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109042184","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008727949,0.00081048475,0.96380603,0.002053872,0.0003452997,0.000119413024,0.000003714999,0.0003677631,0.03162063],"genre_scores_gemma":[0.2651085,0.000027401456,0.7244648,0.00063378306,0.00051940075,0.00007809292,0.000010614796,0.000009068393,0.009148351],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897647,0.000026497293,0.00015839656,0.0002251113,0.00022860206,0.00038492845],"domain_scores_gemma":[0.9991814,0.000051276293,0.000036947036,0.0004742557,0.00005637341,0.00019977536],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015608712,0.00010545856,0.00008033891,0.000062391286,0.00022460546,0.0000362446,0.00055999914,0.00005163909,0.00016176271],"category_scores_gemma":[0.000008943111,0.00009412295,0.00005907406,0.00038126015,0.000041147596,0.0009793044,0.00022725842,0.00009365972,0.0011124411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.7357238e-7,0.00023131375,0.0029721875,0.0000023612708,0.000010322412,9.199855e-7,0.00008612404,0.00003168384,0.00031564766,0.9531039,0.020580616,0.022664422],"study_design_scores_gemma":[0.00035322752,0.000045448905,0.27206773,0.000007613261,0.000009392808,0.00021430106,0.000055083725,0.19238837,0.0007701716,0.028250147,0.5052942,0.00054433284],"about_ca_topic_score_codex":0.000028869801,"about_ca_topic_score_gemma":4.4397325e-7,"teacher_disagreement_score":0.9248538,"about_ca_system_score_codex":0.000053887376,"about_ca_system_score_gemma":0.00004435279,"threshold_uncertainty_score":0.9996653},"labels":[],"label_agreement":null},{"id":"W2109589711","doi":"10.1145/2457450.2457453","title":"Identity verification based on handwritten signatures with haptic information using genetic programming","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Computer science; Naive Bayes classifier; Support vector machine; Artificial intelligence; Haptic technology; Identity (music); Pattern recognition (psychology); Signature (topology); Genetic programming; Random forest; Machine learning; Relevance (law); Data mining; Mathematics","score_opus":0.016167222480621513,"score_gpt":0.2584575447652899,"score_spread":0.2422903222846684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109589711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027427687,0.00009787962,0.9918343,0.003115076,0.000035920937,0.0016475533,0.00001764059,0.00036281484,0.00014604126],"genre_scores_gemma":[0.4784654,0.000046107674,0.5203616,0.0002675871,0.000023114519,0.00077028613,0.000046195757,0.000012284561,0.000007424669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830174,0.00010891275,0.00047525702,0.00044393787,0.0003565591,0.00031359473],"domain_scores_gemma":[0.9957123,0.0006028913,0.00025286898,0.0028694544,0.0003790497,0.00018345546],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00020000519,0.00026747835,0.00019174501,0.00035891644,0.0018050526,0.0005396242,0.0017848416,0.00011756843,0.0000143177895],"category_scores_gemma":[0.000022338343,0.00025462688,0.000072159615,0.0011108696,0.00026661763,0.0012716621,0.00008756753,0.00044340864,0.00010389435],"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":[0.0000070872047,0.0008786711,0.00020967868,0.00003715294,0.00005364113,2.4044687e-7,0.0005483052,0.06438303,0.0007019531,0.010984568,0.00003611112,0.92215955],"study_design_scores_gemma":[0.00049535965,0.00010992544,0.0062355986,0.000061020317,0.00003778954,0.0000099575445,0.00015242115,0.9871209,0.00008612655,0.0013792849,0.0040044533,0.00030711087],"about_ca_topic_score_codex":0.00021320749,"about_ca_topic_score_gemma":0.000015138075,"teacher_disagreement_score":0.9227379,"about_ca_system_score_codex":0.00010326752,"about_ca_system_score_gemma":0.000115185874,"threshold_uncertainty_score":0.9999906},"labels":[],"label_agreement":null},{"id":"W2109892450","doi":"10.1007/s11047-007-9038-8","title":"Repeated patterns in genetic programming","year":2007,"lang":"en","type":"article","venue":"Natural Computing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; University of Essex","keywords":"Genetic programming; Computer science; Crossover; Shuffling; Tree (set theory); Binary tree; Theoretical computer science; Artificial intelligence; Mathematics; Algorithm; Programming language; Combinatorics","score_opus":0.00966252290649453,"score_gpt":0.264951589042624,"score_spread":0.25528906613612945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109892450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47474566,0.00021390387,0.5243247,0.00017461191,0.00015395881,0.00010705894,1.470021e-7,0.00014312948,0.000136846],"genre_scores_gemma":[0.784244,0.0000012548437,0.21554388,0.000088256784,0.0000889251,0.0000015192229,0.0000024337019,0.000003795452,0.000025938341],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989604,0.00002000133,0.00024735395,0.0002884208,0.00015900859,0.00032485562],"domain_scores_gemma":[0.999518,0.00008779925,0.00006445902,0.00022868298,0.000052716256,0.000048336995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003385962,0.00008459874,0.00008100033,0.00010575795,0.00012682081,0.0000614724,0.00041567,0.000040448387,0.0000013216595],"category_scores_gemma":[0.00001925286,0.00008151382,0.000033071057,0.000602893,0.000013387226,0.00012657588,0.00018432322,0.00023691777,0.000010078885],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014649275,0.000057360343,0.05161092,0.000009021281,0.0000042607044,0.00005810968,0.000413021,0.00056879566,0.0005514001,0.0060595204,0.00003226847,0.94063383],"study_design_scores_gemma":[0.0001883694,0.000017244342,0.63301,0.000032142652,9.4646725e-7,0.00005052044,0.000035983412,0.36496478,0.00028280832,0.00038862857,0.00088211184,0.00014644826],"about_ca_topic_score_codex":0.00007503098,"about_ca_topic_score_gemma":0.000038521637,"teacher_disagreement_score":0.9404874,"about_ca_system_score_codex":0.000060934675,"about_ca_system_score_gemma":0.000016969498,"threshold_uncertainty_score":0.3324037},"labels":[],"label_agreement":null},{"id":"W2111445724","doi":"10.1145/1569901.1569998","title":"Evolution, development and learning using self-modifying cartesian genetic programming","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Computer science; Genetic representation; Cartesian coordinate system; Genetic algorithm; Artificial intelligence; Grammatical evolution; Theoretical computer science; Machine learning; Mathematics","score_opus":0.01476979695304872,"score_gpt":0.24390354257004793,"score_spread":0.2291337456169992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111445724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13854097,0.00029523758,0.8600881,0.00023969961,0.000020322937,0.000119029995,3.260516e-8,0.00028875552,0.0004078297],"genre_scores_gemma":[0.45801547,0.000002319878,0.5418636,0.00003393165,0.000022979453,0.0000055142964,4.0341308e-7,0.0000020765815,0.00005367065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916446,0.000026179117,0.00016264973,0.0002811073,0.00013705686,0.00022852799],"domain_scores_gemma":[0.9996514,0.000017131966,0.000044377444,0.00014730122,0.000054335265,0.00008545916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013961903,0.00009159387,0.00007274528,0.000067583336,0.0006524665,0.00012038446,0.00017781406,0.000032086078,0.0000016674892],"category_scores_gemma":[0.000006383408,0.00009011513,0.000016239865,0.00029943645,0.000013910261,0.000246307,0.000066870896,0.00010290582,0.000007090381],"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":[9.578151e-7,0.0002171507,0.010997907,0.000020040574,0.000023216422,0.000011161649,0.0040883967,0.007028558,0.0018294227,0.14331943,0.00004401315,0.83241975],"study_design_scores_gemma":[0.0001893791,0.000049359598,0.10355732,0.000017719854,0.00000666679,0.00010155276,0.00016277489,0.88121,0.00021136414,0.0019636704,0.012260421,0.0002697596],"about_ca_topic_score_codex":0.000019190067,"about_ca_topic_score_gemma":0.000002326924,"teacher_disagreement_score":0.87418145,"about_ca_system_score_codex":0.00008774895,"about_ca_system_score_gemma":0.000097978074,"threshold_uncertainty_score":0.50183105},"labels":[],"label_agreement":null},{"id":"W2113586204","doi":"10.1109/cec.2003.1299773","title":"Trying to evolve sorting networks in echo","year":2004,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sorting; Computer science; Echo (communications protocol); Sorting network; Sorting algorithm; Theoretical computer science; Artificial intelligence; Imperfect; Algorithm; Computer network","score_opus":0.012929472259194972,"score_gpt":0.25186690850910604,"score_spread":0.23893743624991107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113586204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009278452,0.000032912543,0.9821152,0.0025207326,0.000055030498,0.000102783386,9.802636e-8,0.000104444945,0.0057903505],"genre_scores_gemma":[0.68100595,0.000002162479,0.31826463,0.00053429767,0.00004768131,0.000024297164,4.626336e-7,0.000002320654,0.00011820011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939156,0.0000057041034,0.0001381006,0.00020253347,0.00007921748,0.00018287638],"domain_scores_gemma":[0.999659,0.000020603129,0.000020148032,0.00021935318,0.000019865049,0.00006104245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011563124,0.000050608734,0.000053748212,0.000054776658,0.00008207163,0.000044515993,0.00032141607,0.00002456039,0.000007328692],"category_scores_gemma":[0.000009338069,0.000048302,0.000019947802,0.0005472139,0.000006171477,0.00020812404,0.00012453391,0.00007511786,0.000058563048],"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":[5.60256e-7,0.00007582955,0.0011623427,0.0000013154424,0.0000020879597,0.000005347438,0.00047532836,0.5330359,0.00021337354,0.42726713,0.0002872878,0.03747351],"study_design_scores_gemma":[0.00033891416,0.00003211852,0.018614348,0.000029919138,8.876749e-7,0.0000099571525,0.00008990823,0.9346779,0.00030466454,0.04462608,0.0010626159,0.00021271726],"about_ca_topic_score_codex":0.00012253338,"about_ca_topic_score_gemma":0.000058892834,"teacher_disagreement_score":0.6717275,"about_ca_system_score_codex":0.000060783797,"about_ca_system_score_gemma":0.00003064438,"threshold_uncertainty_score":0.19696984},"labels":[],"label_agreement":null},{"id":"W2116890140","doi":"10.1109/icsmc.1997.637345","title":"What is a symbolic measurement process?","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Representation (politics); Computer science; Process (computing); Mathematical structure; Theoretical computer science; Transformation (genetics); Object (grammar); Mathematical theory; Data structure; Algorithm; Artificial intelligence; Programming language; Mathematics","score_opus":0.04217037402033936,"score_gpt":0.2523656937088336,"score_spread":0.21019531968849425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116890140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018485632,0.0029920442,0.9042438,0.050057072,0.0003036712,0.0003102727,6.9493944e-7,0.00045808865,0.039785765],"genre_scores_gemma":[0.961015,0.00039871235,0.029358113,0.003321017,0.00009602593,0.00012393996,3.6610717e-7,0.000005902767,0.005680912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926245,0.000006530368,0.00009050457,0.00020679149,0.00030342038,0.00013029418],"domain_scores_gemma":[0.9994904,0.000005597038,0.000020709733,0.00031313108,0.00011352014,0.000056670684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007984731,0.00005675287,0.000047722137,0.000028301576,0.000107602886,0.00015490958,0.00038472927,0.000018108098,0.00030937907],"category_scores_gemma":[0.000002713982,0.000047641548,0.00002600527,0.00027101758,0.0000134026095,0.0008004827,0.000047591333,0.000037968395,0.00063686765],"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":[5.849608e-7,0.0009527057,0.0002895558,0.00003166131,0.000045189736,0.0000048521697,0.006750091,0.00010388027,0.0006982014,0.4668596,0.1504259,0.37383777],"study_design_scores_gemma":[0.00037259757,0.00006728331,0.0022594798,0.0000460303,0.000007014917,0.000038031514,0.0003142392,0.7980058,0.0037142506,0.047245037,0.14751264,0.00041757998],"about_ca_topic_score_codex":0.0000038954167,"about_ca_topic_score_gemma":0.0000012871147,"teacher_disagreement_score":0.95916647,"about_ca_system_score_codex":0.000023715362,"about_ca_system_score_gemma":0.0000123264335,"threshold_uncertainty_score":0.81858575},"labels":[],"label_agreement":null},{"id":"W2117021925","doi":"10.1371/journal.pone.0078401","title":"A Solution to the Challenge of Optimization on ''Golf-Course''-Like Fitness Landscapes","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Institutes of Health; Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico; National Institute of General Medical Sciences; Financiadora de Estudos e Projetos; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Fitness landscape; Task (project management); Genetic algorithm; Artificial intelligence; Machine learning; Optimization problem; Mathematical optimization; Algorithm; Mathematics; Engineering","score_opus":0.02946641124470764,"score_gpt":0.224545907203595,"score_spread":0.19507949595888735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117021925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031348124,0.00022817291,0.89039457,0.073940046,0.0000914621,0.00097501755,0.000008524706,0.0001603373,0.0028537763],"genre_scores_gemma":[0.9053251,0.00006296585,0.09347693,0.00030515855,0.00012533586,0.00031750413,0.000009096818,0.0000072504445,0.00037064616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992468,0.000026782312,0.00013547292,0.00019665936,0.0002603959,0.00013390358],"domain_scores_gemma":[0.999249,0.00005154483,0.00006142079,0.00043552584,0.0001538462,0.000048677062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010107337,0.00006964956,0.00009207019,0.000041598905,0.00013283589,0.00003401779,0.00043526274,0.00003249134,0.000055927143],"category_scores_gemma":[0.000014409279,0.000051470986,0.000025150359,0.00026966268,0.000015840224,0.0002031273,0.000094846466,0.0000634331,0.00017984952],"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":[0.000034561293,0.023636075,0.0010342856,0.00025722163,0.0005972288,0.0000026397329,0.0076369536,0.41866273,0.009829182,0.43403256,0.035024658,0.06925188],"study_design_scores_gemma":[0.00015405139,0.00016620035,0.0037945942,0.00006086578,0.0000161381,5.522712e-7,0.000026592832,0.9925866,0.00091808377,0.0018140026,0.00034997874,0.00011235591],"about_ca_topic_score_codex":0.000035564757,"about_ca_topic_score_gemma":0.000009172862,"teacher_disagreement_score":0.873977,"about_ca_system_score_codex":0.0000162364,"about_ca_system_score_gemma":0.000022520024,"threshold_uncertainty_score":0.23116618},"labels":[],"label_agreement":null},{"id":"W2117847370","doi":"10.1109/tsmcb.2007.896406","title":"Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection","year":2007,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; RSS; Selection (genetic algorithm); Heuristics; Genetic programming; Block (permutation group theory); Benchmarking; Machine learning; Artificial intelligence; Genetic algorithm; Overhead (engineering); Binary classification; Data mining; Algorithm; Mathematics; Support vector machine","score_opus":0.020893965450406844,"score_gpt":0.2715984832799908,"score_spread":0.250704517829584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117847370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025064463,0.0005026263,0.98204345,0.01042386,0.0016185248,0.0016478681,0.00079426,0.00036465377,0.00009833039],"genre_scores_gemma":[0.44102496,0.0018331538,0.36833593,0.15673192,0.0097013,0.002476262,0.0026769193,0.0012431932,0.015976358],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99433494,0.0003034994,0.0011586894,0.0017327117,0.001105583,0.001364594],"domain_scores_gemma":[0.9973439,0.00028369337,0.00035242614,0.0013070459,0.00022295295,0.0004899729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055551634,0.0008400779,0.0007296214,0.00075490033,0.00086071045,0.00083871395,0.0010670845,0.00096806395,0.000023155199],"category_scores_gemma":[0.00000775237,0.0009176323,0.00022930477,0.0010927434,0.00017036987,0.00022243963,0.000043671367,0.0022875105,0.00015791331],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014238666,0.003526991,0.00021834142,0.0026636617,0.0018059807,0.001342934,0.0035834857,0.17776297,0.0013885896,0.004907459,0.6492176,0.1534396],"study_design_scores_gemma":[0.0004959956,0.00024983694,0.00013195258,0.0004134511,0.00018371195,0.0005030201,0.00004101047,0.33784547,0.00011170426,0.00012699893,0.65880984,0.0010870299],"about_ca_topic_score_codex":0.00033354075,"about_ca_topic_score_gemma":0.00018198042,"teacher_disagreement_score":0.6137075,"about_ca_system_score_codex":0.0003611903,"about_ca_system_score_gemma":0.0001844746,"threshold_uncertainty_score":0.9993274},"labels":[],"label_agreement":null},{"id":"W2118318258","doi":"10.1109/have.2008.4685299","title":"Generation of rule-based adaptive strategies for a collaborative virtual simulation environment","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Adaptability; Artificial intelligence; Machine learning; Classifier (UML); Learning classifier system; Adaptive learning; Unsupervised learning","score_opus":0.06547255662013188,"score_gpt":0.26850567696205707,"score_spread":0.2030331203419252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118318258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017403368,0.000025499023,0.9815194,0.0002213532,0.000025456722,0.00034359464,0.000022048625,0.0000336393,0.00040565536],"genre_scores_gemma":[0.71618253,0.000003508973,0.2835319,0.00003852603,0.000032806925,0.000092663126,0.000017993563,0.0000023457296,0.000097682685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942124,0.000022044866,0.00015572783,0.0001827638,0.0001386251,0.00007958866],"domain_scores_gemma":[0.9995026,0.000105141866,0.00007951763,0.00016729503,0.000119013755,0.000026451977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005892796,0.00006712645,0.00007636464,0.000039904055,0.00016599655,0.000015106875,0.00012742242,0.00003016393,0.000012223697],"category_scores_gemma":[0.0000038185312,0.000061929095,0.000031284202,0.00015099334,0.00005750087,0.00035210594,0.000019748622,0.000021388169,0.000006166017],"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":[0.0000063754646,0.0000899905,0.000012002519,9.871061e-7,0.000005979823,1.6449408e-7,0.0002776594,0.6953048,0.002930178,0.29982436,0.00019565108,0.0013518782],"study_design_scores_gemma":[0.00031638096,0.00023582991,0.00059701194,0.0000013954221,0.0000026030878,4.015701e-7,0.00015112304,0.9907398,0.004823,0.002324688,0.0007338209,0.00007391061],"about_ca_topic_score_codex":0.000007755034,"about_ca_topic_score_gemma":0.0000023242085,"teacher_disagreement_score":0.69877917,"about_ca_system_score_codex":0.00003485051,"about_ca_system_score_gemma":0.00018853802,"threshold_uncertainty_score":0.25253955},"labels":[],"label_agreement":null},{"id":"W2119632600","doi":"10.1504/ijdmb.2011.040388","title":"Combining multiple perspective as intelligent agents into robust approach for biomarker detection in gene expression data","year":2011,"lang":"en","type":"article","venue":"International Journal of Data Mining and Bioinformatics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Perspective (graphical); Gene; Gene expression; Biomarker; Computer science; Computational biology; Expression (computer science); microRNA; Data mining; Biology; Artificial intelligence; Genetics","score_opus":0.15965036198780436,"score_gpt":0.336622064281259,"score_spread":0.17697170229345466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119632600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013865992,0.00011519095,0.9852022,0.00012737472,0.0002622577,0.000117104566,0.000084312785,0.0000112622465,0.00021430904],"genre_scores_gemma":[0.30085155,0.00009299121,0.6987962,0.000042099775,0.000052251664,0.0000025500854,0.00015341386,0.000003543129,0.0000054068983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988776,0.000022330867,0.0005149733,0.00019213911,0.00028236778,0.00011061893],"domain_scores_gemma":[0.99865973,0.00011418483,0.00040769627,0.0004553579,0.00029677866,0.00006625487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073732843,0.00009501711,0.000119348304,0.00023977831,0.0000918507,0.00011405392,0.001996252,0.000046852867,0.0000024868289],"category_scores_gemma":[0.00024277327,0.000079709855,0.00002484058,0.00013285353,0.000041704123,0.0024410652,0.00109424,0.000099469384,0.0000011710979],"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":[0.00027281474,0.0010208536,0.0032660007,0.00006115615,0.00035013256,0.000021286784,0.032926347,0.0003411169,0.0016838696,0.0012659674,0.0023785036,0.95641196],"study_design_scores_gemma":[0.0006177345,0.00008561528,0.00092513376,0.00006886211,0.000010538669,0.00017973724,0.0046254573,0.9911951,0.0014149821,0.00040664268,0.00036747998,0.000102761725],"about_ca_topic_score_codex":0.00006687723,"about_ca_topic_score_gemma":0.000010580004,"teacher_disagreement_score":0.99085397,"about_ca_system_score_codex":0.00006593099,"about_ca_system_score_gemma":0.00007228009,"threshold_uncertainty_score":0.3709566},"labels":[],"label_agreement":null},{"id":"W2119775079","doi":"10.2991/978-94-6239-030-0_15","title":"Probabilistic Evolutionary Procedure Learning","year":2014,"lang":"en","type":"book-chapter","venue":"Atlantis thinking machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pyrogenesis (Canada)","funders":"","keywords":"Computer science; Probabilistic logic; Evolutionary algorithm; Artificial intelligence; Software deployment; Component (thermodynamics); Evolutionary programming; Machine learning; Software engineering","score_opus":0.009575638731693061,"score_gpt":0.22264749788082505,"score_spread":0.213071859149132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119775079","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017765518,0.0037787827,0.38030466,0.0039144014,0.0014077511,0.0010717977,0.000024025088,0.0023884678,0.60693246],"genre_scores_gemma":[0.02487024,0.000395095,0.067215964,0.0005419755,0.0017693406,0.00008881601,0.00046348688,0.00014786408,0.9045072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99775296,0.000041658728,0.00042656861,0.00086920284,0.00057304226,0.0003365885],"domain_scores_gemma":[0.9984836,0.00018604718,0.00030462202,0.00073993934,0.0001789588,0.00010683269],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029030614,0.0004577721,0.00040563164,0.00017697005,0.0007594859,0.0001906657,0.0013915204,0.00031470569,0.000059846625],"category_scores_gemma":[0.00005388601,0.00042069572,0.00019735606,0.00010381207,0.00011482637,0.00020675585,0.00057727646,0.00082243455,0.0004250132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014261375,0.000016512755,0.00018254445,0.00007200368,0.000038985392,0.000013762996,0.000137954,0.0013841083,0.000006830085,0.9826867,0.008540605,0.006918579],"study_design_scores_gemma":[0.00014586172,0.000074909505,0.001184281,0.00027927294,0.000043530195,0.0002632423,0.0000012016282,0.14707273,0.0000013748084,0.47824338,0.37203103,0.0006591767],"about_ca_topic_score_codex":0.000044837143,"about_ca_topic_score_gemma":0.000011193439,"teacher_disagreement_score":0.50444335,"about_ca_system_score_codex":0.00008644988,"about_ca_system_score_gemma":0.00015325037,"threshold_uncertainty_score":0.99982446},"labels":[],"label_agreement":null},{"id":"W2121489891","doi":"10.1109/cec.2008.4630863","title":"Automatic modeling of a novel gene expression programming based on statistical analysis and critical velocity","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Gene expression programming; Computer science; Statistic; Convergence (economics); Artificial neural network; Set (abstract data type); Artificial intelligence; Population; Genetic programming; Machine learning; Function (biology); Data mining; Evolutionary algorithm; Algorithm; Mathematics; Statistics","score_opus":0.033254297425453154,"score_gpt":0.29304871374498,"score_spread":0.2597944163195268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121489891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05369193,0.000008379222,0.9458229,0.00025880383,0.000007598961,0.000072123716,0.0000069902717,0.000066008906,0.00006529295],"genre_scores_gemma":[0.49787208,7.164753e-7,0.5020778,0.000026454836,0.0000043725604,0.000011808672,0.000002887548,0.0000013148066,0.000002506666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919015,0.000019986106,0.00019077757,0.00024414103,0.00022785985,0.00012706101],"domain_scores_gemma":[0.99935126,0.0002229526,0.000024990877,0.00024169771,0.00007492648,0.000084173436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011443344,0.00006775975,0.0001332298,0.00010731688,0.00016451064,0.000019344412,0.00014649155,0.00002979936,0.000014451146],"category_scores_gemma":[0.000063108906,0.000056564742,0.000037474358,0.0004419903,0.00007596144,0.000107279884,0.00006282829,0.000055474567,0.0000010748389],"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":[0.000023544402,0.0059362794,0.0075628976,0.00019663491,0.00019023771,0.00003753965,0.001106987,0.35674,0.011907786,0.51965666,0.00018674768,0.09645472],"study_design_scores_gemma":[0.00013883453,0.000038195583,0.004520454,0.0000082463575,0.000020668787,0.0000046786004,0.0000062143004,0.9942317,0.0005373033,0.00042335934,0.0000030071374,0.00006735634],"about_ca_topic_score_codex":0.000034789213,"about_ca_topic_score_gemma":0.0000012053631,"teacher_disagreement_score":0.6374917,"about_ca_system_score_codex":0.00001285369,"about_ca_system_score_gemma":0.000044318862,"threshold_uncertainty_score":0.23066431},"labels":[],"label_agreement":null},{"id":"W2123092006","doi":"10.1007/978-3-540-78671-9_16","title":"A Comparison of Cartesian Genetic Programming and Linear Genetic Programming","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Verafin (Canada); Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Computer science; Directed acyclic graph; Graph; Linear programming; Implementation; Directed graph; Symbolic regression; Genetic algorithm; Theoretical computer science; Cartesian coordinate system; Benchmark (surveying); Algorithm; Artificial intelligence; Programming language; Mathematics; Machine learning","score_opus":0.021446460331969376,"score_gpt":0.2759214630943454,"score_spread":0.254475002762376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123092006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010917068,0.0028522322,0.9945948,0.0003051124,0.00023331703,0.00064153195,0.0000027859512,0.00010817913,0.00017034182],"genre_scores_gemma":[0.16790216,0.00009947169,0.831624,0.000067568915,0.00020289501,0.000028129292,0.0000030339095,0.00002102376,0.000051745512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688727,0.000024399169,0.0006498196,0.0011898503,0.0007105724,0.00053809606],"domain_scores_gemma":[0.9980835,0.00016618523,0.00035712065,0.00094574474,0.00026060894,0.00018684457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022177401,0.00039816025,0.0005389819,0.00045917943,0.0003486315,0.00017767311,0.0016387578,0.00022261881,0.0000031142613],"category_scores_gemma":[0.000019539342,0.00038690245,0.00009810592,0.00063499185,0.001031514,0.0002240296,0.0008121061,0.00049467804,0.0000061595265],"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":[0.0000011490304,0.00006757076,0.0009798927,0.00004872626,0.0000087079925,0.000025224637,0.0014381314,0.010060743,0.00005476417,0.0015538913,0.0000069548814,0.98575425],"study_design_scores_gemma":[0.00023156863,0.00036186358,0.0035257176,0.00027553193,0.000015935555,0.00026383315,0.0000010398718,0.98064977,0.00035732935,0.008710541,0.0049903207,0.0006165214],"about_ca_topic_score_codex":0.000034373777,"about_ca_topic_score_gemma":0.000037851358,"teacher_disagreement_score":0.9851377,"about_ca_system_score_codex":0.00009795799,"about_ca_system_score_gemma":0.00037706268,"threshold_uncertainty_score":0.99985826},"labels":[],"label_agreement":null},{"id":"W2123251383","doi":"10.1162/evco.2007.15.2.199","title":"Reducing the Number of Fitness Evaluations in Graph Genetic Programming Using a Canonical Graph Indexed Database","year":2007,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic programming; Computer science; Graph; Graph database; Theoretical computer science; Artificial intelligence","score_opus":0.03178961716159892,"score_gpt":0.33922122399863697,"score_spread":0.3074316068370381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123251383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.306343,0.00025433078,0.6924085,0.0003164888,0.0001479269,0.0003898829,0.000007003517,0.0000536774,0.00007915989],"genre_scores_gemma":[0.6624811,0.000006611101,0.33733606,0.000022520915,0.000061816405,0.000035446752,0.000041640757,0.000007544532,0.0000072138123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978766,0.00017266741,0.0006456822,0.00042824,0.0005365153,0.00034030125],"domain_scores_gemma":[0.9985862,0.00035751204,0.00024851295,0.00043084365,0.0002995412,0.00007738565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008750145,0.00015279741,0.00015867666,0.00028359293,0.00040996543,0.000040576982,0.00049684115,0.00007285051,0.000009457912],"category_scores_gemma":[0.00006251336,0.00014170875,0.00008554868,0.0023132167,0.00018319993,0.0005088951,0.00018541352,0.00021920656,0.0000074065542],"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":[0.000039291284,0.0012247787,0.043815125,0.00007375124,0.00007051774,0.000032535274,0.0021713541,0.75405926,0.00417307,0.105507046,0.00040256637,0.088430725],"study_design_scores_gemma":[0.00037293803,0.000018177703,0.33837438,0.000057234258,0.000014956861,0.00013696277,0.00011742263,0.6442813,0.00007904026,0.0163215,0.00006674377,0.00015933278],"about_ca_topic_score_codex":0.0006571775,"about_ca_topic_score_gemma":0.00007687454,"teacher_disagreement_score":0.35613814,"about_ca_system_score_codex":0.0001870751,"about_ca_system_score_gemma":0.00038362446,"threshold_uncertainty_score":0.5778715},"labels":[],"label_agreement":null},{"id":"W2123884785","doi":"10.1109/cec.2011.5949704","title":"Financial control of the evolution of autonomous non-player characters","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Adversary; Movement (music); Population; Control (management); Function (biology); Autonomous agent; Controller (irrigation); Object (grammar); Stochastic game; Multi-agent system; Artificial intelligence; Computer security; Microeconomics; Economics; Biology","score_opus":0.009353082662482448,"score_gpt":0.19447645235728936,"score_spread":0.18512336969480692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123884785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07183281,0.000018504705,0.9235881,0.00031024238,0.00015055621,0.00016235672,0.000005979063,0.000020865138,0.003910588],"genre_scores_gemma":[0.98789996,8.637133e-7,0.011748307,0.00007230002,0.000020780935,0.000015137233,2.4662805e-7,0.0000016901645,0.00024073277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995114,0.000014413843,0.00017228101,0.00011357233,0.00009942043,0.00008886842],"domain_scores_gemma":[0.9994266,0.000018279907,0.00011572677,0.0003428163,0.000079618345,0.000016936798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009452705,0.000048094375,0.0000799042,0.000028461507,0.000055242872,0.0000028916268,0.00048743543,0.000030449819,0.00002013438],"category_scores_gemma":[0.000009061282,0.00003285216,0.000061762905,0.00018768206,0.000058541238,0.00014678613,0.00006816489,0.000043612636,0.0000105201025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045391207,0.00016713199,0.004653137,0.0000052897594,0.0000084695175,1.6418927e-7,0.00041475063,0.000034293596,0.007853784,0.9837915,0.000297264,0.0027696886],"study_design_scores_gemma":[0.0003346336,0.00005571871,0.9214106,0.000008302255,0.0000074162695,0.0000036927659,0.000018523566,0.05630148,0.0091811335,0.012321221,0.0002804851,0.000076767035],"about_ca_topic_score_codex":0.00026111945,"about_ca_topic_score_gemma":0.00000759512,"teacher_disagreement_score":0.97147024,"about_ca_system_score_codex":0.00002874976,"about_ca_system_score_gemma":0.0001367257,"threshold_uncertainty_score":0.13396722},"labels":[],"label_agreement":null},{"id":"W2123885828","doi":"10.1109/icsmc.2007.4413999","title":"One-class learning with multi-objective genetic programming","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"One-class classification; Artificial intelligence; Support vector machine; Computer science; Classifier (UML); Novelty detection; Machine learning; Genetic programming; Class (philosophy); Pattern recognition (psychology); Binary classification; Multiclass classification; Novelty; Data mining","score_opus":0.016123292718468817,"score_gpt":0.2533175292702368,"score_spread":0.237194236551768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123885828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011627263,0.00004289655,0.9840074,0.00022857772,0.00002141746,0.00017930461,9.7290446e-8,0.00025797953,0.0036350642],"genre_scores_gemma":[0.384636,0.0000018186738,0.61438334,0.000044768876,0.000029314706,0.000019149225,6.561386e-7,0.0000043149075,0.0008806345],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992037,0.00001366144,0.000109718465,0.00026946576,0.0001585244,0.00024491834],"domain_scores_gemma":[0.99953955,0.00005227497,0.000043017106,0.00020243492,0.00008594828,0.000076786375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001555481,0.000072695366,0.00006305209,0.00005454624,0.00022120913,0.000061681756,0.00026095385,0.000028289292,0.0000070731603],"category_scores_gemma":[0.0000074202844,0.00006303905,0.000021028003,0.00040542273,0.000040265473,0.00017633714,0.00007989818,0.0001268539,0.000052256233],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068769527,0.00051222707,0.015929546,0.0000092507735,0.00004536525,0.000019638475,0.0013444363,0.002670833,0.0011188122,0.092419714,0.000047639456,0.88587564],"study_design_scores_gemma":[0.0010037572,0.00048709236,0.53069997,0.000030276904,0.000014430749,0.00008489736,0.00068380014,0.43320435,0.00298568,0.0009804213,0.02923813,0.0005871709],"about_ca_topic_score_codex":0.00005550003,"about_ca_topic_score_gemma":0.00006705411,"teacher_disagreement_score":0.8852885,"about_ca_system_score_codex":0.00003619996,"about_ca_system_score_gemma":0.000037782243,"threshold_uncertainty_score":0.25706577},"labels":[],"label_agreement":null},{"id":"W2124875591","doi":"10.2166/hydro.2009.032","title":"Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content","year":2009,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"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","keywords":"Water content; Environmental science; Genetic programming; Lag; Soil science; Moisture; Computer science; Meteorology; Machine learning; Engineering; Geotechnical engineering; Geography","score_opus":0.050630034897519015,"score_gpt":0.282061851503608,"score_spread":0.231431816606089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124875591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32736057,0.0008411279,0.66377604,0.0071443645,0.00010737647,0.00032348948,0.000020707317,0.00003194123,0.00039438932],"genre_scores_gemma":[0.7500838,0.000047364207,0.249732,0.00007848381,0.000048844406,9.265065e-7,0.000002884521,0.0000020458415,0.0000036241295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858284,0.000041115538,0.0008140511,0.00005232704,0.00040606537,0.00010360633],"domain_scores_gemma":[0.9979055,0.00018658832,0.0010919233,0.0005190196,0.00026558785,0.000031402244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068255625,0.000081858125,0.00017223528,0.000079138576,0.00020593104,0.000031982945,0.001088344,0.00003304309,6.8995513e-7],"category_scores_gemma":[0.00015633508,0.00004678158,0.000062022664,0.0002492026,0.00015309377,0.0011582072,0.00015424794,0.0002044483,2.1365018e-7],"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":[0.000003547799,0.00016198104,0.00023228451,0.00015277075,0.00008344049,0.0000010655233,0.011797913,0.875737,0.0023722604,0.09176438,0.0016299691,0.016063353],"study_design_scores_gemma":[0.00007038101,0.0000763823,0.0012068482,0.00015147134,0.000020870324,0.0001511659,0.0012567107,0.98476285,0.0007018951,0.01147234,0.00008017151,0.000048927224],"about_ca_topic_score_codex":0.000022435082,"about_ca_topic_score_gemma":0.0000015624548,"teacher_disagreement_score":0.42272323,"about_ca_system_score_codex":0.000049109898,"about_ca_system_score_gemma":0.0001708026,"threshold_uncertainty_score":0.20224322},"labels":[],"label_agreement":null},{"id":"W2125875816","doi":"10.5430/air.v1n1p1","title":"Two-Strategy reinforcement group cooperation based symbiotic evolution for TSK-type fuzzy controller design","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council","keywords":"Crossover; Controller (irrigation); Set (abstract data type); Reinforcement; Computer science; Population; Fuzzy logic; SIGNAL (programming language); Reinforcement learning; Group (periodic table); Compensation (psychology); Control (management); Artificial intelligence; Base (topology); Engineering; Mathematics; Biology; Psychology","score_opus":0.2209426353081047,"score_gpt":0.4114545148173586,"score_spread":0.1905118795092539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125875816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011464511,0.0002685417,0.9951657,0.0010201836,0.00026689348,0.0014518623,0.000002945893,0.00009303713,0.00058439624],"genre_scores_gemma":[0.94116265,0.000016718997,0.057744794,0.00006842691,0.00037599902,0.0003942597,0.000022292608,0.0000126785335,0.0002021705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973807,0.00031978122,0.0004528453,0.00041172872,0.00058559433,0.000849392],"domain_scores_gemma":[0.9975178,0.0008116272,0.00007230676,0.00046103733,0.0009095762,0.0002276187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003557712,0.00015055816,0.00015437928,0.00024265643,0.0008382701,0.00025374177,0.0006389918,0.00009318478,0.000049179398],"category_scores_gemma":[0.00030612442,0.00014102011,0.00006983678,0.0011902411,0.00016297399,0.00075659674,0.00010206281,0.00024715258,0.00049893535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059185073,0.00025936338,0.000033961172,0.000009638981,0.000011346064,5.151733e-7,0.00012281095,0.047343813,0.01707791,0.91651624,0.00044935933,0.018115858],"study_design_scores_gemma":[0.00010127146,0.00042234908,0.000092644106,0.0000143801935,0.0000057149728,0.000002936338,0.00014650996,0.9121759,0.022616103,0.0637949,0.00045468597,0.00017262617],"about_ca_topic_score_codex":0.00013008581,"about_ca_topic_score_gemma":0.000019418747,"teacher_disagreement_score":0.9400162,"about_ca_system_score_codex":0.00028839606,"about_ca_system_score_gemma":0.00029434427,"threshold_uncertainty_score":0.6447381},"labels":[],"label_agreement":null},{"id":"W2126021476","doi":"10.1109/iecon.2000.972588","title":"Functional reconstruction of dynamical systems from time series using genetic programming","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Saskatchewan","funders":"","keywords":"Genetic programming; Computer science; Series (stratigraphy); Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics; Biology","score_opus":0.018939920243612227,"score_gpt":0.20513082189985224,"score_spread":0.18619090165624003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126021476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069047906,0.00018743242,0.9297657,0.00016047574,0.00020146155,0.00010992287,0.0000052988885,0.00009418064,0.00042763108],"genre_scores_gemma":[0.21989498,0.0000070381775,0.7791713,0.000007311361,0.0001225951,0.000017057113,0.000005727775,0.000004522764,0.00076946063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993638,0.000020946783,0.00019127196,0.0001902914,0.00013495811,0.000098748686],"domain_scores_gemma":[0.9996049,0.000028545328,0.000067127025,0.00019572252,0.00006714598,0.00003657864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035029385,0.00006009319,0.00008346143,0.00004005974,0.00010021432,0.000045703808,0.00013982976,0.000037610083,0.00015267979],"category_scores_gemma":[0.000003934489,0.000056524896,0.00003230067,0.00020113682,0.00005425875,0.00030130407,0.00004824138,0.000041474905,0.00005709195],"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":[0.000012232112,0.00081376726,0.008839787,0.00006974922,0.0002253766,0.0000096645745,0.0005034652,0.06117443,0.047706697,0.25171724,0.0025027236,0.62642485],"study_design_scores_gemma":[0.00007028666,0.000018664206,0.003724962,0.0000111251575,0.0000048916822,0.000100103454,0.000022209546,0.9944209,0.00011340295,0.0009980262,0.00044435385,0.00007107887],"about_ca_topic_score_codex":0.00010784254,"about_ca_topic_score_gemma":0.000001313003,"teacher_disagreement_score":0.9332465,"about_ca_system_score_codex":0.000032184154,"about_ca_system_score_gemma":0.000013739826,"threshold_uncertainty_score":0.23050185},"labels":[],"label_agreement":null},{"id":"W2126223847","doi":"10.1109/mcte.2002.1175046","title":"Genetic circuit design from an electronics perspective","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Circuit design; Linear circuit; Capacitor; Computer science; Circuit extraction; Discrete circuit; Electronic circuit; Equivalent circuit; Electronic engineering; Electronics; Electronic circuit simulation; Network analysis; Mixed-signal integrated circuit; Electrical element; Amplifier; Electrical engineering; Engineering; CMOS; Voltage","score_opus":0.022492694881816874,"score_gpt":0.2517357935888396,"score_spread":0.2292430987070227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126223847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018589841,0.00052685256,0.9892898,0.00038525823,0.000033633845,0.00010502285,9.881078e-7,0.00014164978,0.0076577975],"genre_scores_gemma":[0.465069,0.000030798354,0.53434217,0.00024108552,0.00003395926,0.000025936708,8.377844e-7,0.000004680334,0.00025156373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992518,0.000069556685,0.000078896184,0.00030466408,0.00010799942,0.00018709946],"domain_scores_gemma":[0.99936277,0.000047376485,0.000020936961,0.00040969392,0.00007790493,0.000081316204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076926066,0.000067683504,0.000053745272,0.000025774905,0.00014223316,0.000062509935,0.00038309934,0.000028990873,0.00009118374],"category_scores_gemma":[0.000011560838,0.0000645696,0.000022797158,0.000218102,0.000020355797,0.00021820464,0.000020981277,0.000064541164,0.00010538697],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.3310344e-7,0.000053155516,0.000053502994,8.4677616e-8,0.0000058656224,0.0000010200089,0.00017567689,0.00030423494,0.00057302264,0.99636775,0.0002150247,0.0022505391],"study_design_scores_gemma":[0.00014375389,0.000097554584,0.0043681264,7.049755e-7,0.0000040858695,0.000014148383,0.00014840365,0.15013546,0.0015332676,0.83705276,0.006324222,0.00017753529],"about_ca_topic_score_codex":0.00006672491,"about_ca_topic_score_gemma":0.000013371794,"teacher_disagreement_score":0.46321,"about_ca_system_score_codex":0.00012662064,"about_ca_system_score_gemma":0.00021145977,"threshold_uncertainty_score":0.26330718},"labels":[],"label_agreement":null},{"id":"W2126509357","doi":"10.1109/iscas.2002.1010161","title":"Chaotic system reconstruction from noisy time series measurements using improved least squares genetic programming","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Chaotic; Clutter; Computer science; A priori and a posteriori; Noise (video); Series (stratigraphy); Radar; Algorithm; Genetic programming; Least-squares function approximation; Genetic algorithm; Time series; Gaussian noise; Artificial intelligence; Mathematics; Machine learning; Statistics; Telecommunications","score_opus":0.023543057637854945,"score_gpt":0.22061633885997103,"score_spread":0.1970732812221161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126509357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09769364,0.00016398549,0.90023935,0.00008155437,0.0003196037,0.0003721583,0.0000033580643,0.00032123984,0.00080513884],"genre_scores_gemma":[0.3348983,0.0000013404259,0.6648296,0.00001186672,0.00006390225,0.000039086135,0.0000026755404,0.000008582858,0.00014465707],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998853,0.000069230024,0.00025689474,0.00038379457,0.00019927841,0.00023774676],"domain_scores_gemma":[0.9993155,0.000015270389,0.000076349395,0.00038465115,0.00012644893,0.000081783124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013856667,0.00013444903,0.0001299482,0.000057339224,0.00032140015,0.00016680674,0.0002594389,0.00005159985,0.000027817805],"category_scores_gemma":[0.000009788537,0.00012758888,0.000048008682,0.00029518866,0.000042239055,0.00047949798,0.0000493982,0.00006222393,0.00007116275],"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":[0.000025903542,0.0007930441,0.022609055,0.00027726355,0.00048511638,0.000021036254,0.0015155877,0.0062629078,0.44971615,0.101496436,0.00027192006,0.41652557],"study_design_scores_gemma":[0.00074146665,0.00013147095,0.0074128625,0.00016055965,0.000059351776,0.00050367403,0.0009203473,0.97044355,0.015516346,0.0017986656,0.0015783345,0.00073337834],"about_ca_topic_score_codex":0.00017563274,"about_ca_topic_score_gemma":0.000013340555,"teacher_disagreement_score":0.96418065,"about_ca_system_score_codex":0.00012886942,"about_ca_system_score_gemma":0.00008552808,"threshold_uncertainty_score":0.52029234},"labels":[],"label_agreement":null},{"id":"W2127322981","doi":"10.1016/j.asoc.2012.08.046","title":"Review of phenotypic diversity formulations for diagnostic tool","year":2012,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Diversity (politics); Computer science; Computational biology; Biology; Sociology","score_opus":0.02365008938422352,"score_gpt":0.26270026346415615,"score_spread":0.23905017407993262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127322981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031570313,0.0029324964,0.9922441,0.0002395284,0.00010323652,0.00054210884,0.000005691104,0.00009712746,0.00067863066],"genre_scores_gemma":[0.723493,0.000109794455,0.27563635,0.0005905641,0.000108839406,0.00003384644,0.000013881979,0.0000048990355,0.000008843178],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999198,0.000010972449,0.00024640426,0.00017132283,0.00012635971,0.00024690427],"domain_scores_gemma":[0.9984567,0.0009369023,0.00014700787,0.00031195206,0.00009026393,0.000057182326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003856963,0.000087768814,0.00015386833,0.000033141067,0.00047031263,0.00001096744,0.00041898413,0.000029220826,0.00000533541],"category_scores_gemma":[0.0001153569,0.00008971464,0.00007196648,0.00028018386,0.000025659907,0.00018554414,0.00054208393,0.000058406484,0.000017764269],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010364108,0.00016309132,0.002149859,0.00079980283,0.00002090019,4.768639e-8,0.00048521327,0.00041963396,0.00007783394,0.90105325,0.0030139352,0.09181541],"study_design_scores_gemma":[0.0027424518,0.000158768,0.20441122,0.004027832,0.00034473356,0.000028729988,0.00014457898,0.5226543,0.0012645457,0.17988466,0.08224222,0.0020959412],"about_ca_topic_score_codex":0.0000047907247,"about_ca_topic_score_gemma":2.0318807e-7,"teacher_disagreement_score":0.7211686,"about_ca_system_score_codex":0.000027908576,"about_ca_system_score_gemma":0.00002549279,"threshold_uncertainty_score":0.36584568},"labels":[],"label_agreement":null},{"id":"W2128670925","doi":"10.1109/cec.2009.4983255","title":"Evolving novel image features using Genetic Programming-based image transforms","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Artificial intelligence; Grayscale; Computer science; Image (mathematics); Set (abstract data type); Pattern recognition (psychology); Genetic algorithm; Feature (linguistics); Cartesian coordinate system; Feature vector; Task (project management); Evolutionary computation; Moment (physics); Feature extraction; Computer vision; Contextual image classification; Mathematics; Machine learning","score_opus":0.012722593608641882,"score_gpt":0.26132847081821237,"score_spread":0.24860587720957047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128670925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032474787,0.00014087222,0.99280244,0.0019049067,0.000041014333,0.00028444346,0.000002688876,0.00029185947,0.0012842948],"genre_scores_gemma":[0.16623406,0.0000019874385,0.8332454,0.0003267896,0.000057826048,0.000014124413,0.000003851662,0.000006344616,0.00010961971],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989043,0.00000916272,0.0001866185,0.00035603845,0.00022004453,0.00032382208],"domain_scores_gemma":[0.9993174,0.000023137962,0.00004473559,0.0004025748,0.000109157496,0.00010300639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009286018,0.00014472746,0.000108002634,0.00008203101,0.0002689401,0.0002787253,0.00053429906,0.00004860815,0.00002300713],"category_scores_gemma":[0.0000070935707,0.000121521596,0.00008809457,0.00045435512,0.00005106983,0.0005784101,0.000027387492,0.000112752285,0.000015233841],"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":[0.00000808432,0.001663791,0.00021798801,0.000044968896,0.000028518285,0.00003465418,0.00057081535,0.003022511,0.5970415,0.066491015,0.0014884721,0.3293877],"study_design_scores_gemma":[0.00075296045,0.00015556437,0.06353764,0.000030158866,0.000016592077,0.00008389361,0.00002942909,0.90969795,0.020357475,0.003476848,0.0013624567,0.0004990154],"about_ca_topic_score_codex":0.000036119123,"about_ca_topic_score_gemma":0.0000040004447,"teacher_disagreement_score":0.90667546,"about_ca_system_score_codex":0.00004772762,"about_ca_system_score_gemma":0.000096965974,"threshold_uncertainty_score":0.4955507},"labels":[],"label_agreement":null},{"id":"W2128780767","doi":"10.1007/978-1-4419-7747-2_10","title":"Symbolic Density Models of One-in-a-Billion Statistical Tails via Importance Sampling and Genetic Programming","year":2010,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Genetic programming; Sampling (signal processing); Computer science; Symbolic regression; Statistics; Econometrics; Mathematics; Artificial intelligence","score_opus":0.022841449790407496,"score_gpt":0.24486813069796118,"score_spread":0.22202668090755368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128780767","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01867821,0.0030371288,0.97681254,0.00013140116,0.000100049176,0.00055695913,0.000028680624,0.000058378635,0.0005966262],"genre_scores_gemma":[0.28578287,0.0006689474,0.7129502,0.000026459302,0.000102627404,0.000031946132,0.00009563631,0.0000233627,0.0003179228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979907,0.00002972508,0.0006319203,0.0007224368,0.00035235335,0.00027281468],"domain_scores_gemma":[0.9988785,0.00016048284,0.0003053517,0.0003116176,0.00020215835,0.00014191876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013012785,0.00028490848,0.00037379612,0.00022892073,0.00021843445,0.0000475183,0.00021463176,0.00027969782,0.000007751623],"category_scores_gemma":[0.00000938318,0.00032722062,0.0000498449,0.00011018184,0.00026644088,0.0001862367,0.0002234098,0.0003326281,0.000004681522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029977271,0.0002656179,0.0039944984,0.0003666899,0.00011046425,0.00005456166,0.00063137,0.032509126,0.0007962436,0.50694263,0.000058138015,0.45424068],"study_design_scores_gemma":[0.0002258851,0.00008172974,0.101399705,0.00006753263,0.00003402672,0.00019847647,0.0000045527117,0.4175623,0.0000047251474,0.47985446,0.00027362848,0.00029296151],"about_ca_topic_score_codex":0.000049492424,"about_ca_topic_score_gemma":0.000032544904,"teacher_disagreement_score":0.4539477,"about_ca_system_score_codex":0.00006624849,"about_ca_system_score_gemma":0.000145995,"threshold_uncertainty_score":0.999918},"labels":[],"label_agreement":null},{"id":"W2129140651","doi":"10.1145/1569901.1570033","title":"Neutrality and variability","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evolvability; Computer science; Exploit; Neutrality; Neutral network; Evolutionary biology; Artificial intelligence; Biology","score_opus":0.011607046838409841,"score_gpt":0.24984111687891367,"score_spread":0.23823407004050381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129140651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014739222,0.000011893216,0.94953,0.014072304,0.000017136546,0.00004928037,3.872937e-7,0.00011539339,0.02146436],"genre_scores_gemma":[0.8790201,0.0000029303703,0.12029724,0.0004929124,0.000013144894,0.000001865794,2.640504e-7,2.8425345e-7,0.00017126578],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99970585,0.000012486184,0.00005027608,0.00013485971,0.000037469134,0.000059056376],"domain_scores_gemma":[0.99971324,0.000019360317,0.00000756726,0.00020855795,0.000014521191,0.000036731355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012412551,0.000026187598,0.000028547875,0.000006932186,0.000059587146,0.000027006394,0.00012998104,0.0000121645835,0.0000069248536],"category_scores_gemma":[0.000004494815,0.000021716482,0.000008785664,0.00008914881,0.00001850279,0.00016469091,0.000028336563,0.000025324864,0.0000076867855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.911773e-8,0.000019800178,0.00023031326,2.2640438e-7,2.9802285e-7,1.5732357e-7,0.000021088495,0.0000020902953,0.00008728859,0.9793485,0.00029432125,0.019995842],"study_design_scores_gemma":[0.000052259067,0.000020374297,0.442673,4.297541e-7,6.0878307e-7,0.000009083067,0.0000025799823,0.020375174,0.00018867593,0.53301996,0.0036043108,0.000053575342],"about_ca_topic_score_codex":0.00000594371,"about_ca_topic_score_gemma":4.0087465e-7,"teacher_disagreement_score":0.8642809,"about_ca_system_score_codex":0.0000048100096,"about_ca_system_score_gemma":0.000008688587,"threshold_uncertainty_score":0.08855724},"labels":[],"label_agreement":null},{"id":"W2130130539","doi":"10.1109/tsmcb.2008.927249","title":"Score-Based Resampling Method for Evolutionary Algorithms","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Korea Institute of Industrial Technology","keywords":"Resampling; Chromosome; Evolutionary algorithm; Computer science; Fraction (chemistry); Algorithm; Realization (probability); Function (biology); Selection (genetic algorithm); Process (computing); Score; Artificial intelligence; Mathematical optimization; Mathematics; Machine learning; Gene; Statistics; Biology; Genetics","score_opus":0.04447657491174278,"score_gpt":0.2769372254744446,"score_spread":0.23246065056270182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130130539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022681558,0.00073825615,0.99292743,0.0006975595,0.001121026,0.0011080478,0.00015512703,0.0003287349,0.0006556448],"genre_scores_gemma":[0.5764822,0.00050772,0.4145417,0.00043104278,0.0004787899,0.0011910861,0.000036593676,0.00008992157,0.0062409854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969097,0.00016568386,0.0007289473,0.0010011004,0.00057222374,0.00062237174],"domain_scores_gemma":[0.9975824,0.00056464394,0.00021995431,0.00095291255,0.0003208369,0.0003592943],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043176982,0.00043113416,0.00044731257,0.0002849022,0.001038132,0.00016197424,0.00065091153,0.00024191444,0.000016446429],"category_scores_gemma":[0.000009491709,0.00044686135,0.0002317202,0.0005308314,0.00023026686,0.00022406956,0.0000122408255,0.00034345136,0.00006799942],"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":[0.00026257953,0.004117641,0.00055413874,0.0006898368,0.000734311,0.00010314891,0.0027933514,0.6799723,0.0026390112,0.18346162,0.034999773,0.089672275],"study_design_scores_gemma":[0.0014166487,0.00043429426,0.00081446586,0.00014473517,0.000072420225,0.00025634415,0.00007083843,0.9321464,0.001726719,0.0015890413,0.060624108,0.0007039952],"about_ca_topic_score_codex":0.0002013299,"about_ca_topic_score_gemma":0.000023418323,"teacher_disagreement_score":0.5783858,"about_ca_system_score_codex":0.00013307037,"about_ca_system_score_gemma":0.00019872031,"threshold_uncertainty_score":0.9997983},"labels":[],"label_agreement":null},{"id":"W2130506830","doi":"10.1109/tai.1997.632230","title":"A generic tool for distributed AI with matching as message passing","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Inference; Inference engine; Genetic programming; Theoretical computer science; Message passing; Matching (statistics); Computation; Artificial intelligence; Programming language; Mathematics","score_opus":0.01602111604510606,"score_gpt":0.23923221527340724,"score_spread":0.22321109922830118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130506830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005148726,0.00004559893,0.9869856,0.0061786664,0.000027075843,0.00017574924,0.000009341015,0.00017533226,0.0012538935],"genre_scores_gemma":[0.48022485,0.000005476887,0.5168617,0.0008547131,0.00006465806,0.00014822368,0.000014211706,0.000007657983,0.0018185435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993647,0.000008776145,0.00010474933,0.00022964262,0.000115084,0.00017707437],"domain_scores_gemma":[0.99955404,0.000045623325,0.00003412558,0.0002715359,0.00004966129,0.000045024397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005569201,0.00007712145,0.00007110209,0.000023526856,0.00027092747,0.00014144834,0.00027931057,0.000022296286,0.000049892657],"category_scores_gemma":[0.0000041194917,0.000058846777,0.00003100969,0.00027275758,0.000018572611,0.00036675594,0.000058579917,0.000048284444,0.000038880233],"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":[0.0000040655946,0.00030050502,0.00027797106,0.000020044745,0.00003931519,0.00001116389,0.0004300887,0.0032754214,0.0014206796,0.882413,0.029876053,0.08193168],"study_design_scores_gemma":[0.0004402243,0.000084062136,0.0016354176,0.000013221717,0.000008401292,0.000062399704,0.00003372614,0.9348241,0.00051118137,0.019994156,0.042135403,0.00025771605],"about_ca_topic_score_codex":0.000017283994,"about_ca_topic_score_gemma":0.000002676242,"teacher_disagreement_score":0.93154866,"about_ca_system_score_codex":0.000026272874,"about_ca_system_score_gemma":0.000017823537,"threshold_uncertainty_score":0.23997019},"labels":[],"label_agreement":null},{"id":"W2132351374","doi":"10.1109/icsmc.2000.886606","title":"Page-based linear genetic programming","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Crossover; Computer science; Genetic programming; Generality; Bottleneck; Genetic representation; Evolutionary computation; Overhead (engineering); Context (archaeology); Computation; Genetic algorithm; A priori and a posteriori; Theoretical computer science; Parallel computing; Algorithm; Programming language; Artificial intelligence; Machine learning","score_opus":0.023261568925511802,"score_gpt":0.23567850857716224,"score_spread":0.21241693965165043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132351374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012493379,0.00012053025,0.9919525,0.0026399451,0.000035664565,0.00010157977,3.260235e-7,0.0002410367,0.0036591021],"genre_scores_gemma":[0.19334963,0.000004643566,0.80485994,0.00033672518,0.000053541535,0.00003971636,7.3108123e-7,0.000003050811,0.0013520247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999489,0.000009336932,0.000087503264,0.0001749759,0.000103389146,0.00013576022],"domain_scores_gemma":[0.9995742,0.000021670294,0.000018204417,0.00030193117,0.000029785935,0.000054218002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034030807,0.000050213275,0.00003887358,0.00002842183,0.00010804532,0.000041807278,0.00031951975,0.000019394813,0.00013134908],"category_scores_gemma":[0.0000038619546,0.000043783144,0.000028884651,0.00026059573,0.00001916083,0.00008891429,0.000042607186,0.000040950956,0.00037555586],"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":[3.474306e-7,0.0005168934,0.0011956495,0.000010280305,0.000008876331,0.000014756941,0.00014311868,0.002289529,0.0004318414,0.12623926,0.009319408,0.85983],"study_design_scores_gemma":[0.0000895654,0.00002406183,0.0012465604,0.0000016963278,0.0000010339063,0.0000043702107,0.0000023346702,0.9395736,0.0001789173,0.0005382535,0.058267925,0.000071723494],"about_ca_topic_score_codex":0.000008324987,"about_ca_topic_score_gemma":0.0000012502616,"teacher_disagreement_score":0.93728405,"about_ca_system_score_codex":0.000009733461,"about_ca_system_score_gemma":0.0000090624735,"threshold_uncertainty_score":0.48271358},"labels":[],"label_agreement":null},{"id":"W2132799405","doi":"10.1109/tcsi.2002.804545","title":"Design of piecewise maps for chaotic spread-spectrum communications using genetic programming","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Chaotic; Computer science; Piecewise; Genetic programming; Channel (broadcasting); Fitness function; Algorithm; Piecewise linear function; Spread spectrum; Genetic algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.05710074303601036,"score_gpt":0.265973811381937,"score_spread":0.20887306834592662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132799405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011060464,0.0018488147,0.9945671,0.00016994012,0.00007830503,0.0019315067,0.00010914596,0.00008561635,0.00010351941],"genre_scores_gemma":[0.96875924,0.00041855863,0.028864097,0.00003631672,0.000038930513,0.0016606634,0.0000049509167,0.000017796629,0.00019946971],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869066,0.00015488267,0.0003977887,0.00039205662,0.00013379176,0.00023082152],"domain_scores_gemma":[0.99840945,0.00052463234,0.00016003486,0.0007238075,0.000060179817,0.00012188797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037658043,0.00017638385,0.00021795488,0.0001401885,0.00096306734,0.00012553875,0.0004179945,0.000074231815,0.000006527079],"category_scores_gemma":[0.0000019868971,0.00017754677,0.00006376819,0.00034315474,0.00026096223,0.0002104313,0.000010117821,0.00012098024,0.000005244596],"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":[0.00001127415,0.0012726993,0.0000219431,0.00021602529,0.00015177621,5.456178e-7,0.0014620718,0.015418307,0.0068141725,0.7457863,0.000025845351,0.22881906],"study_design_scores_gemma":[0.0012784544,0.0004798517,0.000103282095,0.00019023777,0.00019976615,0.00027005238,0.0011844167,0.94431263,0.0016539899,0.04271302,0.00693069,0.0006836047],"about_ca_topic_score_codex":0.000017576132,"about_ca_topic_score_gemma":0.0000015035049,"teacher_disagreement_score":0.96765316,"about_ca_system_score_codex":0.00004668061,"about_ca_system_score_gemma":0.0000214558,"threshold_uncertainty_score":0.74072325},"labels":[],"label_agreement":null},{"id":"W2133956363","doi":"10.1007/978-3-540-39869-1_18","title":"Improving Evolutionary Learning of Cooperative Behavior by Including Accountability of Strategy Components","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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 Calgary","funders":"","keywords":"Accountability; Computer science; Evolutionary algorithm; Artificial intelligence; Action (physics); Process (computing); Evolutionary computation; Measure (data warehouse); Machine learning; Data mining","score_opus":0.026676161430804687,"score_gpt":0.2755858887287938,"score_spread":0.2489097272979891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133956363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038192342,0.00065303344,0.99414235,0.00007124006,0.00024847972,0.0004540342,0.000032510146,0.000048010665,0.0005311034],"genre_scores_gemma":[0.80522627,0.000033524182,0.19441259,0.00008114958,0.000060216425,0.000025536103,0.000026339028,0.000019930632,0.00011446362],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969542,0.00006604298,0.00073777145,0.0010510987,0.0008046749,0.00038623594],"domain_scores_gemma":[0.9975777,0.0003600567,0.0006018528,0.00077163306,0.0005883458,0.00010039534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006971341,0.0003603473,0.0005179343,0.0003547841,0.00034124585,0.00008244106,0.0017138273,0.00023049646,0.000019314462],"category_scores_gemma":[0.00006304224,0.0003579715,0.00011144014,0.0006227399,0.0009420969,0.0006373446,0.0008904117,0.00070551934,0.0000033668896],"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":[0.000021237634,0.0009782597,0.0044300994,0.0002988716,0.000062859326,0.000021702468,0.0020053638,0.270831,0.06936593,0.09117073,0.000109902845,0.56070405],"study_design_scores_gemma":[0.00050167594,0.00045705892,0.0025942868,0.0003447621,0.000027377944,0.000054847602,0.00000338,0.9633388,0.008858956,0.022490382,0.00045629422,0.0008721864],"about_ca_topic_score_codex":0.000170332,"about_ca_topic_score_gemma":0.000011594386,"teacher_disagreement_score":0.80140704,"about_ca_system_score_codex":0.00032392112,"about_ca_system_score_gemma":0.00048187329,"threshold_uncertainty_score":0.9998872},"labels":[],"label_agreement":null},{"id":"W2134177608","doi":"10.1109/ccece.2002.1013102","title":"Directing crossover for reduction of bloat in GP","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Genetic programming; Computer science; Reduction (mathematics); Genetic algorithm; Tree (set theory); Code (set theory); Operator (biology); Artificial intelligence; Algorithm; Machine learning; Mathematics; Programming language; Biology; Genetics","score_opus":0.017642199612866522,"score_gpt":0.2710642533116641,"score_spread":0.2534220536987976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134177608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057057846,0.000053772525,0.93185425,0.00032239116,0.00012626316,0.00015426872,8.2881945e-7,0.000035426583,0.010394974],"genre_scores_gemma":[0.7416133,0.0000028193406,0.25731122,0.000013945641,0.000015016804,0.000029137254,3.5973713e-7,0.0000015784236,0.0010126238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996634,0.000007890977,0.0000989738,0.0001131531,0.000043441913,0.00007313679],"domain_scores_gemma":[0.99976444,0.00003330536,0.000028866589,0.00012490057,0.000036267786,0.000012234619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012558034,0.000027226668,0.00004133987,0.000029757379,0.000044372195,0.000010934385,0.00008960826,0.000015386971,0.000007516607],"category_scores_gemma":[0.000022384147,0.000025554731,0.00002015308,0.00021162594,0.000011925229,0.00015329813,0.000012797707,0.000020692709,0.000002241326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011586867,0.000094499715,0.0004197479,0.000005464063,0.0000022601764,7.171137e-8,0.00019205308,0.0004712625,0.007499584,0.9833153,0.00060634164,0.007392253],"study_design_scores_gemma":[0.0023697363,0.00017782049,0.026229234,0.000046978286,0.000008367743,0.000063528714,0.00049701176,0.2679705,0.30491713,0.32189453,0.07525539,0.00056978397],"about_ca_topic_score_codex":0.000024840769,"about_ca_topic_score_gemma":0.000003954761,"teacher_disagreement_score":0.6845555,"about_ca_system_score_codex":0.000014373548,"about_ca_system_score_gemma":0.000020542968,"threshold_uncertainty_score":0.10420916},"labels":[],"label_agreement":null},{"id":"W2134574564","doi":"10.1109/micai.2009.25","title":"Comparison of Two Evolvable Systems in the Automated Analog Circuit Synthesis","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Spice; Analogue electronics; Computer science; Electronic circuit; Genetic algorithm; Representation (politics); Analogue filter; Algorithm; Electronic engineering; Filter (signal processing); Digital filter; Engineering; Electrical engineering; Machine learning","score_opus":0.03734124567761061,"score_gpt":0.3164469993104367,"score_spread":0.2791057536328261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134574564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04788794,0.0009373846,0.8952474,0.00372855,0.00013269513,0.0006381543,0.000006501749,0.00072412216,0.050697245],"genre_scores_gemma":[0.99209803,0.0000029104012,0.007715476,0.00007846542,0.000015353166,0.000026935175,0.0000011815582,0.0000013855141,0.00006023089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919665,0.000069795446,0.00025084673,0.00015797929,0.00018801374,0.00013671657],"domain_scores_gemma":[0.9992603,0.00017300567,0.00007168145,0.00042720544,0.000045494646,0.000022334947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003079476,0.00006154528,0.00014209586,0.00007083522,0.00007373666,0.000049876784,0.00072102883,0.000024183628,0.000007855572],"category_scores_gemma":[0.000013326944,0.000042122763,0.000029695519,0.00066734676,0.000020482143,0.0001601014,0.000023903858,0.000057822115,0.000021840431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.2671413e-7,0.00044448857,0.0035582033,0.0000075422668,0.000006749754,0.0000018078211,0.0004793454,0.0121197915,0.0010218055,0.96867687,0.0075899386,0.006092945],"study_design_scores_gemma":[0.00006973522,0.000027356962,0.044832606,0.0000136908575,0.000003008714,0.00000620253,0.00011833885,0.95085406,0.00024399828,0.0033471351,0.000423916,0.000059962553],"about_ca_topic_score_codex":0.00032714324,"about_ca_topic_score_gemma":0.000013149689,"teacher_disagreement_score":0.9653297,"about_ca_system_score_codex":0.000022722248,"about_ca_system_score_gemma":0.00002991614,"threshold_uncertainty_score":0.17177163},"labels":[],"label_agreement":null},{"id":"W2134922741","doi":"10.1142/s0219720012710023","title":"DETECTION AND DECOMPOSITION: TREATMENT-INDUCED CYCLIC GENE EXPRESSION DISRUPTION IN HIGH-THROUGHPUT TIME-SERIES DATASETS","year":2012,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Gene; Gene expression; Organism; Decomposition; Computational biology; Biology; Expression (computer science); Series (stratigraphy); Computer science; Genetics; Ecology","score_opus":0.012441089639826432,"score_gpt":0.27673103492857876,"score_spread":0.2642899452887523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134922741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.543129,0.00026727232,0.4559117,0.00044182572,0.000118247604,0.000077565324,0.00002759621,0.000009070811,0.000017697901],"genre_scores_gemma":[0.78008157,0.00014966905,0.21953887,0.000053602147,0.00008846976,0.0000045179004,0.000079022466,0.0000020197306,0.0000022417116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931085,0.000040410596,0.00037240345,0.000073559306,0.00007919082,0.00012355881],"domain_scores_gemma":[0.99945134,0.00009045573,0.00025292396,0.00007611411,0.0000510557,0.00007811328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017460078,0.000088000386,0.00014329806,0.00013563054,0.00012769426,0.000041437088,0.00008730485,0.000057346293,0.0000027240694],"category_scores_gemma":[0.000007203024,0.00006796688,0.000023377354,0.00011753537,0.000038955175,0.0010343718,0.000063301326,0.00006522669,0.00000525773],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024003354,0.0015575427,0.016070532,0.00009288177,0.00020343588,0.000013851624,0.0054769577,0.006365132,0.13818067,0.081533246,0.00038733563,0.7498784],"study_design_scores_gemma":[0.0047355006,0.0030265355,0.30808988,0.000109843706,0.000060314123,0.004022891,0.00032392077,0.58740896,0.017165145,0.069657855,0.0046849726,0.0007141551],"about_ca_topic_score_codex":0.0000065014906,"about_ca_topic_score_gemma":0.0000012930543,"teacher_disagreement_score":0.7491642,"about_ca_system_score_codex":0.000044463843,"about_ca_system_score_gemma":0.000026866312,"threshold_uncertainty_score":0.27716088},"labels":[],"label_agreement":null},{"id":"W2135011160","doi":"10.1109/tvlsi.2005.844286","title":"Physical resource binding for a coarse-grain reconfigurable array using evolutionary algorithms","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Genetic algorithm; Kernel (algebra); Evolutionary algorithm; Resource (disambiguation); Algorithm; Computer engineering; Theoretical computer science; Mathematics; Artificial intelligence; Machine learning","score_opus":0.022877395608439217,"score_gpt":0.26841290883269814,"score_spread":0.24553551322425893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135011160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075038155,0.00012041308,0.98728305,0.001036998,0.0012820247,0.0011611853,0.00045712173,0.00043783817,0.00071756693],"genre_scores_gemma":[0.8747435,0.00001939399,0.11624282,0.00020658407,0.0011134713,0.0011109231,0.00010214069,0.000070339156,0.006390781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970275,0.00020567804,0.0006955238,0.0008774531,0.0005557487,0.0006380707],"domain_scores_gemma":[0.9981291,0.00029525877,0.00025837234,0.00077104743,0.00032312982,0.00022305019],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005972529,0.00039095472,0.00042085839,0.0004069642,0.00132713,0.00029704996,0.00063363556,0.00021610208,0.00002862094],"category_scores_gemma":[0.000011879998,0.00038347827,0.0003735818,0.0008965424,0.00008889913,0.0013283016,0.0000037479015,0.00044190272,0.00018006882],"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":[0.00024064687,0.0065129483,0.000019507326,0.0002421343,0.0005043864,0.000010556183,0.008619065,0.57656056,0.21200071,0.039100554,0.024786942,0.131402],"study_design_scores_gemma":[0.00074431405,0.00014799413,0.0000072229377,0.00015930583,0.00003938247,0.000082525876,0.00093639165,0.93482894,0.020125749,0.0002325391,0.042268094,0.00042753323],"about_ca_topic_score_codex":0.00013297249,"about_ca_topic_score_gemma":0.000087089,"teacher_disagreement_score":0.8710402,"about_ca_system_score_codex":0.0006018278,"about_ca_system_score_gemma":0.00019007508,"threshold_uncertainty_score":0.999973},"labels":[],"label_agreement":null},{"id":"W2136859123","doi":"10.1109/dasc.2009.79","title":"Similarity Computation Using Reconfigurable Embedded Hardware","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Field-programmable gate array; Reconfigurable computing; Embedded system; Computer hardware; Multiplexer; Design space exploration; Software; Computation; Flexibility (engineering); Computer architecture; Hardware acceleration; Multiplexing; Algorithm","score_opus":0.03512040881234258,"score_gpt":0.29191302376524425,"score_spread":0.2567926149529017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136859123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058083315,0.000029475472,0.9730529,0.0023682052,0.00007055467,0.00010322395,0.0000014415282,0.0002254641,0.018340375],"genre_scores_gemma":[0.5105288,0.0000031683574,0.4881726,0.0007864244,0.000047804886,0.0000027153364,0.00000694556,0.0000023158673,0.00044924978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999347,0.000019711804,0.00013776222,0.00022884185,0.00011957304,0.00014712814],"domain_scores_gemma":[0.99955916,0.000023104412,0.00004254479,0.0002286223,0.00008806054,0.000058502497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009850226,0.00006993113,0.000074995405,0.00004282253,0.00021447048,0.00009339278,0.00026341973,0.000035037632,0.00003579988],"category_scores_gemma":[0.0000067916203,0.000067683635,0.000034964356,0.00029252918,0.000011745616,0.00046454277,0.000019080562,0.00006603341,0.000043070886],"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":[0.0000039829383,0.00049526093,0.00022724675,0.000009391899,0.000019390001,0.000007813044,0.0005640968,0.046751253,0.012553981,0.6890711,0.02052763,0.22976881],"study_design_scores_gemma":[0.00011866944,0.000027044667,0.0036809717,0.0000047002295,0.000002143102,0.000016519574,0.000017232362,0.9446558,0.0012764485,0.048344433,0.0017428678,0.00011321423],"about_ca_topic_score_codex":0.000019491757,"about_ca_topic_score_gemma":0.0000014608694,"teacher_disagreement_score":0.8979045,"about_ca_system_score_codex":0.00003520773,"about_ca_system_score_gemma":0.00004625274,"threshold_uncertainty_score":0.27600586},"labels":[],"label_agreement":null},{"id":"W2136903583","doi":"10.1109/cec.2007.4424559","title":"Genetic swarm grammar programming: Ecological breeding like a gardener","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Swarm behaviour; Genetic programming; Grammatical evolution; Computer science; Rule-based machine translation; Grammar; Artificial intelligence; Fitness landscape; Swarm robotics; Theoretical computer science; Linguistics","score_opus":0.018087278131396482,"score_gpt":0.2531702290495113,"score_spread":0.23508295091811482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136903583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029867586,0.000067594316,0.9608759,0.0010786047,0.00016172758,0.00020890805,2.2050462e-7,0.0003465298,0.007392926],"genre_scores_gemma":[0.41949326,0.0000072496664,0.57858163,0.0003494934,0.00015291106,0.00003534632,0.0000010634333,0.000004644601,0.0013744095],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887246,0.000011030361,0.00020068833,0.00034216646,0.00019992831,0.0003737112],"domain_scores_gemma":[0.9993657,0.000073150404,0.0000404657,0.00030273673,0.00006865787,0.00014929977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002887787,0.000099828976,0.00008481048,0.00006100486,0.00020051425,0.000097771255,0.0005569592,0.000067042485,0.000040381317],"category_scores_gemma":[0.000011018334,0.00008099193,0.00006063148,0.00036519385,0.000051609164,0.00011696237,0.00021534043,0.00010127677,0.00013709797],"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":[0.0000069848493,0.00094802136,0.0099315345,0.000009232228,0.000032120413,0.00009438656,0.00027208484,0.00033260605,0.00095770246,0.38122958,0.010138303,0.59604746],"study_design_scores_gemma":[0.00091713195,0.00055766216,0.27843097,0.000011552655,0.000017746823,0.00033640303,0.00016139503,0.10120351,0.00068934105,0.013354234,0.6035058,0.00081429636],"about_ca_topic_score_codex":0.000013828962,"about_ca_topic_score_gemma":0.000023893126,"teacher_disagreement_score":0.59523314,"about_ca_system_score_codex":0.000039878763,"about_ca_system_score_gemma":0.00003068082,"threshold_uncertainty_score":0.3302755},"labels":[],"label_agreement":null},{"id":"W2138819322","doi":"10.1109/iwfhr.2002.1030900","title":"Genetic engineering of handwriting representations","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Genetic programming; Computer science; Frame (networking); Artificial intelligence; Feature (linguistics); Character (mathematics); Handwriting; Pattern recognition (psychology); Set (abstract data type); Handwriting recognition; Representation (politics); Fuzzy set; Feature vector; Fuzzy logic; Feature extraction; Mathematics; Programming language","score_opus":0.010652180445997217,"score_gpt":0.23328401935266865,"score_spread":0.22263183890667143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138819322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009164651,0.00006456565,0.9815929,0.00011432631,0.000026285608,0.00003831993,3.220933e-7,0.00004654301,0.008952112],"genre_scores_gemma":[0.51333135,0.000003199642,0.48647714,0.000010016997,0.000006431864,0.000007834026,1.5111537e-7,0.0000011555616,0.00016270705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968415,0.000006320535,0.00009570573,0.00009177224,0.00005805864,0.00006400835],"domain_scores_gemma":[0.9997013,0.00003826511,0.000018472525,0.00018757257,0.000031833195,0.000022532817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043500797,0.000026574973,0.00003298243,0.000029506786,0.00003808422,0.00001193695,0.00012575484,0.000009164964,0.000018686558],"category_scores_gemma":[0.000023653418,0.000026535728,0.000017978467,0.00020892118,0.0000070887754,0.000086550484,0.000021332817,0.000020170844,0.000008085699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.461835e-8,0.000020310526,0.0009118276,0.000002747424,0.0000028620823,4.2122488e-7,0.000047332414,0.004952292,0.0033660582,0.9891507,0.00014785392,0.0013975449],"study_design_scores_gemma":[0.00029670002,0.00002761591,0.07591651,0.000014610899,0.0000054127895,0.000058603946,0.00010017785,0.8669262,0.029939827,0.017735695,0.0087570455,0.00022160319],"about_ca_topic_score_codex":0.000009017488,"about_ca_topic_score_gemma":4.8860744e-7,"teacher_disagreement_score":0.97141504,"about_ca_system_score_codex":0.0000042186343,"about_ca_system_score_gemma":0.000015162021,"threshold_uncertainty_score":0.108209565},"labels":[],"label_agreement":null},{"id":"W2140542500","doi":"10.1007/978-3-642-37192-9_49","title":"On GPU Based Fitness Evaluation with Decoupled Training Partition Cardinality","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Partition (number theory); Genetic programming; Implementation; Evolutionary computation; Computation; Population; Parallel computing; Pareto principle; Programming style; Evolutionary algorithm; Theoretical computer science; Artificial intelligence; Mathematical optimization; Algorithm; Programming language","score_opus":0.038098832401498195,"score_gpt":0.27502090626496234,"score_spread":0.23692207386346414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140542500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00075272267,0.00007942127,0.9940687,0.0012159753,0.0004122731,0.00075715943,0.0000065315044,0.00012763366,0.0025795968],"genre_scores_gemma":[0.6190527,0.000004694684,0.3794234,0.0009289031,0.0002709344,0.00016548915,0.000033934717,0.00002328532,0.000096650314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647164,0.000056087312,0.00035851373,0.0012858163,0.0013843357,0.0004436134],"domain_scores_gemma":[0.9974592,0.00045896127,0.00025447286,0.0011503267,0.0005320017,0.00014505873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012186893,0.00037647688,0.00034404936,0.00039146416,0.0003919018,0.0003601243,0.0013395016,0.00018561532,0.00009052892],"category_scores_gemma":[0.0000580189,0.00031532586,0.000087053784,0.00056593714,0.00040482392,0.00055148924,0.00018725639,0.0004671724,0.000065049],"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":[0.000006074484,0.00004986993,0.000023940582,0.000018049475,0.000009898934,0.000008777723,0.0003011882,0.43575022,0.00003322615,0.056565467,0.000040174167,0.5071931],"study_design_scores_gemma":[0.00031662517,0.00016541615,0.00077651715,0.0002323955,0.000011586927,0.000014525889,1.9904896e-7,0.839809,0.00014364494,0.15793408,0.00023829575,0.0003577338],"about_ca_topic_score_codex":0.000028151242,"about_ca_topic_score_gemma":0.000049378483,"teacher_disagreement_score":0.61829996,"about_ca_system_score_codex":0.0004121429,"about_ca_system_score_gemma":0.0009437741,"threshold_uncertainty_score":0.9999299},"labels":[],"label_agreement":null},{"id":"W2140839263","doi":"10.1145/1143997.1144158","title":"Relaxed genetic programming","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Genetic programming; Generalization; Computer science; Set (abstract data type); Relaxation (psychology); Genetic algorithm; Degree (music); Mathematical optimization; Variation (astronomy); Artificial intelligence; Algorithm; Mathematics; Machine learning; Programming language","score_opus":0.007329911486107012,"score_gpt":0.2155496572390274,"score_spread":0.20821974575292038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140839263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038357608,0.00008330819,0.98202515,0.0011352887,0.000033428274,0.00007308794,1.3637288e-7,0.00021715822,0.012596665],"genre_scores_gemma":[0.23789507,0.0000013289584,0.75909907,0.000054206783,0.00005918788,0.000028832217,0.0000011297635,0.000001922248,0.0028592378],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960226,0.000005220435,0.000077825476,0.0001358585,0.0000738996,0.00010495138],"domain_scores_gemma":[0.99972093,0.000011196196,0.000015150988,0.00021047406,0.000021338945,0.000020913225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000029698402,0.0000350072,0.000027722344,0.000019175475,0.00008587242,0.00004633738,0.00023220528,0.000014385023,0.00001233988],"category_scores_gemma":[0.0000011584035,0.000030564872,0.000018571684,0.00019787901,0.000013326103,0.00009943707,0.00005174117,0.00002550557,0.00011364818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.730561e-8,0.00007044014,0.0010330175,0.0000010939085,0.0000012973043,0.000002404143,0.000011532584,0.00020764214,0.00028651732,0.90282196,0.0048248107,0.090739205],"study_design_scores_gemma":[0.0002722697,0.00004899808,0.21239759,0.0000043377368,0.0000034790603,0.000058059828,0.000011247589,0.253341,0.0008250379,0.11192752,0.42081615,0.00029430696],"about_ca_topic_score_codex":0.00007599175,"about_ca_topic_score_gemma":0.000007510349,"teacher_disagreement_score":0.79089445,"about_ca_system_score_codex":0.000009277276,"about_ca_system_score_gemma":0.000013770854,"threshold_uncertainty_score":0.14607553},"labels":[],"label_agreement":null},{"id":"W2140933216","doi":"10.1109/fuzzy.2008.4630598","title":"Derivation of relational fuzzy classification rules using evolutionary computation","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Evolutionary computation; Fuzzy classification; Data mining; Fuzzy logic; Evolutionary algorithm; Genetic programming; Machine learning; Evolutionary programming; Neuro-fuzzy; Human-based evolutionary computation; Population; Fuzzy set; Fuzzy set operations; Grammatical evolution; Fuzzy rule; Membership function; Fuzzy control system; Interactive evolutionary computation","score_opus":0.07486622579595338,"score_gpt":0.2777606076500732,"score_spread":0.20289438185411984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140933216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1353366,0.000077777746,0.86121935,0.00068809703,0.00006828812,0.00012547978,0.000004886744,0.0001024278,0.002377085],"genre_scores_gemma":[0.5483154,0.00001650041,0.45147085,0.000024595534,0.000041751813,0.00000668207,0.00004914155,0.0000033112574,0.00007177214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989637,0.000039495735,0.0003338699,0.00024298082,0.0003076873,0.00011226068],"domain_scores_gemma":[0.9991767,0.00008491581,0.00018364549,0.00022412834,0.0002885701,0.000042072716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102781225,0.000083721345,0.000091734706,0.00014054375,0.00032435582,0.000009602146,0.00020917828,0.000057770372,0.000013885367],"category_scores_gemma":[0.00001704606,0.00008688992,0.00005062647,0.00046863468,0.000095236384,0.00074017653,0.00006055032,0.00006196838,0.00003363889],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027253427,0.00011424382,0.006118653,0.000006480238,0.000010967798,6.495909e-7,0.00016265473,0.023357028,0.0056677368,0.9606206,0.0009931353,0.0029451638],"study_design_scores_gemma":[0.00012544639,0.000013727545,0.2794306,0.00000541306,0.0000026441598,0.000043696098,0.000013220145,0.69458026,0.00015837837,0.025215136,0.0003317531,0.000079740246],"about_ca_topic_score_codex":0.000031616935,"about_ca_topic_score_gemma":5.665977e-7,"teacher_disagreement_score":0.93540543,"about_ca_system_score_codex":0.00008220356,"about_ca_system_score_gemma":0.00015826266,"threshold_uncertainty_score":0.3543268},"labels":[],"label_agreement":null},{"id":"W2141402868","doi":"10.1109/cec.2006.1688519","title":"On Clustering in Evolutionary Computation","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Local optimum; Evolutionary computation; Overhead (engineering); Evolutionary algorithm; Computer science; Computation; Mathematical optimization; Euclidean distance; Human-based evolutionary computation; Algorithm; Mathematics; Interactive evolutionary computation; Artificial intelligence; Evolutionary programming","score_opus":0.00989473800404137,"score_gpt":0.23887016261822003,"score_spread":0.22897542461417866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141402868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010408437,0.000022447382,0.96372306,0.0012825963,0.000057998066,0.000075399665,5.5093875e-7,0.00011159661,0.024317916],"genre_scores_gemma":[0.8486375,8.445355e-7,0.15072478,0.00013308357,0.000034023065,0.000017149816,0.0000064866667,0.0000021361127,0.0004439559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949306,0.000013245987,0.000117866606,0.00016889881,0.00010371665,0.00010320045],"domain_scores_gemma":[0.9997724,0.000050382096,0.000019736168,0.00012283596,0.000018962604,0.000015657319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053346477,0.000047380276,0.000040741088,0.00008491227,0.00006469601,0.000023209048,0.00015770263,0.000019991518,0.00000902799],"category_scores_gemma":[0.0000022293798,0.000046263922,0.00001617257,0.00028614455,0.000011025655,0.0001893051,0.00005296413,0.00004596676,0.00009013562],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.106378e-7,0.00012364877,0.0004863729,0.0000014726262,5.8193285e-7,0.0000026135342,0.00001401362,0.13113718,0.000087331704,0.8583311,0.004675802,0.0051389774],"study_design_scores_gemma":[0.00012420876,0.000014304959,0.094457045,0.000004253602,1.7594714e-7,0.000004816312,0.0000028185257,0.80529535,0.000018012626,0.099519774,0.000503419,0.000055816465],"about_ca_topic_score_codex":0.00015176341,"about_ca_topic_score_gemma":0.00004131794,"teacher_disagreement_score":0.8382291,"about_ca_system_score_codex":0.000055180946,"about_ca_system_score_gemma":0.000016465372,"threshold_uncertainty_score":0.18865879},"labels":[],"label_agreement":null},{"id":"W2141741329","doi":"10.1145/1102256.1102295","title":"802.11 network intrusion detection using genetic programming","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Computer science; Genetic programming; Intrusion detection system; Computer network; Computer security; Artificial intelligence","score_opus":0.014823818086247418,"score_gpt":0.24482172112387393,"score_spread":0.22999790303762652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141741329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072992,0.00010571061,0.9258783,0.00034177833,0.000106907784,0.00013371311,8.760327e-8,0.00019919527,0.0002423476],"genre_scores_gemma":[0.38679764,0.0000049309742,0.6126332,0.00009215795,0.00037456886,0.000013512299,4.1506365e-7,0.000003461051,0.00008005786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992914,0.000017700397,0.00014101884,0.00022367357,0.00011802507,0.00020813312],"domain_scores_gemma":[0.9995935,0.000015866353,0.000041903277,0.00025215253,0.00004046529,0.000056121156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009281679,0.00006970433,0.000053280055,0.000034401044,0.00029669792,0.000079054196,0.00023729212,0.000036825586,0.000017936118],"category_scores_gemma":[0.0000030664858,0.00006486238,0.00003083684,0.00041406127,0.000017208165,0.00027833227,0.00012953678,0.000055359505,0.000043581025],"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":[4.825298e-7,0.000044988767,0.0006888273,0.0000015415385,0.0000029386422,6.94314e-7,0.00004893779,0.043369822,0.00096248125,0.00839368,0.000113184404,0.94637245],"study_design_scores_gemma":[0.00007147144,0.00001799686,0.006104568,0.000005532513,0.0000030263789,0.000034505796,0.000005819565,0.9566286,0.0006050991,0.0011371223,0.035283368,0.000102894315],"about_ca_topic_score_codex":0.000057477242,"about_ca_topic_score_gemma":0.000065622284,"teacher_disagreement_score":0.9462695,"about_ca_system_score_codex":0.00005436218,"about_ca_system_score_gemma":0.000022616006,"threshold_uncertainty_score":0.26450112},"labels":[],"label_agreement":null},{"id":"W2141962622","doi":"10.1109/icvd.1997.568186","title":"Shake And Bake: a method of mapping code to irregular DSPs","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Code (set theory); Parallel computing; Abstraction; Dead code; Unreachable code; Set (abstract data type); Redundant code; Schedule; Programming language; Code generation; Sequence (biology); Instruction set; Algorithm; Operating system","score_opus":0.034444363275410624,"score_gpt":0.2706422723919154,"score_spread":0.2361979091165048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141962622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013454857,0.00012197801,0.9883025,0.00514532,0.000020480722,0.00009408969,0.0000028208121,0.000050277384,0.00491702],"genre_scores_gemma":[0.035560325,0.000011674223,0.9616529,0.00035658217,0.000018605624,0.00001641726,3.4468545e-7,0.0000024778208,0.0023807213],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946743,0.00001728446,0.00011851023,0.00019684268,0.00009770292,0.00010220737],"domain_scores_gemma":[0.99955136,0.000050495644,0.000024098017,0.00026670189,0.00003831143,0.00006903802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013501634,0.000049769264,0.0000788348,0.000051534535,0.00006215426,0.000021125903,0.00024682886,0.00001967652,0.00006860865],"category_scores_gemma":[0.0000090224785,0.00004489234,0.000019736322,0.00031674156,0.00001570032,0.00011702938,0.00013902986,0.000030349429,0.000028325438],"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":[4.862582e-7,0.000116580806,0.00022425479,0.000015531714,0.00001691986,0.0000021126539,0.0012577799,0.0002990659,0.007904702,0.6713814,0.013530243,0.3052509],"study_design_scores_gemma":[0.00016005088,0.000039530154,0.008114474,0.000012961328,0.0000030815183,0.000037477897,0.00006180532,0.88339883,0.0014925594,0.009354703,0.09717844,0.00014606693],"about_ca_topic_score_codex":0.000016361106,"about_ca_topic_score_gemma":0.000003829267,"teacher_disagreement_score":0.8830998,"about_ca_system_score_codex":0.0000065216395,"about_ca_system_score_gemma":0.000005105119,"threshold_uncertainty_score":0.18306564},"labels":[],"label_agreement":null},{"id":"W2144425309","doi":"10.1109/tai.2003.1250213","title":"Experiments in automatic programming for general purposes","year":2004,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Crossover; Computer science; USable; Genetic programming; clone (Java method); Software engineering; Software; Selection (genetic algorithm); Set (abstract data type); Variety (cybernetics); Automatic programming; Usability; Programming language; Search-based software engineering; Software development; Artificial intelligence; Software construction; Human–computer interaction; World Wide Web","score_opus":0.0242388217915714,"score_gpt":0.300554049728991,"score_spread":0.27631522793741964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144425309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06212639,0.00005406304,0.93585587,0.0010375126,0.000046813107,0.00037409933,3.9472374e-7,0.00013471371,0.00037012945],"genre_scores_gemma":[0.1764335,0.000001055533,0.8227882,0.00009822879,0.00002443418,0.00044374613,0.0000018548499,0.0000024503515,0.0002065591],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995372,0.0000036728866,0.0001066877,0.00014904999,0.00006510045,0.00013825622],"domain_scores_gemma":[0.999774,0.000011091669,0.00001786482,0.00015190174,0.00001591018,0.00002928034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051726238,0.0000458015,0.000048032733,0.000037661866,0.00006259032,0.0000453598,0.0002328216,0.000015761194,0.00000361959],"category_scores_gemma":[0.000003954481,0.000040676765,0.000022512168,0.00016668343,0.000010764508,0.00020800905,0.00004663003,0.000019058296,0.0000117410655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.4600967e-7,0.00026380664,0.00013982083,0.0000067467004,0.000003646153,0.0000016673611,0.00036958753,0.0006409094,0.0008220954,0.8920517,0.0001310095,0.10556864],"study_design_scores_gemma":[0.00227687,0.0002151935,0.010712661,0.0000392934,0.0000033096342,0.000029445175,0.0001545409,0.78750086,0.016251778,0.16245572,0.019912757,0.00044755547],"about_ca_topic_score_codex":0.000046113066,"about_ca_topic_score_gemma":0.000008928187,"teacher_disagreement_score":0.78686,"about_ca_system_score_codex":0.0000426219,"about_ca_system_score_gemma":0.000037519945,"threshold_uncertainty_score":0.16587503},"labels":[],"label_agreement":null},{"id":"W2145173052","doi":"10.1007/978-3-540-24840-8_11","title":"On Customizing Evolutionary Learning of Agent Behavior","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"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 Calgary","funders":"","keywords":"Fitness function; Computer science; Fitness approximation; Artificial intelligence; Evolutionary algorithm; Function (biology); Representation (politics); Machine learning; Encoding (memory); Feature (linguistics); Genetic algorithm","score_opus":0.01602664295265665,"score_gpt":0.2507713468589779,"score_spread":0.23474470390632124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145173052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024343022,0.00043034015,0.99523675,0.0003611777,0.0006083516,0.0003393737,0.000005084135,0.00012040676,0.0026550803],"genre_scores_gemma":[0.3986452,0.000055077642,0.6002045,0.00031858985,0.0002618359,0.000038358135,0.000011002311,0.00003050415,0.0004348784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971384,0.00002327939,0.00047374156,0.0010675051,0.00088155386,0.00041554854],"domain_scores_gemma":[0.9981483,0.0002970685,0.00031610887,0.0008997464,0.00021591685,0.00012285133],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039067998,0.00034828734,0.00035760592,0.00066141743,0.00034287033,0.000095835705,0.001957243,0.00021383923,0.000033227145],"category_scores_gemma":[0.00004654911,0.00033935855,0.00014667076,0.0005437259,0.0005303222,0.0003247468,0.0007628285,0.000805857,0.00005447418],"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":[0.0000027413053,0.000099508165,0.000050511127,0.000018462288,0.000006013586,0.000034790388,0.0002467142,0.38291854,0.00011632133,0.3704074,0.000013878826,0.2460851],"study_design_scores_gemma":[0.0004656676,0.0004225113,0.001795336,0.00070337974,0.000017660623,0.00008890073,3.296147e-7,0.5765206,0.0006108467,0.417572,0.0010092274,0.0007935261],"about_ca_topic_score_codex":0.000025284304,"about_ca_topic_score_gemma":0.0000037053849,"teacher_disagreement_score":0.39840177,"about_ca_system_score_codex":0.0005345981,"about_ca_system_score_gemma":0.0006602997,"threshold_uncertainty_score":0.9999058},"labels":[],"label_agreement":null},{"id":"W2145471143","doi":"10.19030/jber.v8i11.52","title":"Testing The Adaptive Efficiency Of U.S. Stock Markets: A Genetic Programming Approach","year":2010,"lang":"en","type":"article","venue":"Journal of Business & Economics Research (JBER)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Thompson Rivers University","funders":"","keywords":"Stock (firearms); Econometrics; Economics; Stock market; Equity (law); Financial economics","score_opus":0.07439495976308935,"score_gpt":0.3035433398305489,"score_spread":0.22914838006745952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145471143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5716082,0.00038242174,0.4232328,0.002334514,0.00026211925,0.00053986337,0.0000047811313,0.000016808948,0.0016184839],"genre_scores_gemma":[0.6643953,0.00007093289,0.3351398,0.000013960573,0.00029270526,0.00003151192,4.8745966e-7,0.000011845908,0.00004349137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982703,0.00012204709,0.0006073245,0.00024990074,0.00035867208,0.0003917731],"domain_scores_gemma":[0.9963375,0.00070834765,0.0004956775,0.0005400492,0.0017907518,0.00012771376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029198758,0.000115812676,0.00022660929,0.00028730123,0.000385915,0.0002158072,0.0018418117,0.00006655208,0.000006106322],"category_scores_gemma":[0.00047575406,0.00008441921,0.00008474076,0.001146758,0.00037079793,0.00049271417,0.00042411083,0.00073676836,0.000005398219],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011653397,0.0021999888,0.0060187844,0.00014293611,0.00018849954,0.000030279167,0.001618536,0.028704047,0.009847744,0.088855095,0.0013335176,0.86094403],"study_design_scores_gemma":[0.00058085745,0.00022926992,0.20432523,0.00006156679,0.00001422102,0.00064293086,0.0002867306,0.7743948,0.00022294633,0.009719453,0.009294524,0.00022744732],"about_ca_topic_score_codex":0.000055222976,"about_ca_topic_score_gemma":0.0000070218034,"teacher_disagreement_score":0.8607166,"about_ca_system_score_codex":0.000076292665,"about_ca_system_score_gemma":0.0008206972,"threshold_uncertainty_score":0.34425157},"labels":[],"label_agreement":null},{"id":"W2147822566","doi":"10.1109/ares.2008.50","title":"Adaptabilty of a GP Based IDS on Wireless Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Research Nova Scotia; Dalhousie University","keywords":"Computer science; Intrusion detection system; Wireless; Preprocessor; Wireless intrusion prevention system; Detector; Computer network; Machine learning; Wireless network; Artificial intelligence; Key distribution in wireless sensor networks; Telecommunications","score_opus":0.021671949989626734,"score_gpt":0.2287732865955943,"score_spread":0.20710133660596755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147822566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020030417,0.000022068747,0.9739573,0.0007508736,0.000048367914,0.00007197144,0.000001121315,0.00008098155,0.005036914],"genre_scores_gemma":[0.9352553,0.000008724704,0.06400195,0.0002921352,0.000035760582,0.00001670761,0.000001879185,0.0000027249503,0.00038484833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945,0.000015387714,0.00011929504,0.00016695177,0.0001382651,0.000110091394],"domain_scores_gemma":[0.99942756,0.00006553754,0.000035679666,0.00037620077,0.000049121736,0.00004589858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060008428,0.000056463286,0.00007491305,0.000030594663,0.00010050415,0.0000058271544,0.00034680657,0.000029812347,0.00003014931],"category_scores_gemma":[0.0000025468598,0.000047281355,0.000041357693,0.00029530903,0.000048632413,0.0000858416,0.000045859328,0.000056797435,0.00001664004],"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":[0.000007655555,0.0006402691,0.0031767513,0.0000052985392,0.000011225585,0.0000072009225,0.000099316756,0.056578062,0.0003615417,0.89729476,0.013872565,0.027945384],"study_design_scores_gemma":[0.00014561243,0.000062177365,0.015496707,0.0000049255104,8.210322e-7,0.000003825157,0.0000026167288,0.9811213,0.0006073012,0.00046434542,0.0020209253,0.00006944392],"about_ca_topic_score_codex":0.000034865043,"about_ca_topic_score_gemma":0.000003269157,"teacher_disagreement_score":0.92454326,"about_ca_system_score_codex":0.000012413391,"about_ca_system_score_gemma":0.000053251086,"threshold_uncertainty_score":0.19280776},"labels":[],"label_agreement":null},{"id":"W2148538884","doi":"10.1109/ccece.2002.1013046","title":"Crossover context in page-based linear genetic programming","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Crossover; Genetic programming; Computer science; Context (archaeology); Fitness landscape; Relation (database); Tree (set theory); Artificial intelligence; Theoretical computer science; Data mining; Mathematics; Biology","score_opus":0.01627690065495219,"score_gpt":0.25371569650546333,"score_spread":0.23743879585051114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148538884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013944906,0.00017582976,0.98155785,0.0005532461,0.000051115443,0.0001953141,5.2754194e-7,0.00009277508,0.0034284608],"genre_scores_gemma":[0.6211622,0.0000014206553,0.3780256,0.00033208475,0.000010440406,0.000044391778,6.4185133e-7,0.0000027875435,0.0004204776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993473,0.000024861789,0.00013509567,0.00021426775,0.00010061409,0.00017781326],"domain_scores_gemma":[0.99958044,0.000039187933,0.000023571954,0.00027374606,0.000034928977,0.000048097074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009939742,0.000062914056,0.00006003703,0.000041414907,0.000073968724,0.000054991608,0.00023247751,0.00002895546,0.000044100314],"category_scores_gemma":[0.000016018974,0.00005704017,0.000027841506,0.00034631588,0.000029705681,0.00011171846,0.00002508307,0.00006161524,0.00008414896],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023255695,0.00050927553,0.013416687,0.000010361623,0.000005633407,0.000020005704,0.00020191405,0.0031117047,0.00033226918,0.83580244,0.0008073196,0.14578006],"study_design_scores_gemma":[0.0013935555,0.000081401915,0.025458245,0.000015123789,0.000002397437,0.000014934302,0.000058599875,0.7707479,0.002202706,0.0079566995,0.19171223,0.00035622227],"about_ca_topic_score_codex":0.00004767965,"about_ca_topic_score_gemma":0.000028904218,"teacher_disagreement_score":0.82784575,"about_ca_system_score_codex":0.00002388791,"about_ca_system_score_gemma":0.000076118566,"threshold_uncertainty_score":0.23260307},"labels":[],"label_agreement":null},{"id":"W2148554518","doi":"10.1007/pl00013273","title":"Automatic mineral identification using genetic programming","year":2001,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Thresholding; Artificial intelligence; Computer science; Mineral resource classification; Identification (biology); Mineral processing; Image processing; Suite; Mineral; Computer vision; Genetic algorithm; Computation; Image (mathematics); Pattern recognition (psychology); Geology; Machine learning; Algorithm; Geography; Materials science; Biology","score_opus":0.013321120281480575,"score_gpt":0.2931154093691397,"score_spread":0.27979428908765913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148554518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055653326,0.00039930834,0.9417441,0.0012396545,0.000028245973,0.00048253196,0.000003919366,0.00024104101,0.00020788131],"genre_scores_gemma":[0.6465726,0.000089963854,0.352412,0.00013832988,0.000104388026,0.0003619102,0.000025217287,0.000011943036,0.0002836774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989748,0.000026543865,0.00028530738,0.00037627853,0.00016069537,0.00017637853],"domain_scores_gemma":[0.9991968,0.000034969842,0.000103805636,0.00049375236,0.00006208206,0.00010856382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012988836,0.00012015923,0.000100033954,0.00011382264,0.0004878629,0.00020052046,0.0003440554,0.000039740044,0.000021425605],"category_scores_gemma":[0.000006160628,0.000109239765,0.000038766888,0.00066941214,0.00005446919,0.0002580519,0.00013471991,0.000078899764,0.000045876764],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.2940015e-7,0.00019620232,0.0012851872,0.000012462628,0.000006779032,0.0000011973914,0.00007938252,0.00029741926,0.0042472812,0.05152727,0.00014142567,0.94220483],"study_design_scores_gemma":[0.00015189975,0.000017794651,0.029773504,0.000007844462,0.000010141142,0.00009500262,0.000014287471,0.91078585,0.000035606517,0.0048544714,0.054109775,0.00014384919],"about_ca_topic_score_codex":0.000044140856,"about_ca_topic_score_gemma":0.0000047568274,"teacher_disagreement_score":0.942061,"about_ca_system_score_codex":0.000025643641,"about_ca_system_score_gemma":0.000023554949,"threshold_uncertainty_score":0.44546685},"labels":[],"label_agreement":null},{"id":"W2150103191","doi":"10.1109/tmag.2006.892105","title":"Evolution of Wire Antennas in Three Dimensions Using a Novel Growth Process","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Antenna (radio); Implementation; Directional antenna; Task (project management); Electronic engineering; Telecommunications; Systems engineering; Engineering","score_opus":0.0211107758710047,"score_gpt":0.2604423649672919,"score_spread":0.23933158909628718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150103191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15801178,0.00006854762,0.84121084,0.00015124536,0.00016090844,0.00020184989,0.000012621757,0.000055199624,0.00012698349],"genre_scores_gemma":[0.8927256,0.000013080856,0.107165694,0.000027284801,0.00001879706,0.000012101468,4.915467e-7,0.000009017791,0.00002793549],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884313,0.000012078678,0.00034046228,0.00028399713,0.00026355835,0.00025676255],"domain_scores_gemma":[0.99922985,0.00007870417,0.00008313624,0.0003372151,0.00019561723,0.000075457356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020853464,0.00012706418,0.00013328067,0.00030978705,0.00016301071,0.00001546084,0.0003264078,0.0000842199,0.000008980822],"category_scores_gemma":[0.0000048547536,0.00013349298,0.000061033705,0.0012500233,0.00009492484,0.00023460768,0.0000035418943,0.00019923961,0.000005664102],"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":[0.00015195002,0.009398692,0.004578436,0.00022724268,0.000085224434,0.000036161804,0.003068727,0.35194367,0.39644352,0.19475031,0.000025142832,0.03929092],"study_design_scores_gemma":[0.0009138443,0.00029931046,0.025119688,0.00010328471,0.000031333962,0.00007147769,0.00018946051,0.9396366,0.018723283,0.014540427,0.000027068209,0.00034420576],"about_ca_topic_score_codex":0.0001500859,"about_ca_topic_score_gemma":0.00026842405,"teacher_disagreement_score":0.7347138,"about_ca_system_score_codex":0.000111173555,"about_ca_system_score_gemma":0.000117599746,"threshold_uncertainty_score":0.54436857},"labels":[],"label_agreement":null},{"id":"W2152075570","doi":"10.1109/cec.2006.1688619","title":"Probabilistic (Genotype) Adaptive Mapping Combinations for Developmental Genetic Programming","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Probabilistic logic; Genetic programming; Benchmark (surveying); Computer science; Population; Genetic algorithm; Genotype; Machine learning; Artificial intelligence; Biology; Genetics","score_opus":0.02058731817041253,"score_gpt":0.22841392896738072,"score_spread":0.20782661079696818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152075570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031540915,0.00004536197,0.99218434,0.0005682033,0.000050295544,0.00071369973,0.0000033960762,0.00018340081,0.0030972054],"genre_scores_gemma":[0.27715605,4.5759882e-7,0.7214179,0.000034119854,0.000034921122,0.0004499842,0.000011917943,0.0000046103937,0.00089001644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992326,0.000009718842,0.00018296539,0.0002607099,0.00011156224,0.00020244357],"domain_scores_gemma":[0.9995898,0.00007016115,0.00004363983,0.00013759715,0.00012312995,0.00003568882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075518394,0.00008287774,0.00006699151,0.00005634176,0.0003305561,0.0000732236,0.00028899155,0.000026517358,0.000006488564],"category_scores_gemma":[0.00000931028,0.00008047813,0.000036384943,0.00031797384,0.000035491117,0.00015224838,0.00007781852,0.00003519858,0.00002903294],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.681075e-7,0.00022819718,0.00015892774,0.0000071352806,0.00000704863,5.643039e-7,0.00009167118,0.0004504746,0.0001509377,0.9603187,0.0010089028,0.03757689],"study_design_scores_gemma":[0.000851816,0.00011783015,0.05608331,0.000018097206,0.000010232463,0.00004034771,0.00016720883,0.6248599,0.00030242137,0.2594819,0.057595428,0.00047148336],"about_ca_topic_score_codex":0.000052315547,"about_ca_topic_score_gemma":0.00002994034,"teacher_disagreement_score":0.7008368,"about_ca_system_score_codex":0.0000856667,"about_ca_system_score_gemma":0.00010267014,"threshold_uncertainty_score":0.32818028},"labels":[],"label_agreement":null},{"id":"W2152445702","doi":"10.1109/crv.2006.32","title":"Evolving a Vision-Based Line-Following Robot Controller","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; OpenGL; Mobile robot; Robot; Controller (irrigation); Evolutionary computation; Artificial intelligence; Simple (philosophy); Computation; Line (geometry); Computer vision; Ubiquitous robot; Genetic programming; Robot control; Human–computer interaction; Computer graphics (images); Visualization; Programming language; Mathematics","score_opus":0.0077125723807177984,"score_gpt":0.2454616681545138,"score_spread":0.23774909577379602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152445702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012396454,0.00017505343,0.9851858,0.0033891483,0.000100018886,0.00012838288,7.274571e-7,0.00027659943,0.0095046],"genre_scores_gemma":[0.73371005,3.6027464e-7,0.26426107,0.0003784916,0.00009319289,0.000029083345,0.0000029425707,0.0000044932567,0.0015203393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913114,0.000018936837,0.0001851358,0.00027191182,0.00019789854,0.00019499715],"domain_scores_gemma":[0.9994244,0.00013154728,0.000038482962,0.0002959328,0.00006103534,0.000048574508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014537411,0.00009022533,0.00010385421,0.000062351144,0.00022515902,0.0001304789,0.0003851439,0.00003557718,0.00003952995],"category_scores_gemma":[0.000011992898,0.00007616709,0.00011082138,0.0003283657,0.000015289568,0.00030749678,0.000063744556,0.000062714535,0.00011686125],"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":[0.00000688046,0.0010741389,0.0036731963,0.000012337752,0.00004649938,0.000036762332,0.00006058304,0.15434247,0.025826983,0.7312214,0.051922157,0.0317766],"study_design_scores_gemma":[0.00048463148,0.00002383253,0.0072299223,0.000008030547,0.0000032306675,0.000001512092,0.000002775799,0.98140454,0.0003803723,0.0066604353,0.0036816762,0.00011902576],"about_ca_topic_score_codex":0.00015240697,"about_ca_topic_score_gemma":0.000011950176,"teacher_disagreement_score":0.8270621,"about_ca_system_score_codex":0.00003124278,"about_ca_system_score_gemma":0.00005478156,"threshold_uncertainty_score":0.3106004},"labels":[],"label_agreement":null},{"id":"W2152792613","doi":"10.1109/icpr.2008.4761644","title":"Non-dominated Sorting Evolution Strategy-based K-means clustering algorithm for accent classification","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Stress (linguistics); Sorting; Computer science; Euclidean distance; Artificial intelligence; Pattern recognition (psychology); Correlation clustering; Algorithm; Data mining; Speech recognition","score_opus":0.10908912907059334,"score_gpt":0.30970280222733837,"score_spread":0.20061367315674503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152792613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07690247,0.0000097379425,0.86906135,0.006158132,0.0021502592,0.0024009862,0.0012828464,0.0008590535,0.041175134],"genre_scores_gemma":[0.96939176,0.00022265359,0.021692755,0.0014866614,0.0013531611,0.0025419847,0.002730319,0.00013200942,0.0004486957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923161,0.00003268631,0.0018381581,0.0025367416,0.0021408228,0.0011354652],"domain_scores_gemma":[0.9895179,0.00016027762,0.0018197312,0.00034062768,0.00767544,0.00048601042],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008301371,0.001141293,0.0007238894,0.0014820159,0.0011388093,0.0015542755,0.0025296728,0.0005383888,0.00091227965],"category_scores_gemma":[0.00017792518,0.0012701927,0.00049978757,0.0007764521,0.0002793652,0.003213678,0.00033489574,0.0010737092,0.0007773898],"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":[0.00049742137,0.0030744022,0.006570774,0.000283138,0.00065425574,0.000030032565,0.001102451,0.000120781224,0.016193915,0.03027021,0.0029979935,0.93820465],"study_design_scores_gemma":[0.0039684246,0.0008667436,0.011240424,0.0011785416,0.000082934355,0.00016033933,0.0012087937,0.9574429,0.0065678754,0.015323569,0.0003433652,0.0016161377],"about_ca_topic_score_codex":0.0001271707,"about_ca_topic_score_gemma":0.000029155164,"teacher_disagreement_score":0.95732206,"about_ca_system_score_codex":0.0012461047,"about_ca_system_score_gemma":0.00042013268,"threshold_uncertainty_score":0.9994822},"labels":[],"label_agreement":null},{"id":"W2154605267","doi":"10.1109/cec.2006.1688362","title":"Distributed Genetic Algorithm with Bi-Coded Chromosomes and a New Evaluation Function for Features Selection","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Feature selection; Fitness function; Feature (linguistics); Computer science; Genetic algorithm; Selection (genetic algorithm); Code (set theory); Algorithm; Binary code; Pattern recognition (psychology); Artificial intelligence; Rate of convergence; Binary number; Population; Function (biology); Mathematics; Machine learning; Key (lock); Biology","score_opus":0.012114020439931227,"score_gpt":0.2381797935404474,"score_spread":0.22606577310051618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154605267","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040846914,0.00028632124,0.9936342,0.0010597295,0.000053896336,0.000587431,0.000011818302,0.00016275446,0.00011916317],"genre_scores_gemma":[0.13528328,0.0000103841,0.8625944,0.000083839484,0.0003447801,0.00031795807,0.00019784822,0.000010470995,0.0011569964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999148,0.000020782896,0.00012565333,0.0003342989,0.00021497735,0.00015625273],"domain_scores_gemma":[0.99949795,0.000038916052,0.000060395752,0.00015273747,0.00019620934,0.000053817465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010528172,0.00010640954,0.00008158002,0.00006183454,0.00024524156,0.000120076846,0.00010235293,0.000048751892,0.000011256463],"category_scores_gemma":[0.0000055543865,0.00008560141,0.000023596991,0.00042002497,0.00002213984,0.0002942936,0.000022887394,0.000044149663,0.0000029070347],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003543114,0.000272121,0.0011101465,0.000019699213,0.00006849243,6.055562e-7,0.00007322995,0.015765153,0.0033384026,0.07453313,0.04531055,0.85947305],"study_design_scores_gemma":[0.0008608148,0.00023753656,0.15457056,0.0000053455033,0.000039834937,0.00004313794,0.000009031328,0.8124264,0.0009263265,0.026971053,0.003759383,0.00015057606],"about_ca_topic_score_codex":0.00028416803,"about_ca_topic_score_gemma":0.000119522985,"teacher_disagreement_score":0.8593225,"about_ca_system_score_codex":0.00005703144,"about_ca_system_score_gemma":0.000117891366,"threshold_uncertainty_score":0.3490724},"labels":[],"label_agreement":null},{"id":"W2154967161","doi":"10.1109/isit.1994.394951","title":"On the determination of catastrophic convolutional encoders","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Encoder; Convolutional code; Computer science; Coding (social sciences); Software; Theoretical computer science; Algorithm; Computational complexity theory; Encoding (memory); Reduction (mathematics); Programming language; Artificial intelligence; Mathematics; Decoding methods; Statistics; Operating system","score_opus":0.021833029522724156,"score_gpt":0.2211207106192203,"score_spread":0.19928768109649617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154967161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004222239,0.000028265831,0.9799312,0.0065263165,0.00004343641,0.00008232368,0.0000038766175,0.00003374604,0.0091286125],"genre_scores_gemma":[0.95915073,0.0000061857263,0.03988112,0.00025454003,0.000016714303,0.000020690533,0.0000015898435,0.0000014514726,0.0006669591],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995639,0.000017841503,0.00009175343,0.00010653456,0.00014915009,0.00007086486],"domain_scores_gemma":[0.9995201,0.0001338905,0.00003824845,0.00024194567,0.00004707433,0.000018741002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007009948,0.00003878011,0.000033772827,0.000029332814,0.000101898506,0.0000115024,0.00032392098,0.000014513183,0.000203567],"category_scores_gemma":[0.00001578094,0.000025744443,0.000025498652,0.00016551938,0.000055042634,0.00010598033,0.000036746686,0.000041923475,0.000101881305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.7387285e-7,0.00004985747,0.000018079942,6.4537346e-7,0.0000012830066,1.6984411e-7,0.000046677047,0.00008449617,0.00016881223,0.9905677,0.0036432508,0.0054188604],"study_design_scores_gemma":[0.000084881176,0.000044959364,0.002186194,0.0000037113855,0.0000013841236,0.000006783768,0.000013906825,0.95989555,0.00058037066,0.03562885,0.0015002261,0.000053150903],"about_ca_topic_score_codex":0.0000073084852,"about_ca_topic_score_gemma":0.0000011832349,"teacher_disagreement_score":0.9598111,"about_ca_system_score_codex":0.000013978463,"about_ca_system_score_gemma":0.000008889075,"threshold_uncertainty_score":0.22289158},"labels":[],"label_agreement":null},{"id":"W2155067110","doi":"10.1109/icma.2005.1626689","title":"Evolutionary swarm intelligence applied to robotics","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Evolutionary robotics; Artificial intelligence; Swarm robotics; Robot; Personality; Reinforcement learning; Process (computing); Computer science; Set (abstract data type); Swarm intelligence; Behavior-based robotics; Evolutionary computation; Computational intelligence; Robotics; Machine learning; Psychology; Particle swarm optimization; Social psychology","score_opus":0.012405001247808368,"score_gpt":0.23525834259802902,"score_spread":0.22285334135022067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155067110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025136728,0.000042347907,0.95470667,0.004276185,0.000090742455,0.00016855447,0.0000019201132,0.0002647658,0.040197473],"genre_scores_gemma":[0.33699074,0.0000021235223,0.66017586,0.00044775664,0.00011704064,0.000049201917,0.000006397691,0.0000044431317,0.0022064368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99908245,0.00000711056,0.00018556823,0.00031726473,0.00019187717,0.00021575754],"domain_scores_gemma":[0.99934155,0.000043421176,0.00002659336,0.0004387222,0.000066552515,0.00008318088],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007112475,0.00009264921,0.000074660915,0.00007280612,0.00016667633,0.00005088421,0.00064250507,0.000033868357,0.000024349974],"category_scores_gemma":[0.000003640369,0.00008819117,0.000032076605,0.00061149517,0.000028105425,0.00014624048,0.00022548116,0.00006281579,0.0009041974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.5619873e-7,0.00007448273,0.000046434187,9.994665e-7,0.000001400379,7.22229e-7,0.000013914274,0.07094842,0.00018241857,0.9121976,0.0117778005,0.0047553373],"study_design_scores_gemma":[0.0001146657,0.000062543695,0.020927977,0.0000073031847,0.000005256191,0.00003256233,0.000047497822,0.4312587,0.0037662615,0.47793728,0.06528276,0.0005571663],"about_ca_topic_score_codex":0.000058908754,"about_ca_topic_score_gemma":0.0000083035775,"teacher_disagreement_score":0.43426034,"about_ca_system_score_codex":0.00005214286,"about_ca_system_score_gemma":0.000045326065,"threshold_uncertainty_score":0.9998737},"labels":[],"label_agreement":null},{"id":"W2155400236","doi":"10.1109/coginf.2002.1039287","title":"System modeling and design using genetic programming","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Genetic programming; Computer science; Nonlinear system; Chaotic; Series (stratigraphy); Algorithm; Noise (video); Path (computing); Code (set theory); Piecewise; Radar; Dynamical systems theory; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.047107555035144834,"score_gpt":0.25365768666078253,"score_spread":0.2065501316256377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155400236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00607802,0.00026214056,0.9929943,0.000025568293,0.000026832091,0.00014134149,5.6697722e-8,0.000110039735,0.00036168395],"genre_scores_gemma":[0.39752042,0.0000019377965,0.6024333,0.000008312074,0.0000070539495,0.0000097406455,3.7517445e-8,0.0000019023204,0.000017292881],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995237,0.000028305658,0.0000914462,0.00017135618,0.000067429726,0.000117749],"domain_scores_gemma":[0.9997381,0.000015446089,0.000016095031,0.0001555504,0.000029480827,0.00004535321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012218698,0.000048287042,0.000045275992,0.000025999721,0.0001673184,0.00007725738,0.000106226915,0.000017741586,8.391046e-7],"category_scores_gemma":[0.0000033464626,0.00004337017,0.000010629476,0.00014715244,0.000009076594,0.00012777245,0.000030238454,0.000025633708,0.0000035957812],"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":[2.535344e-7,0.000029460945,0.00011735082,0.000019479485,0.0000069760363,0.0000038490507,0.00013380314,0.27465594,0.00053947285,0.7011763,0.000010857306,0.023306275],"study_design_scores_gemma":[0.000046376586,0.000007216301,0.000015782585,0.000007244093,0.0000022422273,0.00008204407,0.000055298045,0.9982205,0.00009113739,0.0012812022,0.0001311151,0.00005984882],"about_ca_topic_score_codex":0.00001708621,"about_ca_topic_score_gemma":2.0080486e-7,"teacher_disagreement_score":0.72356457,"about_ca_system_score_codex":0.000021251986,"about_ca_system_score_gemma":0.000031590913,"threshold_uncertainty_score":0.17685843},"labels":[],"label_agreement":null},{"id":"W2155832050","doi":"","title":"Using genetic programming to synthesize monotonic stochastic processes","year":2007,"lang":"en","type":"article","venue":"Computational intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Computer science; Monotonic function; Set (abstract data type); Programming language; Series (stratigraphy); Interpreter; Stochastic process; Theoretical computer science; Process calculus; Stochastic calculus; Genetic algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning","score_opus":0.05219381325565875,"score_gpt":0.3324311843105771,"score_spread":0.28023737105491836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155832050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053962893,0.00026876188,0.993334,0.0002640503,0.000107555854,0.00036767373,0.0000018909093,0.00015471567,0.00010505299],"genre_scores_gemma":[0.47717813,0.0000010894786,0.5226056,0.000113658105,0.000052520205,0.000023798992,0.0000011883475,0.0000060820557,0.0000179663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855417,0.000015461328,0.00033553835,0.000431333,0.00033206536,0.00033140212],"domain_scores_gemma":[0.9986148,0.00049133314,0.000085134445,0.00024734013,0.00039053976,0.00017084854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024164228,0.00013948309,0.000108856155,0.0001585138,0.00026409453,0.00012310674,0.0007073323,0.000037550944,0.000009522638],"category_scores_gemma":[0.00013799389,0.00014646826,0.00003726394,0.0011620689,0.00005874566,0.00023240484,0.00017201784,0.000091676426,0.00013072448],"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":[0.00000269475,0.000078999656,0.000031477004,0.000014074644,0.0000073729802,0.00000545827,0.00029048364,0.86460996,0.00005850394,0.03629244,0.00001901703,0.09858952],"study_design_scores_gemma":[0.000028218985,0.000051493746,0.0018282555,0.000050287123,0.0000056946046,0.0000872467,0.00006653079,0.95248234,0.0006963152,0.04397098,0.0004868583,0.00024576078],"about_ca_topic_score_codex":0.000016420154,"about_ca_topic_score_gemma":0.0000048557613,"teacher_disagreement_score":0.47178185,"about_ca_system_score_codex":0.00009694132,"about_ca_system_score_gemma":0.0002495577,"threshold_uncertainty_score":0.5972802},"labels":[],"label_agreement":null},{"id":"W2155865786","doi":"10.1145/1569901.1570044","title":"Evolving stochastic processes using feature tests and genetic programming","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Computer science; Feature (linguistics); Artificial intelligence; Machine learning","score_opus":0.01439480686996151,"score_gpt":0.26209389104461506,"score_spread":0.24769908417465356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155865786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01534937,0.0010993871,0.9818826,0.0011936498,0.000018596624,0.00016186734,2.9818662e-7,0.0001509324,0.00014333897],"genre_scores_gemma":[0.5046821,0.000002906454,0.49511176,0.000082273014,0.000035714595,0.0000055677983,3.600288e-7,0.0000019589372,0.00007735252],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940616,0.0000058554992,0.00007626953,0.00023989657,0.00010596088,0.0001658826],"domain_scores_gemma":[0.99961257,0.00003346034,0.00003133304,0.00016899526,0.000090158515,0.00006351206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003103256,0.000078274265,0.000061524486,0.000041640953,0.00020646439,0.00016124478,0.00020131453,0.000029205436,0.000001995662],"category_scores_gemma":[0.000029338691,0.00006767071,0.000010891151,0.00039795417,0.000021174155,0.00030772373,0.00005401185,0.000059409525,0.0000024296492],"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":[0.0000028221236,0.0005114997,0.0021994982,0.00012314017,0.000024851448,0.000034402157,0.0013499365,0.008307679,0.009793622,0.050159976,0.0013681924,0.9261244],"study_design_scores_gemma":[0.00017182904,0.00010388437,0.052038874,0.00005742224,0.0000104349165,0.00031000713,0.00003714409,0.9357084,0.00011012964,0.01069225,0.0004833205,0.0002763161],"about_ca_topic_score_codex":0.0000060986877,"about_ca_topic_score_gemma":0.00000318075,"teacher_disagreement_score":0.9274007,"about_ca_system_score_codex":0.000015460535,"about_ca_system_score_gemma":0.000066817294,"threshold_uncertainty_score":0.27595317},"labels":[],"label_agreement":null},{"id":"W2156547919","doi":"10.1109/ccece.2002.1013042","title":"Intelligence in architectures: reconfigurable learning techniques in autonomous agents","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Merge (version control); Evolvable hardware; Field-programmable gate array; Reconfigurable computing; Computer architecture; Genetic algorithm; Decomposition; Functional decomposition; Logic gate; Boolean circuit; Theoretical computer science; Computer engineering; Embedded system; Parallel computing; Algorithm; Machine learning","score_opus":0.01802214600495692,"score_gpt":0.2689420303442119,"score_spread":0.25091988433925494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156547919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010763405,0.00007734335,0.90052843,0.000433511,0.00003520553,0.00021130776,1.9326535e-7,0.00019448569,0.08775611],"genre_scores_gemma":[0.7636879,0.000031154173,0.23462737,0.00014250242,0.000006550302,0.00007896106,7.176564e-7,0.0000042508877,0.0014205829],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991817,0.000068961286,0.00020104725,0.00026810545,0.000077506746,0.00020270543],"domain_scores_gemma":[0.9996352,0.00006232023,0.00003207008,0.00021827544,0.000015912896,0.000036202404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003054734,0.00007574681,0.00008308748,0.00017420264,0.00005607529,0.00003775488,0.00035301832,0.000039752285,0.00007429097],"category_scores_gemma":[0.000044855384,0.00007264719,0.000020187026,0.00056454097,0.000020633595,0.0001128283,0.0000314868,0.00021338598,0.000039060233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015894256,0.00028962325,0.008972396,0.000010783349,0.0000037286009,0.000023323379,0.0013773289,0.016554402,0.0008748004,0.60635036,0.00018499886,0.36535665],"study_design_scores_gemma":[0.00030057688,0.00020464559,0.028590262,0.00012826567,0.0000017540775,0.00011107835,0.00046237363,0.46200114,0.08974155,0.2995345,0.118033364,0.00089047215],"about_ca_topic_score_codex":0.00016909056,"about_ca_topic_score_gemma":0.00010147695,"teacher_disagreement_score":0.7529245,"about_ca_system_score_codex":0.00006260158,"about_ca_system_score_gemma":0.00005851771,"threshold_uncertainty_score":0.29624665},"labels":[],"label_agreement":null},{"id":"W2157880479","doi":"10.1145/2576768.2598303","title":"Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Council for the Arts","keywords":"Computer science; Set (abstract data type); Mel-frequency cepstrum; Genetic algorithm; Population; Cepstrum; Fitness function; Genetic programming; Noise (video); Speech recognition; Artificial intelligence; Programming language; Machine learning; Feature extraction","score_opus":0.04763265770930345,"score_gpt":0.27596942713923117,"score_spread":0.22833676942992773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157880479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02714063,0.0001952183,0.97154874,0.00033783828,0.00007890558,0.00036006494,0.000005246825,0.00019205298,0.00014130243],"genre_scores_gemma":[0.46547288,0.000003737181,0.5343904,0.000030130192,0.00003739549,0.000017562568,0.000008632505,0.000007962879,0.000031294363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841607,0.0001612508,0.00034522952,0.00048172192,0.00031174233,0.00028396162],"domain_scores_gemma":[0.9979733,0.00033471256,0.00015769232,0.0013291292,0.00009584572,0.000109297864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048024693,0.00015453505,0.00019792994,0.00008725986,0.00025286202,0.00007614851,0.0013574889,0.00007414,0.000027359221],"category_scores_gemma":[0.0001079911,0.00014289995,0.000044151457,0.0004254411,0.00012635978,0.00043515544,0.0003887662,0.00008324602,0.00003774689],"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":[0.000013492056,0.0015096264,0.008169133,0.0004002623,0.00036932586,0.000023680806,0.0014730134,0.047051094,0.011193703,0.21811071,0.004543695,0.7071423],"study_design_scores_gemma":[0.00011336746,0.000058101436,0.0032395718,0.000028662154,0.000029982593,0.000053988835,0.00009569129,0.9816925,0.00019956117,0.013909157,0.000401638,0.00017776713],"about_ca_topic_score_codex":0.00014600274,"about_ca_topic_score_gemma":0.000009600075,"teacher_disagreement_score":0.9346414,"about_ca_system_score_codex":0.000043856406,"about_ca_system_score_gemma":0.00019248432,"threshold_uncertainty_score":0.5827291},"labels":[],"label_agreement":null},{"id":"W2159270208","doi":"10.1145/1143997.1144162","title":"Pareto-coevolutionary genetic programming classifier","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Mitacs","keywords":"Classifier (UML); Artificial intelligence; Computer science; Genetic programming; Machine learning; Pareto principle; Pareto optimal; Pattern recognition (psychology); Coevolution; Multi-objective optimization; Mathematics; Mathematical optimization","score_opus":0.010063946008717916,"score_gpt":0.2214311828401011,"score_spread":0.2113672368313832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159270208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041606324,0.0002830948,0.97580606,0.0017949212,0.00009303668,0.00015337803,0.000001237736,0.00036171474,0.017345954],"genre_scores_gemma":[0.41095537,0.0000050891,0.5851825,0.00012452065,0.00017072384,0.000092202135,0.000007948696,0.0000054487555,0.0034562254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9991158,0.000015825994,0.00017128457,0.00028883133,0.00017741333,0.00023083984],"domain_scores_gemma":[0.99946076,0.00002673378,0.00003646992,0.00036565398,0.000059042748,0.000051313244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005674877,0.00008562081,0.000063362866,0.000048551097,0.00021230521,0.000071184,0.00040945236,0.000040222516,0.000033249864],"category_scores_gemma":[0.000002538751,0.00007755244,0.000047053698,0.00035888702,0.000046663165,0.00023260848,0.00011157565,0.000062673746,0.0001848473],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.9812246e-7,0.00019754187,0.0067532174,0.0000037488076,0.0000053588806,0.000007591417,0.00001637399,0.000521609,0.00030352944,0.9097678,0.024473835,0.057948902],"study_design_scores_gemma":[0.0002269571,0.000041231,0.36588752,0.0000057126867,0.000004994024,0.00007843407,0.000017477076,0.23012146,0.00018556176,0.07897652,0.32414168,0.0003124495],"about_ca_topic_score_codex":0.000108214605,"about_ca_topic_score_gemma":0.000017753775,"teacher_disagreement_score":0.8307913,"about_ca_system_score_codex":0.000037408117,"about_ca_system_score_gemma":0.000051331564,"threshold_uncertainty_score":0.31624967},"labels":[],"label_agreement":null},{"id":"W2160149488","doi":"10.1109/icnn.1996.549071","title":"Neurosolver solves blocks world problems","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Construct (python library); Problem solver; Task (project management); Solver; Process (computing); Artificial intelligence; Neuromorphic engineering; Control (management); Space (punctuation); State (computer science); Theoretical computer science; Programming language; Software engineering; Artificial neural network; Engineering","score_opus":0.04199792720959844,"score_gpt":0.24971038111181648,"score_spread":0.20771245390221804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160149488","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27645886,0.001382964,0.0814916,0.10663903,0.0063281977,0.0032168205,0.00009367187,0.0021965685,0.5221923],"genre_scores_gemma":[0.98856217,0.000270605,0.0046237796,0.0007666684,0.00035639966,0.00010261983,0.000008131122,0.000019234072,0.005290377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977112,0.000008257874,0.0005377745,0.000691267,0.0006338934,0.0004176293],"domain_scores_gemma":[0.9983973,0.00008209954,0.00037639777,0.00022404388,0.0007582916,0.0001618774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001534224,0.00029782546,0.00025672707,0.00026194545,0.00019447142,0.00034666373,0.001817372,0.00008739052,0.00043040942],"category_scores_gemma":[0.00003608282,0.0002790727,0.000144391,0.00064651057,0.00014676705,0.0009646604,0.00036779468,0.00045462383,0.000031034106],"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":[0.000030237261,0.00069354934,0.0054735336,0.000028435203,0.000082807615,0.0000066523357,0.00034331848,0.008145582,0.0038952357,0.9134296,0.04461654,0.023254469],"study_design_scores_gemma":[0.00033965477,0.00015405151,0.0012562319,0.000094681614,0.000007941296,0.000027626073,0.000021733447,0.98451215,0.00056963676,0.007475814,0.005246484,0.0002939744],"about_ca_topic_score_codex":0.000013012786,"about_ca_topic_score_gemma":0.0000045314664,"teacher_disagreement_score":0.9763666,"about_ca_system_score_codex":0.000050349976,"about_ca_system_score_gemma":0.000015915572,"threshold_uncertainty_score":0.99996614},"labels":[],"label_agreement":null},{"id":"W2160726062","doi":"10.1145/1389095.1389330","title":"Genetic programming with polymorphic types and higher-order functions","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Genetic programming; Computer science; Order (exchange); Artificial intelligence; Business","score_opus":0.013618625809282529,"score_gpt":0.2054709236671315,"score_spread":0.19185229785784896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160726062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036240768,0.00044652986,0.9558622,0.0026694331,0.00004847513,0.00013369249,6.6040354e-7,0.0002178558,0.0043803467],"genre_scores_gemma":[0.5156401,0.000023627046,0.4777528,0.00013363332,0.000053981606,0.00004826066,0.000001132949,0.00000445464,0.006342037],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995338,0.000006628551,0.00006303156,0.00019036343,0.00008810794,0.00011810766],"domain_scores_gemma":[0.9996647,0.000015413349,0.000017242712,0.00019769656,0.000048565842,0.000056329358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000017158809,0.000059083497,0.000046434692,0.000032063097,0.00027305205,0.000029681683,0.000119445,0.000018482846,0.000032306452],"category_scores_gemma":[0.0000010791083,0.000043888325,0.000009035241,0.00033419812,0.00007590651,0.00014342417,0.00005017131,0.000042058746,0.000039767143],"study_design_candidate":"observational","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":[0.000010741199,0.0007553202,0.07410029,0.000023691986,0.00012967712,0.00007981251,0.0008154387,0.00080532517,0.00065215747,0.4973505,0.009666003,0.41561103],"study_design_scores_gemma":[0.00055450207,0.00029603307,0.81107795,0.000009872065,0.000017321498,0.00083848066,0.000036899593,0.02907918,0.000066221786,0.0019493301,0.15565704,0.00041716872],"about_ca_topic_score_codex":0.000056900422,"about_ca_topic_score_gemma":0.000010318488,"teacher_disagreement_score":0.73697764,"about_ca_system_score_codex":0.0000062010995,"about_ca_system_score_gemma":0.000040402014,"threshold_uncertainty_score":0.21001233},"labels":[],"label_agreement":null},{"id":"W2161122238","doi":"10.1142/s021821300600262x","title":"GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS: PRINCIPLES AND CASE-STUDY","year":2006,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Evolutionary computation; Software engineering; Software; Genetic programming; Evolutionary algorithm; Software development; Evolutionary programming; Computation; Genetic representation; Theoretical computer science; Artificial intelligence; Programming language","score_opus":0.08307800819345276,"score_gpt":0.32984414211450447,"score_spread":0.2467661339210517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161122238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42166987,0.00013433036,0.57700574,0.0006385241,0.00032159325,0.0001356945,0.000007719918,0.000021378482,0.00006513854],"genre_scores_gemma":[0.8988336,0.000019938543,0.100715324,0.000053201587,0.00033840927,0.000011647084,0.000005247567,0.00000598178,0.000016666963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980414,0.00008842477,0.00093478814,0.00025412053,0.00051504606,0.00016622426],"domain_scores_gemma":[0.998424,0.0003473888,0.0003879959,0.00013860929,0.00064048293,0.00006154866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000513339,0.00013275162,0.00017764712,0.0003233637,0.00012509398,0.00034906622,0.0006045225,0.000051081086,0.000012352039],"category_scores_gemma":[0.00016505316,0.00012864672,0.000071350936,0.00035099196,0.00007942957,0.0014064664,0.00018740144,0.00020677982,0.000010789693],"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":[0.000055711607,0.0022469338,0.020488864,0.0000069225,0.00008220722,0.003976652,0.0014275495,0.18601972,0.00056864735,0.10312545,0.00029195525,0.6817094],"study_design_scores_gemma":[0.0004075681,0.0006209554,0.14066568,0.0001069973,0.00002922599,0.013629373,0.0037860356,0.6795356,0.0014573924,0.15761472,0.0015059657,0.000640453],"about_ca_topic_score_codex":0.00040025025,"about_ca_topic_score_gemma":0.00021452176,"teacher_disagreement_score":0.68106896,"about_ca_system_score_codex":0.00016321747,"about_ca_system_score_gemma":0.00013260297,"threshold_uncertainty_score":0.5246061},"labels":[],"label_agreement":null},{"id":"W2161148706","doi":"10.1109/tcsi.2005.846216","title":"A hybrid evolutionary programming method for circuit optimization","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Evolutionary programming; Computer science; Evolutionary computation; Genetic programming; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.02066765419552845,"score_gpt":0.26997851228860686,"score_spread":0.2493108580930784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161148706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024134095,0.00070853235,0.9960265,0.00034809177,0.00012065218,0.0017554569,0.00016396621,0.00021262711,0.0004228057],"genre_scores_gemma":[0.8939741,0.00014024405,0.09922332,0.00023321745,0.0002338555,0.0051830923,0.00003167265,0.000026844766,0.0009536449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984607,0.00012818005,0.000366306,0.00059798243,0.0001726312,0.00027420785],"domain_scores_gemma":[0.9987912,0.00045042153,0.00012027094,0.0003732809,0.00009243204,0.00017240105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060781394,0.00020828216,0.00020224103,0.0001498587,0.0012285492,0.0002291096,0.0002484759,0.00007507975,0.000011831178],"category_scores_gemma":[0.0000030480958,0.0002094233,0.00008353575,0.0002490013,0.00010745638,0.0004868497,0.0000059907784,0.00014031972,0.00001099747],"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":[0.00000685895,0.0002971249,0.0000024242408,0.000044189106,0.000045610315,2.2304923e-7,0.0001958124,0.038691476,0.00038441544,0.5889934,0.00004982011,0.3712887],"study_design_scores_gemma":[0.0011674812,0.00023550443,0.000050047933,0.00007063375,0.000095658346,0.0003884658,0.00056770816,0.8845422,0.00071625767,0.030148989,0.08139352,0.000623501],"about_ca_topic_score_codex":0.000009942352,"about_ca_topic_score_gemma":0.0000012309245,"teacher_disagreement_score":0.8968032,"about_ca_system_score_codex":0.00009080466,"about_ca_system_score_gemma":0.00004702589,"threshold_uncertainty_score":0.9449131},"labels":[],"label_agreement":null},{"id":"W2161213945","doi":"10.1109/tciaig.2011.2138707","title":"Search-Based Procedural Generation of Maze-Like Levels","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Intelligence and AI in Games","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"California Institute of Technology","keywords":"Fitness function; Initialization; Computer science; Fitness approximation; Artificial intelligence; Path (computing); Representation (politics); Grid; Genetic programming; Function (biology); Evolutionary algorithm; Machine learning; Genetic algorithm; Mathematics","score_opus":0.09710456400680863,"score_gpt":0.30396370292417885,"score_spread":0.20685913891737023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161213945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026101064,0.00003985701,0.9727761,0.0006841881,0.00011959331,0.00016587239,0.000013814986,0.000038698585,0.00006079903],"genre_scores_gemma":[0.893556,0.000015043135,0.10588145,0.00043686232,0.000014626291,0.000042579104,0.0000034594848,0.0000050648373,0.000044932105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990126,0.000040115257,0.00029549244,0.00028901952,0.0002201296,0.00014266609],"domain_scores_gemma":[0.9994794,0.000100624384,0.000050535495,0.00015123948,0.0001621061,0.00005605627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013048471,0.00011121303,0.000113072434,0.00023036441,0.00008061509,0.000029984763,0.00023670416,0.00004820467,0.000039507664],"category_scores_gemma":[0.0000020099872,0.00010993982,0.00004809408,0.00043259768,0.00013018357,0.00033969196,0.000002937909,0.00014574126,0.000014753932],"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":[0.000015269728,0.00059110194,0.0001025568,0.000028097556,0.000017550834,0.0000029627636,0.0018695679,0.8675519,0.00066958583,0.03428382,0.00005740728,0.094810136],"study_design_scores_gemma":[0.00010593546,0.00013193442,0.0030801636,0.00003175409,0.000003766839,0.000009078007,0.00006488191,0.9493121,0.03593069,0.011157611,0.000044030305,0.00012805499],"about_ca_topic_score_codex":0.00006106959,"about_ca_topic_score_gemma":0.00001847248,"teacher_disagreement_score":0.86745495,"about_ca_system_score_codex":0.000027096834,"about_ca_system_score_gemma":0.00013528431,"threshold_uncertainty_score":0.44832158},"labels":[],"label_agreement":null},{"id":"W2161383373","doi":"10.1109/nafips.2004.1336316","title":"Scheduling exploration/exploitation levels in genetically-generated fuzzy knowledge bases","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Crossover; Computer science; Scheduling (production processes); Fuzzy set; Fuzzy logic; Genetic algorithm; Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.027515157255777075,"score_gpt":0.2713248296591385,"score_spread":0.24380967240336143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161383373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29473156,0.0013037145,0.6908789,0.0056201937,0.0009119651,0.0010235563,0.00007897489,0.00044564964,0.0050054532],"genre_scores_gemma":[0.88059497,0.000021883998,0.11869794,0.00030661788,0.00016191519,0.00010951568,0.000025182924,0.000015561585,0.00006641781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974993,0.00011088513,0.0011258285,0.00030921455,0.0005402842,0.00041449076],"domain_scores_gemma":[0.9974147,0.00011036411,0.00061085337,0.0004957174,0.001264091,0.00010424011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008754788,0.00026491281,0.00025418124,0.00034960138,0.00066318084,0.00026598925,0.0010340025,0.00014195156,0.0000023729256],"category_scores_gemma":[0.0004026173,0.00023033154,0.00009212261,0.002111642,0.00014399798,0.005299616,0.0001503872,0.0002738064,0.00006525223],"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":[0.000020176089,0.00045487093,0.0005264279,0.00033585925,0.000031582906,0.0000011797779,0.03243994,0.88968635,0.003740157,0.022529438,0.0011295378,0.04910448],"study_design_scores_gemma":[0.0086520035,0.0005860769,0.024113538,0.005359631,0.00014924114,0.00013563315,0.015048885,0.56144196,0.17481951,0.19675641,0.009495976,0.0034411494],"about_ca_topic_score_codex":0.00007708674,"about_ca_topic_score_gemma":0.00003586383,"teacher_disagreement_score":0.5858634,"about_ca_system_score_codex":0.00020407443,"about_ca_system_score_gemma":0.0008767687,"threshold_uncertainty_score":0.9392648},"labels":[],"label_agreement":null},{"id":"W2162320128","doi":"10.1145/1274000.1274004","title":"An abstraction-based genetic programming system","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Genetic programming; Computer science; Programming language; Abstraction; Inductive programming; Recursion (computer science); Answer set programming; Theoretical computer science; Functional reactive programming; Reactive programming; Programming paradigm; Logic programming; Artificial intelligence","score_opus":0.010386384200326533,"score_gpt":0.2586890141189084,"score_spread":0.24830262991858185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162320128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013015231,0.000017291783,0.9835551,0.00015784886,0.00008753111,0.00012092178,2.9898158e-7,0.0004356592,0.0026101163],"genre_scores_gemma":[0.5743466,1.08759e-7,0.42550245,0.00003791258,0.000051219184,0.000012032374,0.0000012772498,0.0000021073272,0.0000463135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993681,0.000007654284,0.00013637249,0.00019588166,0.00013014623,0.0001618968],"domain_scores_gemma":[0.9994407,0.000034601697,0.000035482724,0.00034268692,0.000054174685,0.00009230428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018702952,0.000052873664,0.00004032404,0.000048815953,0.0001539321,0.00007249256,0.0003023692,0.00002771158,0.000007278657],"category_scores_gemma":[0.0000014269456,0.000048096797,0.000022844139,0.0002316473,0.0000141355795,0.00018099346,0.000012996818,0.000043852015,0.000071515584],"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":[0.0000024101823,0.00043986723,0.0032823097,0.000031088377,0.000008878386,0.000027825017,0.000099285564,0.006175843,0.0024714111,0.38583285,0.0002406575,0.60138756],"study_design_scores_gemma":[0.00016533342,0.000075431104,0.11369317,0.000008674699,0.000003359685,0.000038915594,0.00016118941,0.8715868,0.0020205923,0.00026265052,0.011802146,0.00018174839],"about_ca_topic_score_codex":0.0000652941,"about_ca_topic_score_gemma":0.000016262555,"teacher_disagreement_score":0.8654109,"about_ca_system_score_codex":0.000042689815,"about_ca_system_score_gemma":0.00003787957,"threshold_uncertainty_score":0.19613305},"labels":[],"label_agreement":null},{"id":"W2163467402","doi":"10.1145/1830483.1830640","title":"Symbiosis, complexification and simplicity under GP","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Killam Trusts","keywords":"Simplicity; Inheritance (genetic algorithm); Computer science; Metaphor; Complexification; Theoretical computer science; Artificial intelligence; Range (aeronautics); Process (computing); Genetic programming; Programming language; Mathematics; Epistemology; Engineering; Biology; Philosophy","score_opus":0.014664619315504774,"score_gpt":0.252159429944485,"score_spread":0.2374948106289802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163467402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08032789,0.000013167433,0.9069156,0.007608421,0.000068239206,0.00008668868,0.0000016179249,0.00014634524,0.0048319986],"genre_scores_gemma":[0.8765761,0.0000046656974,0.12269054,0.00046381116,0.00003310568,0.000012849261,0.0000030036406,0.0000018816631,0.00021401662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99956954,0.0000066283055,0.00007763786,0.00018827914,0.00007045059,0.00008749615],"domain_scores_gemma":[0.9995338,0.00003838202,0.000021944224,0.00030862904,0.000039705083,0.00005754592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007254271,0.000046856076,0.00004075279,0.000023172017,0.00015735583,0.00006347899,0.00023257834,0.000029727536,0.000031267813],"category_scores_gemma":[0.0000043318064,0.00004124555,0.000012267666,0.00013166548,0.000046311026,0.00019721594,0.00008826277,0.00007856968,0.00004458457],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.836266e-8,0.000027029426,0.0005682659,7.517917e-7,0.0000013677073,3.8223156e-8,0.000016352758,0.0000018074315,0.012368296,0.98172474,0.0007219136,0.0045693466],"study_design_scores_gemma":[0.00018662047,0.000021388638,0.54980433,0.0000016073277,0.0000032835248,0.00003517351,0.000028382503,0.1839705,0.0033187107,0.24550186,0.016917286,0.00021088867],"about_ca_topic_score_codex":0.000040724575,"about_ca_topic_score_gemma":0.000020180185,"teacher_disagreement_score":0.79624826,"about_ca_system_score_codex":0.000004698222,"about_ca_system_score_gemma":0.000013941596,"threshold_uncertainty_score":0.16819446},"labels":[],"label_agreement":null},{"id":"W2163533082","doi":"10.1145/1273496.1273606","title":"On the role of tracking in stationary environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Meta learning (computer science); Computer science; Obstacle; Tracking (education); Artificial intelligence; Task (project management); Transfer of learning; Machine learning; Selection (genetic algorithm); Adaptation (eye); Point (geometry); Term (time); Simple (philosophy); Feature (linguistics); Algorithm; Mathematics; Psychology","score_opus":0.010512190578176511,"score_gpt":0.24037666511955413,"score_spread":0.22986447454137762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163533082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14295816,0.00004642067,0.83645666,0.00089783163,0.00001665236,0.000104258215,9.616525e-7,0.000012797643,0.019506278],"genre_scores_gemma":[0.9839741,0.0000029396567,0.015735032,0.00012868796,0.000005586677,0.0000045974775,6.7047694e-7,0.0000010400704,0.0001473341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99966073,0.000008076419,0.00008984457,0.00007567677,0.00010185806,0.00006382771],"domain_scores_gemma":[0.9996677,0.00015257469,0.000022532267,0.00014211917,0.0000038564117,0.000011190005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018622767,0.00002434635,0.000022761085,0.000028447384,0.000033175245,0.00000443427,0.00019242756,0.000009783204,0.00002395757],"category_scores_gemma":[0.000004787594,0.000016863089,0.000010183467,0.0001219814,0.000015748265,0.00008372365,0.000028936607,0.000034514323,0.000019331854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.277624e-7,0.00007787344,0.0011315002,1.9255125e-7,9.307485e-7,3.457084e-7,0.00014265346,0.00033254916,0.0020266392,0.9662216,0.000040173763,0.030024959],"study_design_scores_gemma":[0.000152202,0.000044477965,0.5610917,0.000007497338,7.0199354e-7,0.0000022253973,0.0003502537,0.085991,0.019860392,0.3253644,0.0070459,0.00008919996],"about_ca_topic_score_codex":0.000013817794,"about_ca_topic_score_gemma":0.000004426551,"teacher_disagreement_score":0.84101593,"about_ca_system_score_codex":0.000013598735,"about_ca_system_score_gemma":0.000006183737,"threshold_uncertainty_score":0.068765685},"labels":[],"label_agreement":null},{"id":"W2164159770","doi":"10.1109/cec.2008.4630965","title":"The geometry of Tartarus fitness cases","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Fitness function; Fitness approximation; Metric (unit); Evolutionary algorithm; Computer science; Set (abstract data type); Mathematical optimization; Space (punctuation); Artificial intelligence; Machine learning; Mathematics; Genetic algorithm; Engineering","score_opus":0.022925050597744075,"score_gpt":0.24640187560226115,"score_spread":0.22347682500451707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164159770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25368413,0.00073011173,0.73288697,0.0036462285,0.00017986717,0.00012699787,0.0000041549656,0.00011520497,0.0086263325],"genre_scores_gemma":[0.97057515,0.00007796836,0.027125133,0.00006376709,0.000022811664,0.000010903092,5.977289e-7,0.0000013426547,0.0021223472],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9996355,0.000008170783,0.00008879507,0.00008611712,0.00010103483,0.00008038758],"domain_scores_gemma":[0.99943393,0.0001679271,0.000026476418,0.00029934815,0.000049241804,0.000023074947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056120105,0.00003135284,0.000038011436,0.000018169463,0.00027615757,0.000008469747,0.00038009742,0.000011267056,0.000015139497],"category_scores_gemma":[0.000014723449,0.000019308105,0.000024740799,0.0002806838,0.000079537655,0.00009991986,0.00007862808,0.000027293518,0.000025086632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012207737,0.0001141781,0.0034849795,0.000002827371,0.000011478218,0.000013924169,0.0001572088,0.0001652825,0.00025861233,0.9576064,0.017074756,0.021109167],"study_design_scores_gemma":[0.00066092465,0.00026306632,0.44407123,0.000013186573,0.000009197201,0.0015013546,0.00027878606,0.18001157,0.016178107,0.03787435,0.31857386,0.0005643696],"about_ca_topic_score_codex":0.000101785605,"about_ca_topic_score_gemma":0.000008807624,"teacher_disagreement_score":0.91973203,"about_ca_system_score_codex":0.0000048487937,"about_ca_system_score_gemma":0.00003210342,"threshold_uncertainty_score":0.21240087},"labels":[],"label_agreement":null},{"id":"W2165666048","doi":"10.1109/snpd.2009.82","title":"Creating Objects Using Genetic Programming Techniques","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Programming language; Java; Executable; Object-oriented programming; Genetic programming; Suite; Scala; Grammatical evolution; Test suite; Compiler; Artificial intelligence; Test case","score_opus":0.017482279157129146,"score_gpt":0.2792884129720104,"score_spread":0.26180613381488127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165666048","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049001207,0.00006961838,0.9861745,0.0002979358,0.000014093639,0.00014273147,1.0041923e-7,0.0004566821,0.007944191],"genre_scores_gemma":[0.28262517,0.0000026884109,0.71705633,0.00015233361,0.000050149396,0.000008254737,3.0480444e-7,0.000001828976,0.000102919876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994312,0.000009544309,0.00011185488,0.00019003922,0.00009923638,0.00015815458],"domain_scores_gemma":[0.99963135,0.000014276947,0.00003358585,0.00023792747,0.000040273597,0.000042587213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059098395,0.00006145264,0.000053333682,0.000043340406,0.0001958627,0.000094988536,0.00027198376,0.000024810173,0.0000044354397],"category_scores_gemma":[0.000006047557,0.000055780878,0.00002548374,0.0002994824,0.000012240634,0.00021321545,0.00004862803,0.000045613397,0.000006248632],"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":[2.497337e-7,0.000087251334,0.00014108919,0.0000023872553,0.000002599797,0.0000046373834,0.00020378387,0.00021865038,0.0076378575,0.09168977,0.0000803598,0.8999314],"study_design_scores_gemma":[0.00012229824,0.00018893894,0.008417902,0.000041023522,0.0000071465074,0.00014525942,0.00007630997,0.931649,0.021619614,0.031526703,0.0058106715,0.0003950886],"about_ca_topic_score_codex":0.000025740317,"about_ca_topic_score_gemma":8.243545e-7,"teacher_disagreement_score":0.9314304,"about_ca_system_score_codex":0.000024711799,"about_ca_system_score_gemma":0.000031773066,"threshold_uncertainty_score":0.22746782},"labels":[],"label_agreement":null},{"id":"W2166145602","doi":"10.3758/bf03202543","title":"Using genetic programming to discover nonlinear variable interactions","year":2003,"lang":"en","type":"article","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":15,"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 Windsor; University of Alberta","funders":"Government of Canada","keywords":"Genetic programming; Computer science; Focus (optics); Genetic representation; Variable (mathematics); Nonlinear programming; Multivariate statistics; Artificial intelligence; Genetic algorithm; Nonlinear system; Machine learning; Theoretical computer science; Mathematics","score_opus":0.20986342437627956,"score_gpt":0.5030617868324138,"score_spread":0.2931983624561342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166145602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051223844,0.000041994263,0.9461134,0.00022722663,0.0007911447,0.001135528,0.000009122155,0.00013181525,0.00032592693],"genre_scores_gemma":[0.008145524,0.00000968711,0.9908583,0.00010562541,0.00011118588,0.00045915123,0.000007852066,0.000029592333,0.00027306358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99586767,0.0010784185,0.00048447435,0.0008822234,0.0007629831,0.0009242588],"domain_scores_gemma":[0.9977329,0.00030353703,0.00009322482,0.0010782297,0.00033634712,0.00045576412],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017837933,0.00023957556,0.0002458635,0.0005263201,0.00075081014,0.0006049708,0.0012638025,0.000069384885,0.000047115107],"category_scores_gemma":[0.0001461363,0.0002464589,0.000105660976,0.0022639716,0.00013181851,0.00072815793,0.00080273586,0.00055336236,0.00006892687],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007295007,0.0013197771,0.003042663,0.00002412058,0.000060750084,0.000030658044,0.0005983868,0.0027946753,0.016170342,0.05112138,0.000644491,0.92418545],"study_design_scores_gemma":[0.001124411,0.00045060442,0.0077783335,0.00020083375,0.000052626932,0.0004914374,0.00040863192,0.29460314,0.009740308,0.0051373923,0.6788405,0.0011717639],"about_ca_topic_score_codex":0.00032669713,"about_ca_topic_score_gemma":0.000005178136,"teacher_disagreement_score":0.9230137,"about_ca_system_score_codex":0.0004083539,"about_ca_system_score_gemma":0.0004187223,"threshold_uncertainty_score":0.99999875},"labels":[],"label_agreement":null},{"id":"W2166306467","doi":"10.1142/s0129626407003046","title":"HOW SOLUTION DENSITY AFFECTS THE FINDING OF SPATIALLY ROBUST SOLUTIONS","year":2007,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Science Council","keywords":"Robustness (evolution); Mathematical optimization; Computer science; Parameter space; Mathematics; Algorithm; Statistics","score_opus":0.031801629663492925,"score_gpt":0.24488169129398402,"score_spread":0.2130800616304911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166306467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027698457,0.00022298643,0.94172436,0.029777141,0.000117165495,0.00017824405,9.0600514e-7,0.00010822242,0.00017250376],"genre_scores_gemma":[0.82931995,0.0000049527093,0.16989598,0.0005739193,0.000118351236,0.00001466776,0.0000036701829,0.0000068220634,0.00006172068],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987817,0.000042659965,0.0001798014,0.00029410343,0.00030322175,0.00039850766],"domain_scores_gemma":[0.99915355,0.00012366776,0.00021426589,0.00035681925,0.00009082774,0.000060855575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006680444,0.00012839361,0.00012292674,0.000089918496,0.00082353683,0.00016702086,0.000619471,0.00005068186,0.0000010749357],"category_scores_gemma":[0.00004143236,0.000102771264,0.00007133455,0.00051509053,0.00017558645,0.00056590105,0.0001644598,0.000173665,0.000005490202],"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":[0.000059816535,0.0008841466,0.0089761885,0.00045228194,0.00017606949,0.000059362024,0.007264017,0.135259,0.3966008,0.150739,0.023409745,0.27611956],"study_design_scores_gemma":[0.000833274,0.00008012259,0.102775864,0.00017244549,0.00005759676,0.00011698304,0.00016768185,0.88116276,0.006771059,0.003810492,0.0033213932,0.0007303403],"about_ca_topic_score_codex":0.00003401586,"about_ca_topic_score_gemma":0.00003941615,"teacher_disagreement_score":0.80162144,"about_ca_system_score_codex":0.00006594215,"about_ca_system_score_gemma":0.00007035117,"threshold_uncertainty_score":0.6334063},"labels":[],"label_agreement":null},{"id":"W2166446567","doi":"10.1093/beheco/ars084","title":"We can study how mechanisms evolve without knowing the rules of chess or the workings of the brain","year":2012,"lang":"en","type":"article","venue":"Behavioral Ecology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Biology; Cognitive science; Psychology","score_opus":0.05184726366457516,"score_gpt":0.30707151496916973,"score_spread":0.2552242513045946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166446567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96507144,0.00007613367,0.009481025,0.023994382,0.00043521967,0.00085541006,0.000009869356,0.000030101679,0.000046414796],"genre_scores_gemma":[0.9944328,0.0000022560218,0.004554647,0.00014434707,0.000054189677,0.00017954242,7.138899e-7,0.000007000048,0.00062448246],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990047,0.00020197993,0.00018758157,0.0001686533,0.00018436338,0.0002526973],"domain_scores_gemma":[0.99885535,0.00020005883,0.0001970469,0.00064833474,0.00006493588,0.000034293225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005433489,0.000103668055,0.00016236457,0.000027721422,0.00031967278,0.000022402239,0.0012678273,0.00005641947,0.00001710982],"category_scores_gemma":[0.000018159573,0.00004309919,0.000073011244,0.00028796194,0.00018776553,0.00012214012,0.00048280755,0.00016015566,0.000003013357],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040641466,0.006912442,0.42077437,0.000030258994,0.0001697654,0.0000054535235,0.06824551,0.00012318543,0.011703268,0.39753923,0.005155291,0.08930059],"study_design_scores_gemma":[0.0005473359,0.00047007573,0.9785993,0.000021279047,0.00012496572,0.00003647132,0.0064507425,0.0019759869,0.0020599612,0.005892858,0.0036075914,0.00021343915],"about_ca_topic_score_codex":0.00013146373,"about_ca_topic_score_gemma":0.00065305276,"teacher_disagreement_score":0.5578249,"about_ca_system_score_codex":0.000035749516,"about_ca_system_score_gemma":0.00009320708,"threshold_uncertainty_score":0.2458697},"labels":[],"label_agreement":null},{"id":"W2166780985","doi":"10.1142/s0218001409007466","title":"SOLUTION OVER-FIT CONTROL IN EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION OF PATTERN CLASSIFICATION SYSTEMS","year":2009,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ministry of Minority Affairs","keywords":"Computer science; Evolutionary algorithm; Artificial intelligence; Multi-objective optimization; Classifier (UML); Feature selection; Memetic algorithm; Optimization problem; Machine learning; Pattern recognition (psychology); Mathematical optimization; Mathematics; Algorithm","score_opus":0.06537619928971737,"score_gpt":0.3104808908962583,"score_spread":0.24510469160654091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166780985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044942815,0.00019165858,0.9518443,0.0020720079,0.0006135725,0.00020195324,0.00004928313,0.000018825811,0.00006562784],"genre_scores_gemma":[0.9938249,0.00024312898,0.0053931423,0.00024001105,0.00025164726,0.000011876203,0.000025312145,0.0000055237056,0.0000044419667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979849,0.00013131772,0.000986021,0.00024684492,0.0004988379,0.00015207748],"domain_scores_gemma":[0.99787223,0.00015441878,0.0007536576,0.00012971547,0.0010145162,0.000075447504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045386082,0.0001378068,0.0002117626,0.00046953475,0.00006814217,0.000106307074,0.00042930437,0.00008958375,0.000027829004],"category_scores_gemma":[0.00008399879,0.00013887158,0.00008964414,0.00027543455,0.000070356604,0.0009000832,0.000034371395,0.00019982546,0.000010805283],"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":[0.000088983,0.0008634112,0.0030572775,0.000012833725,0.00006124425,0.000021769145,0.00065828196,0.045025032,0.0046152514,0.0037115314,0.00005871503,0.9418257],"study_design_scores_gemma":[0.00028560281,0.00020572625,0.031667553,0.00020004013,0.000012948448,0.000099763056,0.0002728098,0.95612395,0.0008074006,0.010137694,0.000031383326,0.00015513948],"about_ca_topic_score_codex":0.00010454424,"about_ca_topic_score_gemma":0.000013490899,"teacher_disagreement_score":0.9488821,"about_ca_system_score_codex":0.00014574092,"about_ca_system_score_gemma":0.000074873846,"threshold_uncertainty_score":0.5663018},"labels":[],"label_agreement":null},{"id":"W2168085447","doi":"10.1007/s10710-007-9027-9","title":"Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings","year":2007,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Probabilistic logic; Genetic programming; Set (abstract data type); Implementation; Encoding (memory); Grammatical evolution; Fitness function; Coevolution; Theoretical computer science; Genetic algorithm; Artificial intelligence; Machine learning; Biology","score_opus":0.010779933300425532,"score_gpt":0.21694746148033825,"score_spread":0.2061675281799127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168085447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14419346,0.0016803837,0.8521841,0.0003692321,0.00010825144,0.0008627494,0.0000016091852,0.00036522505,0.00023496908],"genre_scores_gemma":[0.3014805,0.000021722302,0.6979585,0.000043337306,0.00014956685,0.00016875325,0.0000063577572,0.00002233352,0.00014889328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723434,0.000041251726,0.0005036201,0.00096104457,0.00038798858,0.00087172724],"domain_scores_gemma":[0.9988581,0.00009587838,0.00018251472,0.00042301478,0.00018139873,0.0002590959],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005876799,0.00036227275,0.0002859708,0.00021948865,0.00080065586,0.0003710311,0.0005260019,0.00008297555,0.0000055547835],"category_scores_gemma":[0.0000381255,0.00030758986,0.00005176182,0.00094540545,0.00022573328,0.00024436804,0.00033901466,0.00023920968,0.000012852338],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019271016,0.00017140324,0.010321047,0.000100629,0.000065520675,0.00004548539,0.001899956,0.0009997793,0.0005071326,0.002142067,0.000064441214,0.98366326],"study_design_scores_gemma":[0.0029760113,0.0017909597,0.30390954,0.0008781983,0.00017020252,0.004328077,0.0035228096,0.5676698,0.00061190437,0.011307055,0.0993429,0.0034925493],"about_ca_topic_score_codex":0.00024833437,"about_ca_topic_score_gemma":0.000072760704,"teacher_disagreement_score":0.9801707,"about_ca_system_score_codex":0.000120384815,"about_ca_system_score_gemma":0.00016284066,"threshold_uncertainty_score":0.9999376},"labels":[],"label_agreement":null},{"id":"W2168924884","doi":"10.1109/cec.2006.1688662","title":"Improving Evolution Strategies through Active Covariance Matrix Adaptation","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CMA-ES; Adaptation (eye); Evolution strategy; Covariance matrix; Test suite; Computer science; Suite; Covariance; Mathematical optimization; Matrix (chemical analysis); Mutation; Algorithm; Artificial intelligence; Evolutionary computation; Machine learning; Mathematics; Statistics; Test case; Biology; Genetics; Geography","score_opus":0.012556102580541509,"score_gpt":0.25219422325505947,"score_spread":0.23963812067451795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168924884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014348269,0.00011265858,0.98522043,0.0007197875,0.0001006787,0.0001475835,0.000003930206,0.00027248217,0.011987627],"genre_scores_gemma":[0.5775136,0.0000024755877,0.4216422,0.00002044272,0.000095006966,0.000032896638,0.000008550956,0.0000033135789,0.0006814941],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920225,0.00002143413,0.0001594907,0.00028069035,0.00016320699,0.00017293067],"domain_scores_gemma":[0.9994782,0.00003894585,0.00008743905,0.00026572563,0.00011009253,0.000019577254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006548545,0.000089149944,0.00006783832,0.00003307163,0.0002357863,0.00014449896,0.00028388592,0.00004376052,0.000014537258],"category_scores_gemma":[0.0000046681876,0.00008500381,0.000033844597,0.00036694563,0.00003341517,0.0020909258,0.00006159218,0.00006999759,0.000059344722],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012931009,0.000040782328,0.000015544027,0.0000027400138,0.0000022845043,6.0358e-7,0.0000821862,0.009574801,0.0015784338,0.9849109,0.00038024655,0.003410195],"study_design_scores_gemma":[0.00015955241,0.000023875366,0.0056142355,0.0000035201272,0.000003239335,0.000008593374,0.00043859612,0.7311705,0.0006098717,0.26030856,0.0015258733,0.00013361007],"about_ca_topic_score_codex":0.0024203022,"about_ca_topic_score_gemma":0.00012291646,"teacher_disagreement_score":0.72460234,"about_ca_system_score_codex":0.00010020122,"about_ca_system_score_gemma":0.00012473142,"threshold_uncertainty_score":0.3658789},"labels":[],"label_agreement":null},{"id":"W2169464732","doi":"10.1109/cec.2006.1688330","title":"The Y-Test: Fairly Comparing Experimental Setups with Unequal Effort","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Statistic; Set (abstract data type); Test statistic; Computation; Work (physics); Test (biology); Statistical hypothesis testing; Point (geometry); Algorithm; Mathematics; Statistics; Engineering","score_opus":0.009359679556369516,"score_gpt":0.22770951037233608,"score_spread":0.21834983081596657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169464732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18664381,0.00023209314,0.7651038,0.0019596994,0.00009093247,0.00026222976,0.0000016308138,0.00037775972,0.045328032],"genre_scores_gemma":[0.9658369,9.225277e-7,0.03264519,0.00005292413,0.000063215935,0.000043358006,0.0000027659783,0.000003965808,0.0013507542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934036,0.000006585715,0.00011574965,0.00018609554,0.00017234126,0.00017884438],"domain_scores_gemma":[0.9995183,0.000079641446,0.000032041364,0.00030936743,0.000025857562,0.000034792338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007031186,0.00007620853,0.00005679782,0.000014109639,0.0004803314,0.00013395045,0.00043923804,0.000014497526,0.0000061220253],"category_scores_gemma":[0.0000010200422,0.000045373166,0.000022230928,0.00017838167,0.00006502716,0.00021714013,0.00010829584,0.00005477205,0.000050179802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002037654,0.00019950724,0.010466036,9.3340947e-7,0.0000060313805,0.0000033742183,0.00009062644,0.0011563273,0.00081757864,0.9820373,0.004403322,0.00081694394],"study_design_scores_gemma":[0.0008542414,0.0002951491,0.1093604,0.000011341989,0.000005646053,0.00007542015,0.0003455326,0.82795066,0.017491005,0.01222256,0.030917313,0.0004707224],"about_ca_topic_score_codex":0.00020049179,"about_ca_topic_score_gemma":0.000070214235,"teacher_disagreement_score":0.9698147,"about_ca_system_score_codex":0.000027678501,"about_ca_system_score_gemma":0.000027709759,"threshold_uncertainty_score":0.36943692},"labels":[],"label_agreement":null},{"id":"W2170657595","doi":"10.25088/complexsystems.15.4.285","title":"Repeated Sequences in Linear Genetic Programming Genomes","year":2005,"lang":"en","type":"article","venue":"Complex Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Genetic programming; Genome; Linear programming; Computer science; Computational biology; Evolutionary biology; Biology; Genetics; Artificial intelligence; Algorithm; Gene","score_opus":0.040226336561178175,"score_gpt":0.278419984426947,"score_spread":0.2381936478657688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170657595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10510492,0.009608779,0.8754837,0.0034322387,0.00084664155,0.0015781358,0.00000990312,0.0008506954,0.0030849897],"genre_scores_gemma":[0.82517564,0.00001925143,0.17395738,0.00003914375,0.00023046773,0.00012229496,0.000006235999,0.000005923365,0.00044368536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989162,0.00005580718,0.0003151365,0.00031215316,0.00016261061,0.00023806181],"domain_scores_gemma":[0.99935746,0.00003260512,0.00007866672,0.0004091199,0.00005830045,0.000063864914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014246402,0.00009579366,0.00013617954,0.00009031384,0.00011626411,0.00009932963,0.00057167374,0.000035231755,0.000007809792],"category_scores_gemma":[0.0000056307563,0.00008948852,0.00003095143,0.00047954443,0.000038521368,0.0001848174,0.000095839045,0.00007106747,0.000116098425],"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":[0.0000063531575,0.0008400251,0.05815361,0.00023091851,0.00008009272,0.000112213966,0.0038997673,0.14445108,0.008154783,0.31748107,0.0044039576,0.46218613],"study_design_scores_gemma":[0.00014562233,0.000025809446,0.012912932,0.000023995082,0.0000014491291,0.00006574831,0.00008622094,0.814571,0.000019129631,0.00024585632,0.17175467,0.00014753542],"about_ca_topic_score_codex":0.00025663027,"about_ca_topic_score_gemma":0.000054752716,"teacher_disagreement_score":0.7200707,"about_ca_system_score_codex":0.00007590694,"about_ca_system_score_gemma":0.00004679359,"threshold_uncertainty_score":0.36492363},"labels":[],"label_agreement":null},{"id":"W2182921912","doi":"10.32920/ryerson.14651808.v1","title":"A Study on Financial Time Series Forecasting and Symbolic Regression by means of a Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Symbolic regression; Probabilistic logic; Genetic programming; Computer science; Hidden Markov model; Markov chain; Representation (politics); Statistical model; Stock market; Econometrics; Artificial intelligence; Machine learning; Mathematics; Geography","score_opus":0.060633634333013824,"score_gpt":0.30307290876519427,"score_spread":0.24243927443218044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182921912","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32514697,0.00023025094,0.67334557,0.00019260359,0.000057311467,0.0008759903,0.000012420505,0.000085623666,0.000053238404],"genre_scores_gemma":[0.3784974,0.000010239178,0.62100405,0.000015584095,0.00003645658,0.00033081364,0.000012960914,0.000013583034,0.00007888397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975979,0.00030531053,0.0005064168,0.0009942482,0.0002839461,0.00031221122],"domain_scores_gemma":[0.99847645,0.00023041957,0.00029427398,0.00071992533,0.00018325796,0.00009564836],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005491437,0.00030691086,0.0005463546,0.00012373913,0.00023381537,0.00013006643,0.00057827483,0.000106839005,0.0000024329681],"category_scores_gemma":[0.00022453189,0.00027366105,0.00009122567,0.00024342876,0.00010004322,0.00012955608,0.0013819014,0.00035863067,4.909261e-7],"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":[0.00011440998,0.0063671716,0.003667734,0.0020970097,0.00055394956,0.00041500365,0.02875811,0.3539592,0.0144825345,0.07344043,0.0013506737,0.51479375],"study_design_scores_gemma":[0.00021628491,0.0003408185,0.0007209846,0.00027899773,0.000057469486,0.00012018519,0.00018053723,0.9831994,0.0010418907,0.013478559,0.000018568688,0.00034627793],"about_ca_topic_score_codex":0.00006736723,"about_ca_topic_score_gemma":0.000015771915,"teacher_disagreement_score":0.6292402,"about_ca_system_score_codex":0.000056980578,"about_ca_system_score_gemma":0.0002919675,"threshold_uncertainty_score":0.99997157},"labels":[],"label_agreement":null},{"id":"W2184042660","doi":"","title":"A computationally efficient modelling-to-generate-alternatives method using the firefly algorithm","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Firefly algorithm; Mathematical optimization; Algorithm; Mathematics","score_opus":0.056110026474821,"score_gpt":0.3348455909854567,"score_spread":0.2787355645106357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184042660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055513894,0.00015386847,0.9914746,0.0017213032,0.0002182635,0.00023944712,0.000006670584,0.00010772504,0.0005267145],"genre_scores_gemma":[0.1268758,0.0000024341327,0.8719311,0.00067649456,0.00024252523,0.0000345708,0.0000032161024,0.000007563053,0.00022635207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876285,0.00009514719,0.00021011192,0.0002791152,0.00033372923,0.0003190229],"domain_scores_gemma":[0.99910086,0.00017763744,0.00006495809,0.0003502769,0.00015423218,0.00015204804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053436996,0.00012879448,0.00010233135,0.0000720183,0.0004448422,0.00011357786,0.0006563765,0.000027769782,0.000014447901],"category_scores_gemma":[0.000005631802,0.00008798548,0.000059523038,0.0005251746,0.000030417055,0.00024247756,0.00025165288,0.000093595845,0.00007394354],"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":[4.0153245e-7,0.00009800347,0.000010825906,8.174503e-7,0.000012232441,2.7473448e-7,0.00089729537,0.8579855,0.00018275551,0.12445107,0.00012471195,0.016236095],"study_design_scores_gemma":[0.00007842934,0.000011898518,0.0003171592,0.0000034523355,0.000004928034,0.000020773778,0.000053323463,0.9944061,0.0005599196,0.002564406,0.0018466271,0.00013297785],"about_ca_topic_score_codex":0.00009120267,"about_ca_topic_score_gemma":5.705898e-7,"teacher_disagreement_score":0.1364206,"about_ca_system_score_codex":0.00005762667,"about_ca_system_score_gemma":0.000062175386,"threshold_uncertainty_score":0.35879436},"labels":[],"label_agreement":null},{"id":"W2184191074","doi":"","title":"An Evolutionary Approach to Behavioral-Level Synthesis","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Scheduling (production processes); High-level synthesis; Fitness function; Theoretical computer science; Mathematical optimization; Genetic algorithm; Field-programmable gate array; Mathematics; Machine learning","score_opus":0.050532267507820555,"score_gpt":0.28347590627638886,"score_spread":0.2329436387685683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184191074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027158246,0.000024378858,0.9587778,0.0004081561,0.0000567255,0.00017334745,0.0000083424575,0.00020976059,0.037625708],"genre_scores_gemma":[0.3452074,0.0000012911908,0.6535731,0.000171916,0.000020699543,0.00016638811,0.0000028504583,0.00000494227,0.00085145317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897563,0.000052692678,0.00014068697,0.00041069067,0.00019902413,0.00022127813],"domain_scores_gemma":[0.9989802,0.000032288288,0.00002171482,0.0006953235,0.00007276003,0.00019768739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001512812,0.000101208025,0.000085416206,0.000083338564,0.00022758514,0.000060984694,0.0006370796,0.000043518998,0.00005675341],"category_scores_gemma":[0.000013743717,0.00009402168,0.000040272447,0.00050140155,0.000023659823,0.0004584957,0.00005938809,0.000057730354,0.00021321821],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.6583006e-7,0.00085099024,0.00033598606,0.000001464052,0.000003302156,7.3149505e-7,0.000101789476,0.0005397319,0.0005489982,0.98380786,0.005891401,0.00791725],"study_design_scores_gemma":[0.0007052237,0.0005391151,0.21188419,0.000024900151,0.000056571153,0.000445221,0.0010188213,0.5008821,0.010765574,0.07113084,0.19985749,0.0026899937],"about_ca_topic_score_codex":0.00004451692,"about_ca_topic_score_gemma":0.0000011396661,"teacher_disagreement_score":0.91267705,"about_ca_system_score_codex":0.00004753872,"about_ca_system_score_gemma":0.00007141565,"threshold_uncertainty_score":0.38340926},"labels":[],"label_agreement":null},{"id":"W2189373833","doi":"","title":"Evolvability and Acceleration in Evolutionary Computation","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Evolvability; Human-based evolutionary computation; Evolutionary computation; Computer science; Evolutionary algorithm; Evolutionary theory; Interactive evolutionary computation; Artificial intelligence; Evolutionary biology; Evolutionary programming; Biology; Epistemology","score_opus":0.02608097211931804,"score_gpt":0.2551749405873212,"score_spread":0.22909396846800317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189373833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24595949,0.000104982326,0.7500595,0.0016690514,0.000033474043,0.0001331483,6.7211636e-7,0.00009216804,0.0019474963],"genre_scores_gemma":[0.842944,0.000026840966,0.1568105,0.000081087266,0.000016808135,0.000021461736,0.000005254274,0.0000014723296,0.000092557166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999412,0.000027340582,0.0001405174,0.00022340022,0.00010550779,0.000091228874],"domain_scores_gemma":[0.9997034,0.000052433104,0.000022649607,0.00013607262,0.000049635266,0.000035794925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008953729,0.000051906718,0.000056006804,0.00006681115,0.00015448578,0.00001952565,0.00012279555,0.000027205539,0.0000092764385],"category_scores_gemma":[0.000010386013,0.00005092539,0.000012250004,0.00030877197,0.000049700127,0.0006560712,0.0000751165,0.000053028132,0.000016088308],"study_design_candidate":"observational","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":[0.000008406108,0.0008324458,0.23495008,0.000020022168,0.000009219521,0.000016514365,0.0016089483,0.008590019,0.0012030518,0.6851182,0.010206488,0.057436578],"study_design_scores_gemma":[0.000108139786,0.000010650177,0.5512112,0.0000011334951,2.2555332e-7,0.00002599856,0.0000070241977,0.43416795,0.000019969631,0.014164673,0.00023609622,0.000046958503],"about_ca_topic_score_codex":0.00007884862,"about_ca_topic_score_gemma":0.000012443906,"teacher_disagreement_score":0.6709536,"about_ca_system_score_codex":0.00004399112,"about_ca_system_score_gemma":0.000046180237,"threshold_uncertainty_score":0.20766771},"labels":[],"label_agreement":null},{"id":"W2190165019","doi":"10.1007/978-3-642-29142-5_2","title":"Aesthetic 3D Model Evolution","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Realm; Genetic programming; Evolutionary algorithm; Artificial intelligence; Entropy (arrow of time); Normality; Machine learning; Theoretical computer science; Mathematics; Statistics","score_opus":0.016613435031731957,"score_gpt":0.237412830465703,"score_spread":0.22079939543397104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2190165019","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016860928,0.0014975251,0.9922593,0.0010780228,0.0006188268,0.00031153177,0.0000057352427,0.00020072008,0.0040114336],"genre_scores_gemma":[0.10895175,0.000066562134,0.88915664,0.00059004256,0.00039741342,0.00002682836,0.000006691933,0.00002978499,0.000774271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969715,0.000017261533,0.00040053081,0.0011351851,0.0008087071,0.0006668195],"domain_scores_gemma":[0.99784285,0.000072738585,0.00020660821,0.0014603112,0.00020762657,0.00020985627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055623846,0.00040722478,0.0003185623,0.000538066,0.00037508545,0.00021736728,0.00263279,0.00027935687,0.000016047417],"category_scores_gemma":[0.000013971782,0.00038888233,0.000112555485,0.00052771805,0.00054676336,0.0008216181,0.0010019277,0.0006198759,0.00017439909],"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":[8.3671523e-7,0.000042409407,0.000013553411,0.0000086826785,0.0000019202723,0.0000048620277,0.00020854137,0.16933914,0.00004537194,0.29287577,0.000035969293,0.53742296],"study_design_scores_gemma":[0.0000757471,0.000028200679,0.000050494524,0.00005775346,0.000004356602,0.00007272736,2.4811749e-8,0.69739735,0.00002523951,0.30078205,0.0011796377,0.00032642137],"about_ca_topic_score_codex":0.00001349614,"about_ca_topic_score_gemma":0.000012508768,"teacher_disagreement_score":0.5370965,"about_ca_system_score_codex":0.0004982881,"about_ca_system_score_gemma":0.0005937084,"threshold_uncertainty_score":0.9998563},"labels":[],"label_agreement":null},{"id":"W2191710555","doi":"10.1609/aaai.v28i1.9099","title":"Genotypic versus Behavioural Diversity for Teams of Programs under the 4-v-3 Keepaway Soccer Task","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Neuroevolution; Task (project management); Diversity (politics); Benchmark (surveying); Computer science; Reinforcement learning; Genetic programming; Artificial intelligence; Perspective (graphical); Function (biology); Machine learning; Variation (astronomy); Domain (mathematical analysis); Artificial neural network; Mathematics; Engineering; Biology; Sociology","score_opus":0.09984402336368582,"score_gpt":0.29562060442139904,"score_spread":0.19577658105771323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191710555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49176624,0.000031416217,0.49190652,0.010244066,0.00082031626,0.0015926172,0.000018923196,0.00012468209,0.0034952122],"genre_scores_gemma":[0.99375385,0.0000067190276,0.0059113335,0.00008711645,0.000059303296,0.000065919856,0.0000011993951,0.000006497954,0.0001080371],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99870247,0.000014386155,0.0003312666,0.0003343198,0.00036033345,0.0002572202],"domain_scores_gemma":[0.99852765,0.00014974507,0.00032238307,0.00032075436,0.0006250603,0.000054400647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004601391,0.00014684854,0.00017001499,0.000043496606,0.0005585727,0.00009023093,0.002081348,0.00006712737,0.0000114507975],"category_scores_gemma":[0.00008563575,0.00009518821,0.00014991939,0.00038442924,0.00041441893,0.00022505729,0.0006599169,0.00016669215,0.000014620616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045862158,0.00016323719,0.00048827654,0.000012039654,0.000015177042,1.1030294e-8,0.0006131765,0.000053287626,0.003133498,0.9475811,0.00007857108,0.04781577],"study_design_scores_gemma":[0.00018435164,0.0008551257,0.006010701,0.000105542465,0.00008230392,0.000002394355,0.0017897361,0.18667562,0.11191453,0.69157666,0.00041843753,0.0003845944],"about_ca_topic_score_codex":0.00015089275,"about_ca_topic_score_gemma":0.000029636736,"teacher_disagreement_score":0.50198764,"about_ca_system_score_codex":0.000039785664,"about_ca_system_score_gemma":0.00006268767,"threshold_uncertainty_score":0.4296146},"labels":[],"label_agreement":null},{"id":"W2199868482","doi":"10.1007/0-387-23254-0_11","title":"Genetic Programming of an Algorithmic Chemistry","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Genetic programming; Synchronization (alternating current); Parallel computing; Theoretical computer science; Distributed computing; Programming language; Artificial intelligence","score_opus":0.009439915812976206,"score_gpt":0.21800485307398076,"score_spread":0.20856493726100456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199868482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000037794045,0.00032715197,0.5629952,0.00007256545,0.00004349289,0.00023211254,0.000014133827,0.00017856232,0.43609902],"genre_scores_gemma":[0.00041720114,0.000017814467,0.60666144,0.000019795722,0.00023689908,0.000029254883,0.000045683733,0.000021307847,0.3925506],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988796,0.0000031298625,0.00030064295,0.00042041356,0.00023920568,0.00015698615],"domain_scores_gemma":[0.9989062,0.000014811531,0.00016788745,0.0007328313,0.000105511455,0.00007279238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049670176,0.00018683127,0.00018642255,0.000041205192,0.00006389932,0.00003791336,0.00075964694,0.00017186663,0.00010435536],"category_scores_gemma":[9.2686827e-7,0.0001859103,0.00009455065,0.000045746743,0.00007620077,0.00010547806,0.00015241232,0.00014457431,0.00002991882],"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":[8.2479875e-7,0.00019116646,0.0000056055355,0.0001112352,0.000048557824,0.000025491003,0.000032577595,0.00028889324,0.0006132793,0.358314,0.0032346016,0.6371337],"study_design_scores_gemma":[0.00038217483,0.00014636504,0.0004166523,0.00011865364,0.00005531855,0.00019456136,0.0000062105073,0.15511021,0.0015204193,0.120114945,0.7207387,0.0011957742],"about_ca_topic_score_codex":0.000038950817,"about_ca_topic_score_gemma":0.0000026401635,"teacher_disagreement_score":0.71750414,"about_ca_system_score_codex":0.00003141675,"about_ca_system_score_gemma":0.00009592647,"threshold_uncertainty_score":0.75812024},"labels":[],"label_agreement":null},{"id":"W2209968826","doi":"10.1007/0-387-28111-8_14","title":"Evolution on Neutral Networks in Genetic Programming","year":2006,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Neutral network; Parallels; Computer science; Simple (philosophy); Genetic network; Genetic algorithm; Neutral theory of molecular evolution; Evolutionary programming; Population; Theoretical computer science; Boolean network; Mathematical optimization; Boolean function; Mathematics; Artificial intelligence; Algorithm; Biology; Machine learning; Genetics; Engineering; Artificial neural network","score_opus":0.013700692924903054,"score_gpt":0.22547362383367578,"score_spread":0.21177293090877272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209968826","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010852449,0.0012640947,0.19684215,0.0008370986,0.0009358246,0.0012164685,0.000009905534,0.0005617985,0.79822415],"genre_scores_gemma":[0.045037586,0.000043038202,0.027238136,0.0013874788,0.0031621929,0.00071434444,0.00019324398,0.00023606223,0.9219879],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99672484,0.000042168343,0.00079392,0.001150011,0.0005104048,0.00077863905],"domain_scores_gemma":[0.9983204,0.000095507916,0.0003699384,0.0008946118,0.0001096095,0.00020996098],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00037501182,0.0005411684,0.00041393025,0.0005732535,0.00019650157,0.0004074982,0.0018585132,0.0012387477,0.000013286339],"category_scores_gemma":[0.000012084143,0.00057838624,0.00020896218,0.00014837873,0.00018168725,0.00071457285,0.00036003115,0.0026541217,0.00005582982],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007909087,0.0000415215,0.0001397542,0.000020828897,0.000030647756,0.000037337402,0.00008066525,0.010259926,0.000005819746,0.7792722,0.109745465,0.100357905],"study_design_scores_gemma":[0.00064503815,0.00011979935,0.0025182518,0.00032168484,0.000034111934,0.000059638114,0.000006853502,0.073110275,0.0000039712077,0.15643772,0.76556456,0.0011780936],"about_ca_topic_score_codex":0.00009744156,"about_ca_topic_score_gemma":0.000026458492,"teacher_disagreement_score":0.6558191,"about_ca_system_score_codex":0.00061424146,"about_ca_system_score_gemma":0.00027461056,"threshold_uncertainty_score":0.99966675},"labels":[],"label_agreement":null},{"id":"W2230600954","doi":"10.5281/zenodo.50020","title":"SpaDES: v1.1.3","year":2016,"lang":"en","type":"article","venue":"INFM-OAR (INFN Catania)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Canadian Forest Service","funders":"","keywords":"Computer science","score_opus":0.015120438787445678,"score_gpt":0.2403209751858808,"score_spread":0.22520053639843512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2230600954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031676035,0.00046229362,0.9283898,0.022892846,0.0007085849,0.00040146956,0.00007214301,0.0008466043,0.014550208],"genre_scores_gemma":[0.9097683,0.0001299168,0.082869425,0.0011693224,0.0005168128,0.00013212344,0.000010317183,0.000026674004,0.005377126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986773,0.000026570575,0.00024159836,0.00043099505,0.00028314526,0.00034035847],"domain_scores_gemma":[0.99854547,0.0001395047,0.000084222505,0.0009713555,0.00010348303,0.00015596443],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001742735,0.00014773448,0.00012962132,0.00007928261,0.00019958284,0.00008459898,0.0010674262,0.00007445014,0.00010675058],"category_scores_gemma":[0.000046052577,0.00010896914,0.000072159215,0.00038439385,0.00009374311,0.0006813416,0.00028592334,0.00009260812,0.002494118],"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":[0.000005154124,0.00020970819,0.0019217718,0.000013970503,0.000039591723,0.00003068318,0.0005048705,0.00002477894,0.01780071,0.7261411,0.057952687,0.19535498],"study_design_scores_gemma":[0.0008807801,0.0001278429,0.019722132,0.0000744661,0.0000128367,0.00012800988,0.000040309744,0.005026171,0.0065986915,0.04582798,0.920868,0.0006928241],"about_ca_topic_score_codex":0.00004032845,"about_ca_topic_score_gemma":0.000014828689,"teacher_disagreement_score":0.8780922,"about_ca_system_score_codex":0.0000827215,"about_ca_system_score_gemma":0.000117582116,"threshold_uncertainty_score":0.99828255},"labels":[],"label_agreement":null},{"id":"W2250923797","doi":"10.5555/1999416.1999484","title":"Synergy of the reinforcement learning and agent-based technique for finding optimal solution in a predefined interval","year":2010,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reinforcement learning; Computer science; Reinforcement; Interval (graph theory); Actuator; Function (biology); Multi-agent system; Wireless; Q-learning; Artificial intelligence; Mathematical optimization; Engineering; Mathematics","score_opus":0.03310957087178093,"score_gpt":0.2911689759997367,"score_spread":0.2580594051279558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250923797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024897963,0.000004936279,0.9739545,0.00037308515,0.00017695646,0.0004908838,0.0000018168123,0.000035245565,0.00006460779],"genre_scores_gemma":[0.8519526,6.137799e-7,0.1478228,0.000030210975,0.000028996245,0.00009839212,0.000008585994,0.000004014285,0.000053794203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991418,0.00004365855,0.000287849,0.00024985583,0.00012897367,0.00014785372],"domain_scores_gemma":[0.99923486,0.00019678903,0.00016280849,0.00022683601,0.00014726141,0.00003142315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024425643,0.000098589415,0.00011111538,0.00009537457,0.00014443515,0.000047160825,0.00032154686,0.00006190763,0.0000091048505],"category_scores_gemma":[0.000031448635,0.00008543013,0.000049393544,0.00021654826,0.000057285055,0.00019462557,0.00019698722,0.00016544203,5.820066e-7],"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":[0.000014004695,0.000063028034,0.0040621236,0.00003475397,0.000008193614,1.668123e-7,0.00031338065,0.9142272,0.00812236,0.061983824,0.000030751547,0.011140235],"study_design_scores_gemma":[0.0004323405,0.00006611865,0.006530496,0.000044622866,0.0000030226802,7.768728e-7,0.000004639097,0.98879296,0.0025540253,0.0004862452,0.0009907737,0.00009397067],"about_ca_topic_score_codex":0.00003090618,"about_ca_topic_score_gemma":0.000022068947,"teacher_disagreement_score":0.8270546,"about_ca_system_score_codex":0.000021196167,"about_ca_system_score_gemma":0.00008074219,"threshold_uncertainty_score":0.34837398},"labels":[],"label_agreement":null},{"id":"W2253367123","doi":"10.1007/978-3-662-44303-3_7","title":"On Diversity, Teaming, and Hierarchical Policies: Observations from the Keepaway Soccer Task","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reinforcement learning; Computer science; Task (project management); Artificial intelligence; Benchmark (surveying); Diversity (politics); Champion; Evolutionary robotics; Field (mathematics); Machine learning; Evolutionary computation; Robotics; Function (biology); Evolutionary algorithm; Fitness function; Robot","score_opus":0.021963336922866943,"score_gpt":0.23709755532950846,"score_spread":0.21513421840664151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253367123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071520515,0.0001459194,0.9876454,0.009410027,0.00042629376,0.000262547,0.000028322422,0.000092956136,0.0012733194],"genre_scores_gemma":[0.48863164,0.00013210071,0.48334238,0.024699477,0.0018153671,0.00003436147,0.000043044343,0.00004748141,0.0012541569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975713,0.000038467544,0.00026662933,0.0010389199,0.00070823514,0.00037645493],"domain_scores_gemma":[0.996841,0.0015988934,0.00015007904,0.0011588264,0.0001159653,0.00013524308],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00040629593,0.00031561765,0.00026212662,0.00020635383,0.0013009295,0.00038495782,0.0029136736,0.00018801753,0.000011184087],"category_scores_gemma":[0.000074080366,0.00023527624,0.00008252188,0.0003689129,0.0010158288,0.00023402972,0.0034668108,0.00071732566,0.000029879004],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002069048,0.00003538683,0.00057976384,0.000005470887,0.000015569038,0.0000059780386,0.0017329097,0.0052575343,0.00003410996,0.8238556,0.0005761248,0.1678995],"study_design_scores_gemma":[0.00016803967,0.00008880632,0.012036005,0.000118011274,0.000009427415,0.000011501097,3.337438e-7,0.3363418,0.00001526419,0.6429054,0.00793094,0.00037448536],"about_ca_topic_score_codex":0.00022690413,"about_ca_topic_score_gemma":0.00011727978,"teacher_disagreement_score":0.50430304,"about_ca_system_score_codex":0.000101052974,"about_ca_system_score_gemma":0.00018488047,"threshold_uncertainty_score":0.9999992},"labels":[],"label_agreement":null},{"id":"W2271982690","doi":"","title":"A Review of Thresheld Convergence","year":2015,"lang":"en","type":"review","venue":"SSRN Electronic Journal","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Convergence (economics); Economics; Macroeconomics","score_opus":0.034865370073911585,"score_gpt":0.33219093624447926,"score_spread":0.29732556617056766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271982690","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.479503e-9,0.8935124,0.105262004,0.0002242045,0.00018048537,0.0002929487,0.0000054460897,0.000025525445,0.00049696496],"genre_scores_gemma":[2.2216746e-7,0.99701846,0.0020820503,0.00008935825,0.00018922363,0.000039637285,0.000009229218,0.000015705536,0.000556099],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971898,0.00015728333,0.0007774075,0.00030380866,0.00038237308,0.0011893172],"domain_scores_gemma":[0.9980869,0.000058492355,0.00079827436,0.0006315391,0.00030411713,0.00012066623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026155913,0.0002514274,0.001028573,0.00011986593,0.000100557496,0.000026001351,0.0019996725,0.000113553084,0.000019504481],"category_scores_gemma":[0.000050125916,0.00019049614,0.0004806548,0.00076292554,0.000047683454,0.00023998425,0.00020189505,0.0018199199,0.000111595444],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[1.2023825e-7,0.000025734756,1.2259179e-7,0.0058190897,0.00006837841,7.972969e-7,0.0000035251155,1.4099761e-7,1.0571601e-8,0.27317017,0.0026229285,0.71828896],"study_design_scores_gemma":[0.000048797327,0.000074859476,6.235468e-8,0.025738833,0.00013783126,0.0012458025,0.0000045755605,0.00003496149,3.6873548e-8,0.07925539,0.89328986,0.00016895849],"about_ca_topic_score_codex":0.000007404017,"about_ca_topic_score_gemma":0.000005335496,"teacher_disagreement_score":0.89066696,"about_ca_system_score_codex":0.00066661614,"about_ca_system_score_gemma":0.012607283,"threshold_uncertainty_score":0.9929903},"labels":[],"label_agreement":null},{"id":"W2290614696","doi":"","title":"DETECTING TEXTUAL ENTAILMENT WITH CONDITIONS ON DIRECTIONAL TEXT RELATEDNESS SCORES","year":2009,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Logical consequence; Textual entailment; Heuristics; Natural language processing; Computer science; Word (group theory); Artificial intelligence; Exploit; Relation (database); Point (geometry); Identity (music); Mathematics; Data mining","score_opus":0.006591953534917815,"score_gpt":0.23918872799734936,"score_spread":0.23259677446243154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290614696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058471568,0.0003261916,0.92825234,0.0013549404,0.000027072141,0.0005467434,0.000029601622,0.00023735208,0.010754184],"genre_scores_gemma":[0.99405205,0.000033313565,0.0051383926,0.00011730331,0.000052301817,0.00023210264,0.000020478594,0.000004843075,0.00034920097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992026,0.00004649745,0.00018806754,0.00027389138,0.00015415464,0.00013480482],"domain_scores_gemma":[0.9991012,0.000271327,0.00010925913,0.00035298074,0.00010311652,0.00006208928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022258873,0.00011770958,0.00011647639,0.000101272046,0.00052366586,0.00004650091,0.00026670547,0.00004004694,0.000016708509],"category_scores_gemma":[0.000011762442,0.00009752721,0.000027877246,0.00043554316,0.00023281439,0.00024380295,0.000037302216,0.000110841065,0.000012905461],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020064112,0.00014788863,0.00006751648,0.0000040507275,0.000013612692,2.8165005e-7,0.0001921781,0.00028340172,0.0007904909,0.9753682,0.000050966086,0.023061324],"study_design_scores_gemma":[0.00042567926,0.00040657207,0.016651472,0.000034886067,0.00003137709,0.00006190877,0.0008561588,0.00060586975,0.013869086,0.9626842,0.00408365,0.00028912473],"about_ca_topic_score_codex":0.000005962248,"about_ca_topic_score_gemma":0.0000016108967,"teacher_disagreement_score":0.9355805,"about_ca_system_score_codex":0.00001967231,"about_ca_system_score_gemma":0.000047687397,"threshold_uncertainty_score":0.4027667},"labels":[],"label_agreement":null},{"id":"W2292265081","doi":"10.1139/tcsme-2013-0082","title":"AUTOMATIC DESIGN OF SHIFT-AND-ADD BASED COLOR SPACE CONVERTER USING A GENETIC ALGORITHM","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"YCbCr; RGB color model; Color space; Computer science; Genetic algorithm; Artificial intelligence; Space (punctuation); Algorithm; Computer vision; Architecture; Color image; Image (mathematics); Image processing","score_opus":0.013301105424619946,"score_gpt":0.19948292128696524,"score_spread":0.18618181586234528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292265081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018294203,0.0000433288,0.99652976,0.00091231026,0.00009014276,0.0005335247,0.00002811379,0.000032574568,8.509302e-7],"genre_scores_gemma":[0.35542995,0.0000014802583,0.64441115,0.000057902667,0.00000726866,0.00007710439,3.906711e-7,0.000007918305,0.000006853975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934524,0.000011309738,0.00018990181,0.00014553031,0.00010715564,0.0002008441],"domain_scores_gemma":[0.9993517,0.00012713333,0.000049451115,0.00026362567,0.00006420378,0.0001438869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011257451,0.000093588504,0.0001372348,0.00003866603,0.00016973593,0.000023140805,0.00032605955,0.00007851137,0.000025432157],"category_scores_gemma":[0.000007956071,0.00008465547,0.00019832463,0.00027376562,0.000041197753,0.00011602038,0.00000891221,0.00009093496,6.8699484e-7],"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":[0.0000013543248,0.00010836815,0.000003292517,0.00028676633,0.0003045193,3.4854702e-7,0.00078340253,0.9180744,0.020101698,0.026420388,0.00029934486,0.033616103],"study_design_scores_gemma":[0.00018944751,0.000031421205,0.00012926577,0.000027138978,0.00003498324,0.000003877692,0.00001691355,0.9960928,0.0022983833,0.0010393845,0.000047807138,0.000088557645],"about_ca_topic_score_codex":0.0061940867,"about_ca_topic_score_gemma":0.00021901638,"teacher_disagreement_score":0.35360053,"about_ca_system_score_codex":0.00012207775,"about_ca_system_score_gemma":0.00027187957,"threshold_uncertainty_score":0.93636477},"labels":[],"label_agreement":null},{"id":"W2299870309","doi":"10.1108/ijicc-07-2015-0026","title":"Diploidy in evolutionary algorithms for dynamic optimization problems","year":2015,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ploidy; Algorithm; Computer science; Evolutionary algorithm; Fitness function; Genetic algorithm; Representation (politics); Mathematical optimization; Function (biology); Selection (genetic algorithm); Position (finance); Mathematics; Artificial intelligence; Biology; Genetics","score_opus":0.027162037825258785,"score_gpt":0.30257861420126114,"score_spread":0.27541657637600236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2299870309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009052059,0.001126787,0.98695916,0.0017391763,0.0008262267,0.00014374165,0.0000045455854,0.000021564652,0.00012671734],"genre_scores_gemma":[0.53424376,0.00027449636,0.46507922,0.00009605614,0.00020468062,0.000004662557,0.00001021249,0.0000086945165,0.000078224395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865663,0.000037421443,0.00057662994,0.00018424384,0.00039087603,0.00015416993],"domain_scores_gemma":[0.99832714,0.00015584097,0.0003177482,0.00011066721,0.0009740641,0.00011455362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052146026,0.00011100711,0.00014858,0.00024648837,0.00004651291,0.00010820591,0.000642623,0.00005322529,0.0000016738471],"category_scores_gemma":[0.00011675592,0.00010532273,0.000065112654,0.00018498162,0.000045842782,0.00024939267,0.00016509154,0.0001489121,0.0000020517598],"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":[0.000020698379,0.00030651936,0.0013187171,0.000007987087,0.00005975714,0.000011711571,0.0012231322,0.9027793,0.00003168377,0.020923344,0.00054136856,0.07277579],"study_design_scores_gemma":[0.00054677366,0.0001629874,0.0010610702,0.00008766923,0.0000059446293,0.0002203263,0.00015053256,0.9795852,0.00004133565,0.014952731,0.003071671,0.00011375922],"about_ca_topic_score_codex":0.000014673693,"about_ca_topic_score_gemma":0.0000028085713,"teacher_disagreement_score":0.52519166,"about_ca_system_score_codex":0.00018151921,"about_ca_system_score_gemma":0.00013647348,"threshold_uncertainty_score":0.42949364},"labels":[],"label_agreement":null},{"id":"W2312298105","doi":"10.2316/journal.203.2012.2.203-4846","title":"OPTIMAL VAR PLANNING USING FACTS","year":2012,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Thyristor; Unified power flow controller; Control theory (sociology); Power flow; Capacitor; AC power; Static VAR compensator; Electric power system; Controller (irrigation); Series (stratigraphy); Computer science; Control engineering; Power (physics); Engineering; Control (management); Voltage; Electrical engineering; Physics; Artificial intelligence; Biology","score_opus":0.022118610907700686,"score_gpt":0.28162437977688676,"score_spread":0.2595057688691861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312298105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0791741,0.0024449925,0.9144549,0.00026244656,0.0026226146,0.0000098343535,0.0000022050788,0.000011387311,0.0010175115],"genre_scores_gemma":[0.9851783,0.000022007916,0.013989529,0.000058081572,0.00061785633,9.583508e-7,7.2307256e-7,0.0000038793028,0.00012868254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922895,0.00002641524,0.0002539766,0.00006905521,0.00029677214,0.00012482791],"domain_scores_gemma":[0.9993843,0.000038874638,0.00019384014,0.00007332908,0.00019873872,0.00011094048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002179285,0.00006518681,0.000094416966,0.00011830643,0.00005683017,0.00010987892,0.00035594543,0.000030711184,0.000004840614],"category_scores_gemma":[0.000008838945,0.0000543085,0.000043353237,0.00006183152,0.000016837866,0.00078276277,0.000079060876,0.000063330655,0.000001731576],"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":[0.000041878357,0.0005383403,0.020064965,0.000014719493,0.0007870601,0.00016834705,0.006335417,0.21100877,0.0066458313,0.7395005,0.0045743147,0.010319811],"study_design_scores_gemma":[0.0013500719,0.00020611579,0.026024485,0.00036909428,0.000036299723,0.01180338,0.0010635939,0.5310672,0.0007556068,0.0010186887,0.42570728,0.00059815275],"about_ca_topic_score_codex":0.000058543883,"about_ca_topic_score_gemma":1.0050631e-7,"teacher_disagreement_score":0.9060042,"about_ca_system_score_codex":0.000044462067,"about_ca_system_score_gemma":0.00003427741,"threshold_uncertainty_score":0.22146364},"labels":[],"label_agreement":null},{"id":"W2313214159","doi":"10.7551/978-0-262-32621-6-ch108","title":"Evolving Autonomous Agent Controllers as Analytical Mathematical Models","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Autonomous agent; Artificial intelligence; Control engineering; Engineering","score_opus":0.01984280675682549,"score_gpt":0.25598515191446486,"score_spread":0.23614234515763938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313214159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066407723,0.000016630545,0.85217905,0.0034774907,0.00003568958,0.00010304382,3.6019074e-7,0.0001857308,0.1433379],"genre_scores_gemma":[0.77586186,0.0000019472177,0.22006124,0.00061935285,0.00005689769,0.000032106516,9.80997e-7,0.0000053131475,0.0033603278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990282,0.00003059852,0.00021265284,0.00028713408,0.00021431215,0.00022709786],"domain_scores_gemma":[0.9991175,0.00016039735,0.000033919783,0.00046962936,0.00006201123,0.00015652228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025481379,0.00009591167,0.00014726364,0.000048133465,0.0001417169,0.000112966525,0.0004946394,0.000042436426,0.00020054595],"category_scores_gemma":[0.00004125264,0.00007784707,0.000078905075,0.00016325033,0.0000444115,0.00032408364,0.00014950507,0.00007901271,0.00068887096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.3695184e-7,0.00006426713,0.000004907657,0.0000018580371,0.000008323456,9.227005e-7,0.000050250812,0.0032788075,0.000014882767,0.99132586,0.0016672405,0.0035822473],"study_design_scores_gemma":[0.00015183409,0.000022623677,0.00009657616,0.0000026902248,0.0000040045243,0.000011919603,0.000008133387,0.70799196,0.000012059884,0.29005527,0.0015665799,0.00007631062],"about_ca_topic_score_codex":0.000012414897,"about_ca_topic_score_gemma":6.9536515e-7,"teacher_disagreement_score":0.77519774,"about_ca_system_score_codex":0.00004458898,"about_ca_system_score_gemma":0.00005097776,"threshold_uncertainty_score":0.88542724},"labels":[],"label_agreement":null},{"id":"W2320280295","doi":"10.7227/ijmee.38.3.3","title":"Use of Linear Graphs and Thevenin/Norton Equivalent Circuits in the Modeling and Analysis of Electro-Mechanical Systems","year":2010,"lang":"en","type":"article","venue":"International Journal of Mechanical Engineering Education","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Thévenin's theorem; Equivalent circuit; Mechanical system; Computer science; Network analysis; Domain (mathematical analysis); Electronic circuit; Coupling (piping); Nonlinear system; Linear system; Control engineering; Mathematics; Engineering; Mechanical engineering; Electrical engineering; Physics; Mathematical analysis; Artificial intelligence; Voltage","score_opus":0.02001199515303856,"score_gpt":0.2720596230921013,"score_spread":0.25204762793906277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320280295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5420433,0.00010808322,0.45710894,0.00037315025,0.00030800432,0.000049868966,0.000002888724,0.000003899619,0.0000018409136],"genre_scores_gemma":[0.98186314,0.000111828216,0.017931817,0.000019168467,0.00005878426,0.000006297898,0.0000031391446,0.0000035959695,0.0000022246825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989868,0.000029743718,0.00045421746,0.00011251856,0.00034067797,0.00007607882],"domain_scores_gemma":[0.9990496,0.00021086069,0.00021453793,0.0001448028,0.00033470086,0.000045488465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005905199,0.00006809063,0.00016440245,0.0003953178,0.000017676864,0.000039254093,0.0004083298,0.0000505878,0.0000012425354],"category_scores_gemma":[0.00015741811,0.000053399177,0.00007144219,0.00038644377,0.000011634886,0.00026888584,0.000050369974,0.00022988519,6.857465e-8],"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":[0.000009840333,0.00038903728,0.00040341433,0.000025650577,0.00032549386,0.0000019722643,0.00042783932,0.12309291,0.09037967,0.776568,0.000012313033,0.008363853],"study_design_scores_gemma":[0.00009749027,0.000044955083,0.0025959215,0.0000450035,0.000056290704,0.00007383789,0.00003515134,0.99378294,0.000726129,0.0024415962,0.00005093372,0.000049733677],"about_ca_topic_score_codex":0.00005698061,"about_ca_topic_score_gemma":0.0000040863692,"teacher_disagreement_score":0.87069005,"about_ca_system_score_codex":0.000021772323,"about_ca_system_score_gemma":0.00008067869,"threshold_uncertainty_score":0.21775553},"labels":[],"label_agreement":null},{"id":"W2345195955","doi":"10.1007/s10489-016-0788-9","title":"XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot","year":2016,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Reinforcement learning; Mobile robot; Motion planning; Artificial intelligence; Motion (physics); Computer vision; Robot; Human–computer interaction; Algorithm","score_opus":0.023395401115473856,"score_gpt":0.2794243832167838,"score_spread":0.25602898210131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345195955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020746082,0.00005580746,0.99841386,0.00012511553,0.0000630561,0.00047898755,0.0000027962155,0.0001118666,0.000541021],"genre_scores_gemma":[0.5247803,0.000011074743,0.47446865,0.000048436505,0.00004402651,0.00047925205,0.000006239293,0.000007801022,0.00015421839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998825,0.000013515058,0.0003374364,0.00035684658,0.00021585198,0.0002513115],"domain_scores_gemma":[0.9989977,0.0003276748,0.00016425834,0.00033175125,0.00010847144,0.00007017309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022224594,0.00012576881,0.00014920236,0.000051950978,0.00014601501,0.000022135006,0.0004986009,0.000056502497,0.000036899255],"category_scores_gemma":[0.000023081862,0.000098485965,0.00006955708,0.0002859193,0.00007812987,0.00014057371,0.00009586575,0.00007536299,0.00003891743],"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":[0.0000061922087,0.00006662132,0.000024120092,0.000009925196,0.000008760438,3.53349e-7,0.00012464459,0.123679936,0.0085645085,0.044818927,0.0000836522,0.82261235],"study_design_scores_gemma":[0.00018574345,0.0002259201,0.00006992333,0.00004006895,0.0000056869667,0.0000018516142,0.00007629038,0.8794703,0.10863084,0.005548158,0.005562113,0.00018309882],"about_ca_topic_score_codex":0.00000772949,"about_ca_topic_score_gemma":1.6841076e-7,"teacher_disagreement_score":0.82242924,"about_ca_system_score_codex":0.000059555507,"about_ca_system_score_gemma":0.000054756176,"threshold_uncertainty_score":0.40161413},"labels":[],"label_agreement":null},{"id":"W2346479336","doi":"","title":"Edge detection of petrographic images using genetic programming","year":2000,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Edge detection; Detector; Computer vision; Genetic programming; Computer science; Artificial intelligence; Petrography; Canny edge detector; Image processing; Enhanced Data Rates for GSM Evolution; Sampling (signal processing); Pattern recognition (psychology); Image (mathematics); Mineralogy; Geology","score_opus":0.012131605469170513,"score_gpt":0.24019857016700447,"score_spread":0.22806696469783394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346479336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17924772,0.00017704447,0.8196246,0.000051544233,0.000024915824,0.00008952294,4.0657875e-7,0.00008980779,0.0006944277],"genre_scores_gemma":[0.60034,0.00001690586,0.39946765,0.000008915572,0.000023992998,0.00000989469,2.5889597e-7,0.0000023379755,0.00013006951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947786,0.00001378374,0.00012856464,0.00016189189,0.000098415345,0.0001194894],"domain_scores_gemma":[0.9996554,0.000012034463,0.000031324245,0.00022666379,0.00004148782,0.00003304581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005218003,0.000052425938,0.00005539832,0.00006492552,0.00010741835,0.00003150158,0.0002110077,0.000020854013,0.000045430497],"category_scores_gemma":[0.0000015201485,0.0000490329,0.000044288627,0.0005130812,0.00004158245,0.00017735221,0.000027531272,0.0000375071,0.000011367943],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.9442325e-7,0.00008530693,0.0005349544,0.000006016916,0.000006590397,7.995764e-7,0.000038467067,0.00053571287,0.015543866,0.0023430318,0.000011593436,0.98089296],"study_design_scores_gemma":[0.00042124212,0.00022738054,0.106692135,0.000025771136,0.000025558991,0.00015530708,0.000042019416,0.8236338,0.045391843,0.0076310434,0.015328157,0.00042576756],"about_ca_topic_score_codex":0.00008586826,"about_ca_topic_score_gemma":0.0000032905407,"teacher_disagreement_score":0.9804672,"about_ca_system_score_codex":0.000009611044,"about_ca_system_score_gemma":0.000016007218,"threshold_uncertainty_score":0.19995035},"labels":[],"label_agreement":null},{"id":"W2347121625","doi":"10.1007/978-3-319-30668-1_14","title":"Modelling Evolvability in Genetic Programming","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Evolvability; Computer science; Genetic programming; A priori and a posteriori; Fitness landscape; Artificial intelligence; Genetic algorithm; Tree (set theory); Theoretical computer science; Machine learning; Mathematics","score_opus":0.019790579970375106,"score_gpt":0.2436607517646063,"score_spread":0.2238701717942312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347121625","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013693042,0.00044170654,0.99617606,0.00096755323,0.0004075975,0.00048442886,0.0000030328385,0.00012074355,0.0012619648],"genre_scores_gemma":[0.08848947,0.000041176547,0.91074616,0.00015663567,0.0002712484,0.000039179828,0.000001332397,0.000019378216,0.00023539126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667656,0.000025409241,0.00052010245,0.0015134007,0.00064661127,0.0006178907],"domain_scores_gemma":[0.9979317,0.00026337706,0.00017097751,0.0013134731,0.00018563475,0.0001347904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006382362,0.00035918338,0.00034014313,0.0005422263,0.00019791175,0.00025402816,0.0024943138,0.0002266905,0.000012960088],"category_scores_gemma":[0.000022014443,0.00030575873,0.00009464239,0.00060901506,0.00052090024,0.00050396455,0.0009007098,0.00051183463,0.000041136514],"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":[0.000001289313,0.000033775697,0.00006832856,0.000014445225,0.0000020524199,0.000018273293,0.00021735109,0.26715884,0.000017181314,0.02604624,0.0000017760215,0.7064205],"study_design_scores_gemma":[0.00010008946,0.000032918073,0.00011929182,0.00016554842,0.0000013586503,0.00001778349,4.13899e-8,0.69653267,0.000032297583,0.30186966,0.0008427757,0.00028556839],"about_ca_topic_score_codex":0.000030689705,"about_ca_topic_score_gemma":0.0000475093,"teacher_disagreement_score":0.7061349,"about_ca_system_score_codex":0.00042296355,"about_ca_system_score_gemma":0.00046909656,"threshold_uncertainty_score":0.99993944},"labels":[],"label_agreement":null},{"id":"W2369818440","doi":"10.1007/978-3-319-16501-1","title":"Genetic programming : 18th European conference, EuroGP 2015, Copenhagen, Denmark, April 8-10, 2015, proceedings","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Genetic programming; Computer science; Crossover; Symbolic regression; Theoretical computer science; Artificial intelligence; Genetic representation; Evolutionary algorithm; Common Lisp; Fitness function; Genetic algorithm; Programming language; Machine learning; Lisp","score_opus":0.03447444470900374,"score_gpt":0.2648129841430723,"score_spread":0.23033853943406857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2369818440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069791367,0.0006607212,0.65299773,0.004695356,0.00029494858,0.0008817326,0.000004401192,0.00073755125,0.3327484],"genre_scores_gemma":[0.39434278,0.000027352773,0.52222157,0.000428413,0.00034343175,0.00012215198,0.000009622002,0.000041638472,0.082463026],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99802035,0.000055629254,0.00036276734,0.0006522365,0.0004380603,0.00047097317],"domain_scores_gemma":[0.998251,0.000025524658,0.00013854848,0.00048479723,0.0006639637,0.00043614817],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055222766,0.00023192709,0.00017861584,0.00009460596,0.00021914669,0.0005218366,0.0013649738,0.000049880207,0.000423455],"category_scores_gemma":[0.000047165653,0.00020686419,0.000060224258,0.0006485943,0.000098552024,0.0006589046,0.0005831092,0.00016120746,0.008246437],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005079121,0.0002708586,0.0004966539,0.000016445509,0.000023807886,0.0000219108,0.00070959853,0.000038163806,0.0002821977,0.05984409,0.89250654,0.045784682],"study_design_scores_gemma":[0.0005605922,0.00020725565,0.00821006,0.000019668938,0.000015238022,0.000111455774,0.00020092101,0.05195193,0.00018726292,0.0012685009,0.9367933,0.000473813],"about_ca_topic_score_codex":0.000024150791,"about_ca_topic_score_gemma":0.0000056005847,"teacher_disagreement_score":0.38736364,"about_ca_system_score_codex":0.000053736236,"about_ca_system_score_gemma":0.00021440745,"threshold_uncertainty_score":0.99252576},"labels":[],"label_agreement":null},{"id":"W2391037491","doi":"10.1007/978-3-319-30668-1_9","title":"A Genetic Programming Approach for the Traffic Signal Control Problem with Epigenetic Modifications","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Genetic programming; Traffic signal; Genetic algorithm; Control (management); Epigenetics; Real-time computing; Artificial intelligence; Machine learning; Biology","score_opus":0.015855179646714398,"score_gpt":0.2243204114279859,"score_spread":0.2084652317812715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2391037491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000057561633,0.0008175067,0.99399924,0.0019824603,0.00010688645,0.0025405788,0.00001758221,0.00013325135,0.00039671728],"genre_scores_gemma":[0.10905987,0.00002247515,0.88916063,0.00025253597,0.0003620683,0.00082690665,0.0000034359703,0.000029818395,0.00028223702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970292,0.000024490246,0.00041210424,0.0013062326,0.0006168132,0.0006112022],"domain_scores_gemma":[0.9973071,0.0007516859,0.00026454113,0.0012112458,0.000334213,0.00013118761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004974686,0.00039889693,0.000312021,0.0002630328,0.0007606016,0.00040956694,0.0029095442,0.00015805161,0.000003891107],"category_scores_gemma":[0.000011574873,0.00023664965,0.000120719786,0.00048105908,0.00091850274,0.00022274474,0.00022892527,0.00033970352,0.000007829475],"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":[0.0000037910404,0.00004533781,0.0000045272336,0.000017571632,0.000017428742,0.0000014841274,0.0001666428,0.31257826,0.000025245676,0.026470987,0.000006876154,0.6606618],"study_design_scores_gemma":[0.00044119856,0.00020827733,0.00010480823,0.00008727262,0.000026972237,0.00006068914,2.7984098e-7,0.9665823,0.000024167251,0.029858071,0.0022093372,0.00039665087],"about_ca_topic_score_codex":0.000003882759,"about_ca_topic_score_gemma":0.000012990568,"teacher_disagreement_score":0.6602652,"about_ca_system_score_codex":0.0001499159,"about_ca_system_score_gemma":0.0006443514,"threshold_uncertainty_score":0.96502924},"labels":[],"label_agreement":null},{"id":"W2395019440","doi":"","title":"Binary versus Real-valued Reward Functions under Coevolutionary Reinforcement Learning","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Coevolution; Binary number; Population; Computer science; Reinforcement learning; Sampling (signal processing); Software deployment; Artificial intelligence; Mathematics; Ecology","score_opus":0.021351452516815025,"score_gpt":0.26625294182542625,"score_spread":0.24490148930861122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395019440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011839089,0.000025417736,0.9111575,0.003177801,0.0012086453,0.00019987722,0.000001497833,0.00066435,0.07172583],"genre_scores_gemma":[0.87462234,0.000043853383,0.099599004,0.00019133878,0.00027912404,0.000115201845,0.000062666746,0.000014294761,0.025072148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987297,0.000033435666,0.00023899684,0.00039780882,0.00030477464,0.0002952977],"domain_scores_gemma":[0.99891305,0.000109837194,0.00007417886,0.00061990425,0.00013615983,0.0001468442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002131611,0.00013950208,0.00010142278,0.00011257482,0.0007200021,0.000066991706,0.00049774116,0.00009112329,0.00038215728],"category_scores_gemma":[0.000025068966,0.0001335067,0.00008149672,0.0005132467,0.00008904702,0.00051251426,0.0002905029,0.00039356237,0.0006716633],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014582982,0.000097761455,0.00020924691,0.000002727861,0.000027589007,0.0000024263115,0.00006981575,0.024231452,0.0035543088,0.9573328,0.012830615,0.0016266847],"study_design_scores_gemma":[0.0013822959,0.0004205045,0.028919354,0.000008253854,0.000024481036,0.000041197807,0.00032503903,0.85106575,0.00015330876,0.009110205,0.10800739,0.0005422084],"about_ca_topic_score_codex":0.00017717914,"about_ca_topic_score_gemma":0.00003106696,"teacher_disagreement_score":0.9482226,"about_ca_system_score_codex":0.00007370854,"about_ca_system_score_gemma":0.00015670546,"threshold_uncertainty_score":0.8633097},"labels":[],"label_agreement":null},{"id":"W2397401021","doi":"","title":"A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture.","year":2012,"lang":"en","type":"article","venue":"IAT","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Architecture; Task (project management); Computer science; Set (abstract data type); Artificial intelligence; Quality (philosophy); Hybrid learning; Machine learning; Engineering","score_opus":0.025482849714282003,"score_gpt":0.3156425854667007,"score_spread":0.2901597357524187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397401021","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069237105,0.0001765314,0.9909823,0.0010001725,0.00014604465,0.0003874647,0.000016476562,0.00014531465,0.00022199452],"genre_scores_gemma":[0.2783258,0.0000011589568,0.7199024,0.00034376993,0.00025947075,0.00067579024,0.000025270776,0.000011526609,0.0004548006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991325,0.00007658659,0.0001291432,0.00023533625,0.00012731824,0.00029908668],"domain_scores_gemma":[0.99928033,0.00023396016,0.000050786217,0.0002468533,0.00007618195,0.00011190438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029283084,0.000108688226,0.000114655515,0.000055605964,0.0002869758,0.000055484365,0.00029992659,0.000029147708,0.000014559798],"category_scores_gemma":[0.00003154201,0.00009581942,0.00007997751,0.00014935796,0.000025600872,0.00019005244,0.00006634399,0.0001437083,0.000040417977],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004555002,0.0018882196,0.0071197534,0.000057525747,0.000081999104,0.000009484779,0.0037088073,0.022078063,0.022561949,0.20556912,0.0064780964,0.73040146],"study_design_scores_gemma":[0.0016190656,0.00036669307,0.01187692,0.000033671775,0.000077364304,0.000100390105,0.00006920925,0.6530304,0.017234283,0.005442195,0.30934766,0.0008021135],"about_ca_topic_score_codex":0.000017320932,"about_ca_topic_score_gemma":0.0000022102317,"teacher_disagreement_score":0.7295993,"about_ca_system_score_codex":0.00003673049,"about_ca_system_score_gemma":0.000056854133,"threshold_uncertainty_score":0.39074025},"labels":[],"label_agreement":null},{"id":"W2404712476","doi":"","title":"Measures of disorder and straight insertion sort with erroneous comparisons.","year":2011,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"sort; Mathematics; Arithmetic; Statistics","score_opus":0.025323314013570887,"score_gpt":0.21994887072597255,"score_spread":0.19462555671240167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404712476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89875174,0.00039778213,0.095185995,0.00026199344,0.0002121852,0.00024601675,0.0000037659047,0.00011527868,0.0048252675],"genre_scores_gemma":[0.9792808,0.000020023488,0.020606808,0.000014477764,0.0000018566768,0.000019388544,0.0000021963372,0.000005171902,0.00004924957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993502,0.000023826738,0.00014331625,0.0001986075,0.00016361542,0.00012041656],"domain_scores_gemma":[0.99946207,0.000021505304,0.00007561772,0.0002948263,0.00008939156,0.000056586723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008598046,0.00008509406,0.00011629567,0.000042637585,0.00010400344,0.000015023135,0.00026106584,0.000033923003,0.000010565804],"category_scores_gemma":[0.0000043947634,0.00007119027,0.000017759474,0.00020407159,0.00009476263,0.00020839341,0.000070330425,0.00007048615,0.000004701659],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000788979,0.00027560064,0.020779338,0.0000066728685,0.000018272613,0.0000018194801,0.00042189792,0.00000948971,0.00014096689,0.9749941,0.0003106318,0.0030332801],"study_design_scores_gemma":[0.0014411886,0.0006088966,0.6634919,0.00003838038,0.000031268628,0.000054472384,0.00016202114,0.01305656,0.0023953903,0.31486508,0.0034507224,0.00040412453],"about_ca_topic_score_codex":0.00010840299,"about_ca_topic_score_gemma":0.000027211976,"teacher_disagreement_score":0.6601291,"about_ca_system_score_codex":0.000009774147,"about_ca_system_score_gemma":0.000039079936,"threshold_uncertainty_score":0.2903055},"labels":[],"label_agreement":null},{"id":"W2423572593","doi":"10.22215/etd/2009-06169","title":"Exaptation and functional shift in evolutionary computing","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Exaptation; Computer science; Humanities; Philosophy; Biology; Evolutionary biology","score_opus":0.012386559285714688,"score_gpt":0.24804945488768712,"score_spread":0.23566289560197243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2423572593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061004534,0.0030492714,0.8924951,0.002436564,0.001048658,0.0008165406,0.00000901662,0.000533191,0.03860713],"genre_scores_gemma":[0.8816387,0.00014260765,0.11156223,0.00032058667,0.00029729152,0.00005549093,0.0012214821,0.000019667126,0.004741946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9986658,0.00003360267,0.00032454077,0.0005126882,0.00026693495,0.00019641315],"domain_scores_gemma":[0.99942213,0.000085462656,0.00012237628,0.00023121924,0.00007701398,0.00006180331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014509603,0.0001833025,0.00016608424,0.0002741379,0.0001923108,0.000080712096,0.0002258557,0.00016957936,0.00002240577],"category_scores_gemma":[0.000012926719,0.00019562116,0.000044287008,0.00043554907,0.000017407901,0.00039946247,0.000041698226,0.00025025406,0.000033466215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015111068,0.0002730012,0.0008109128,0.000053947322,0.000018283556,0.000009371553,0.0010253191,0.0013291142,0.00011920191,0.82520247,0.0041350066,0.16700825],"study_design_scores_gemma":[0.00021516051,0.000027365078,0.81617576,0.000050235798,0.0000044365793,0.000010447791,0.00011135335,0.124279425,0.000011265145,0.05787907,0.0009996995,0.00023577006],"about_ca_topic_score_codex":0.000093891926,"about_ca_topic_score_gemma":0.0002144866,"teacher_disagreement_score":0.8206342,"about_ca_system_score_codex":0.00008006716,"about_ca_system_score_gemma":0.00014872168,"threshold_uncertainty_score":0.79771996},"labels":[],"label_agreement":null},{"id":"W2436862416","doi":"10.1016/j.procs.2017.09.009","title":"A Comparative Analysis of the Performance of Scalable Parallel Patterns Applied to Genetic Algorithms and Configured for NVIDIA GPUs","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Parallel computing; Scalability; Algorithm; Computational science; Computer architecture; Operating system","score_opus":0.021249484756136673,"score_gpt":0.2718349738215539,"score_spread":0.2505854890654172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2436862416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39057332,0.00002270593,0.6084794,0.00028873453,0.0000936424,0.0004322536,0.000015562971,0.000012191061,0.00008221487],"genre_scores_gemma":[0.72603387,0.000008530749,0.27375844,0.00006464515,0.000026136,0.00009369204,7.448304e-7,0.0000019156273,0.000012023084],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855846,0.000009601898,0.0002807608,0.0005239087,0.0003634335,0.00026382852],"domain_scores_gemma":[0.9982059,0.00007929272,0.000313664,0.00093924365,0.0003465608,0.00011532357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032862526,0.0001249847,0.00030755028,0.0001750772,0.000684587,0.00016523345,0.0023979822,0.000026917061,0.0000011047648],"category_scores_gemma":[0.00001595616,0.00009433889,0.000067685265,0.0009180846,0.00050615316,0.00039263006,0.000750262,0.000055213302,0.00000124346],"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":[0.00009266977,0.0012272745,0.28007656,0.00061208074,0.00092255115,0.0000014047631,0.0252476,0.20311701,0.04145877,0.203973,0.0006506373,0.24262045],"study_design_scores_gemma":[0.00013616239,0.000056914338,0.39777055,0.000012238766,0.000027434518,0.000001287502,0.000006727339,0.5970021,0.0046830275,0.00017629404,0.00004979039,0.00007745614],"about_ca_topic_score_codex":0.000028825616,"about_ca_topic_score_gemma":0.000011104498,"teacher_disagreement_score":0.3938851,"about_ca_system_score_codex":0.00001970429,"about_ca_system_score_gemma":0.00016830825,"threshold_uncertainty_score":0.52653587},"labels":[],"label_agreement":null},{"id":"W2478763563","doi":"10.1145/2908961.2909011","title":"mpEAd","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Formalism (music); Notation; Theoretical computer science; Simple (philosophy); Population; Artificial intelligence; Epistemology; Mathematics; Sociology","score_opus":0.011886221092908307,"score_gpt":0.23149456568300242,"score_spread":0.21960834459009412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478763563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051296,0.00001011444,0.9577862,0.011472097,0.00003297621,0.00001691263,2.774577e-7,0.000112434325,0.03005603],"genre_scores_gemma":[0.6385378,0.000011709563,0.33760414,0.00046033852,0.00006317034,0.000017234523,1.3950383e-7,0.0000017813784,0.023303635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997844,0.000002939169,0.00003273793,0.0000826776,0.000041657706,0.000055552737],"domain_scores_gemma":[0.9997488,0.000020850062,0.000006056568,0.00018713194,0.000013874948,0.000023239512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026098312,0.000018088811,0.000015236446,0.000010235995,0.00003282156,0.000009294364,0.00021924003,0.0000068208374,0.00007542042],"category_scores_gemma":[0.0000026651398,0.000009546671,0.000010373412,0.00006852156,0.000009706864,0.00016031068,0.000050555303,0.000006423903,0.00067483564],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.7337824e-8,0.0000069941034,0.00008682641,7.276022e-8,4.7180575e-7,1.7015753e-7,0.0000034603354,1.3427136e-7,0.0011846544,0.9101821,0.0061374856,0.08239759],"study_design_scores_gemma":[0.00032524863,0.000034633977,0.031788867,0.0000069844386,8.86707e-7,0.000020614887,0.0000050292333,0.009724733,0.0067432015,0.2722302,0.6789227,0.00019684991],"about_ca_topic_score_codex":0.0000020097054,"about_ca_topic_score_gemma":5.707853e-7,"teacher_disagreement_score":0.6727852,"about_ca_system_score_codex":0.000005737576,"about_ca_system_score_gemma":0.000008992123,"threshold_uncertainty_score":0.8673872},"labels":[],"label_agreement":null},{"id":"W2483786840","doi":"10.4018/978-1-59904-982-3.ch007","title":"Genetic Learning","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Genetic representation; Genetic algorithm; Computer science; Initialization; Artificial intelligence; Population; Quality control and genetic algorithms; Cultural algorithm; Representation (politics); Machine learning; Population-based incremental learning; Meta-optimization; Sociology; Political science","score_opus":0.013013060463683727,"score_gpt":0.23206669493579107,"score_spread":0.21905363447210735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483786840","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000066535436,0.0005064751,0.08508555,0.00017539499,0.000120525845,0.00014971867,0.000007887981,0.0002847999,0.91366297],"genre_scores_gemma":[0.02466433,0.000032307347,0.13625675,0.001066798,0.0008495604,0.000031695174,0.0000051768106,0.000039049704,0.8370543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987542,0.0000098489745,0.0002315618,0.000485155,0.00027920032,0.00024005138],"domain_scores_gemma":[0.99909985,0.000015528389,0.00013284943,0.00055467035,0.00007461802,0.00012248248],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004528673,0.00024342463,0.00019981507,0.000038811784,0.00018725132,0.00010381572,0.0007708234,0.00020132071,0.000015618625],"category_scores_gemma":[0.0000034123918,0.0002571197,0.00013553497,0.000022793132,0.000044749562,0.0000470833,0.00019835155,0.00027914642,0.0005426293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.2809154e-7,0.0000041267126,0.0000027087845,0.0000022576985,0.000010355794,0.000019562549,0.00000911625,0.000065315144,0.0000029292555,0.88761246,0.0012612562,0.11100946],"study_design_scores_gemma":[0.000076744764,0.000056178804,0.00027562754,0.000032518867,0.000011414298,0.00007449189,5.7001733e-7,0.0020091287,0.0000022101137,0.7043104,0.29288855,0.0002621449],"about_ca_topic_score_codex":0.000012699717,"about_ca_topic_score_gemma":0.0000045917004,"teacher_disagreement_score":0.2916273,"about_ca_system_score_codex":0.00012209015,"about_ca_system_score_gemma":0.00014000318,"threshold_uncertainty_score":0.9999881},"labels":[],"label_agreement":null},{"id":"W2484895019","doi":"","title":"Evolutionary optimization of logic-oriented systems","year":2001,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Genetic programming; Artificial intelligence; Curse of dimensionality; Fuzzy logic; Parametric statistics; Artificial neural network; Evolutionary algorithm; Evolutionary computation; Theoretical computer science; Mathematics","score_opus":0.02059902467514501,"score_gpt":0.2418151764635243,"score_spread":0.22121615178837928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484895019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008987208,0.0019092568,0.986086,0.00084966794,0.0002743473,0.0003129439,0.00001555255,0.00014441545,0.0014205711],"genre_scores_gemma":[0.6714185,0.00044440778,0.32772768,0.000047375986,0.00006114208,0.00004341369,0.00005739626,0.0000063469483,0.00019374584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838644,0.0001020939,0.0004705437,0.00046030345,0.00034290957,0.00023769392],"domain_scores_gemma":[0.99864924,0.00010717841,0.00022311283,0.00029035058,0.0006061181,0.00012399247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011370043,0.0001743123,0.0002011054,0.00017483043,0.00029774322,0.000049499064,0.00031176998,0.000090730275,0.000033772718],"category_scores_gemma":[0.000024140196,0.00018097168,0.000045918016,0.0006282838,0.00017312268,0.00039771554,0.00017117546,0.00009206222,0.000018386909],"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":[0.000010440347,0.00014311544,0.0034018601,0.000023886025,0.000020521251,0.000004740888,0.00013559524,0.7749737,0.00008602939,0.21405028,0.0011077665,0.006042071],"study_design_scores_gemma":[0.00031845324,0.00009545676,0.07196319,0.000028662927,0.000009894737,0.00018524895,0.00008935813,0.91270214,0.0000031302607,0.012886163,0.0015363268,0.00018196099],"about_ca_topic_score_codex":0.00006093222,"about_ca_topic_score_gemma":8.498787e-7,"teacher_disagreement_score":0.6624313,"about_ca_system_score_codex":0.000055382323,"about_ca_system_score_gemma":0.00019165044,"threshold_uncertainty_score":0.7379811},"labels":[],"label_agreement":null},{"id":"W2488334571","doi":"10.4018/978-1-59904-705-8.ch012","title":"Parallelizing Genetic Algorithms","year":2008,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Task (project management); Convergence (economics); Premature convergence; Parallelism (grammar); Process (computing); Algorithm; Genetic algorithm; Theoretical computer science; Parallel computing; Machine learning; Programming language","score_opus":0.022489245069433002,"score_gpt":0.24354347906873533,"score_spread":0.22105423399930232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488334571","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005561491,0.0017097007,0.1803654,0.00018173816,0.00030150992,0.00026227147,0.000042760803,0.00031450912,0.81681657],"genre_scores_gemma":[0.006998615,0.0003980053,0.6306232,0.0016345036,0.0016857554,0.00015156098,0.0000162979,0.00010045878,0.35839158],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980216,0.000011401825,0.0003915688,0.000745513,0.0004520842,0.0003778176],"domain_scores_gemma":[0.9984475,0.000025355012,0.00018948442,0.00101369,0.000120440134,0.000203551],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000045286622,0.00038925803,0.0003258003,0.000059320857,0.00029344248,0.00009700817,0.0012134827,0.00029865192,0.000017030245],"category_scores_gemma":[0.0000031716984,0.00040759807,0.00022860369,0.000036328653,0.00011726519,0.00008089605,0.0004220974,0.00026769063,0.0006720913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.41211e-7,0.0000092365835,0.0000023507603,0.0000050865456,0.000031946012,0.00009356089,0.000027752785,0.0000348477,0.0000020524071,0.9511898,0.010585656,0.038016967],"study_design_scores_gemma":[0.00023385584,0.00006303615,0.00021686578,0.000066072935,0.000023093588,0.00060563645,0.0000014456963,0.0059448974,0.000006365628,0.5376064,0.45456466,0.00066764216],"about_ca_topic_score_codex":0.000041460637,"about_ca_topic_score_gemma":0.000007809185,"teacher_disagreement_score":0.45842496,"about_ca_system_score_codex":0.00019029727,"about_ca_system_score_gemma":0.00026902434,"threshold_uncertainty_score":0.9998376},"labels":[],"label_agreement":null},{"id":"W2489986895","doi":"10.1145/2908961.2931655","title":"On Synergies between Diversity and Task Decomposition in Constructing Complex Systems with GP","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Diversity (politics); Computer science; Task (project management); Context (archaeology); Decomposition; Genetic programming; Flexibility (engineering); Population; Reuse; Coevolution; Artificial intelligence; Knowledge management; Data science; Systems engineering; Mathematics; Engineering; Ecology; Biology","score_opus":0.020270269421836545,"score_gpt":0.23789670143369424,"score_spread":0.2176264320118577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489986895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51982045,0.000007736857,0.47846985,0.0005655161,0.000015690326,0.00007887202,0.0000049088944,0.000053563865,0.0009833849],"genre_scores_gemma":[0.97728544,0.0000030853175,0.022626666,0.00002435433,0.000015532929,0.0000052915398,0.0000013022523,0.0000015530999,0.000036779984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99950445,0.00002339329,0.00008374584,0.00018976591,0.00009884648,0.00009977465],"domain_scores_gemma":[0.99958366,0.00018084564,0.000036252844,0.00013616365,0.000026010253,0.000037047295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007440285,0.000056621408,0.00007933026,0.000052494783,0.0002833379,0.000030549974,0.00015432895,0.000018923252,0.0000021094725],"category_scores_gemma":[0.0000028004936,0.000035507295,0.000007415838,0.000115786046,0.00006195514,0.0002489244,0.00027123056,0.000029680885,0.000005985361],"study_design_candidate":"observational","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":[0.0000051445004,0.000038359325,0.22588588,0.000011054562,0.000016524378,0.000005196318,0.00030999558,0.00015465546,0.0009813842,0.7603488,0.00026695876,0.011976052],"study_design_scores_gemma":[0.0014618669,0.00023752614,0.9559388,0.00022251453,0.000009908434,0.00007094658,0.00034030218,0.022859275,0.0002829492,0.017867748,0.00031716397,0.00039100804],"about_ca_topic_score_codex":0.00013193919,"about_ca_topic_score_gemma":0.000011051059,"teacher_disagreement_score":0.74248105,"about_ca_system_score_codex":0.000032249423,"about_ca_system_score_gemma":0.000008554561,"threshold_uncertainty_score":0.21792348},"labels":[],"label_agreement":null},{"id":"W2491583902","doi":"","title":"Simulating complex dynamical systems in a distributed programming environment","year":2015,"lang":"en","type":"other","venue":"ANU Open Research (Australian National University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multiset; Computer science; Theoretical computer science; Dynamical systems theory; Swarm intelligence; Genetic programming; Grid; Computation; Distributed computing; Grid computing; Artificial intelligence; Algorithm; Particle swarm optimization; Mathematics","score_opus":0.1634572879478864,"score_gpt":0.3680877206618292,"score_spread":0.20463043271394282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491583902","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001040876,0.00015034773,0.06776055,0.007548698,0.00020139464,0.007254556,0.0026456704,0.00041003583,0.91392463],"genre_scores_gemma":[0.01269758,0.000025485217,0.028501954,0.000013249878,0.00015225346,0.00003892486,0.0021212609,0.00007732429,0.95637196],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972389,0.00032404313,0.00020044847,0.00062241993,0.0011547408,0.00045947538],"domain_scores_gemma":[0.99891096,0.00013352177,0.000115143084,0.00037313177,0.0002391454,0.00022811604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093948544,0.00017106677,0.00022542088,0.0006481936,0.00019932279,0.0003056531,0.0023185406,0.00019185674,0.00018517426],"category_scores_gemma":[0.000047137008,0.00019616207,0.00003863812,0.0015411121,0.00017913636,0.00038400188,0.0012965347,0.000485237,0.00024139463],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009553984,0.00038109356,0.00019671184,0.000042256353,0.000043546195,0.00014927026,0.000051700015,0.004124792,0.000011906222,0.25968134,0.7344202,0.0008876651],"study_design_scores_gemma":[0.0005111571,0.000040182607,0.00047084448,0.000081810984,0.0000026377313,0.0000051285124,0.00012960614,0.04704049,2.9744038e-7,0.001111447,0.9504004,0.00020600972],"about_ca_topic_score_codex":0.0024879568,"about_ca_topic_score_gemma":0.00018798918,"teacher_disagreement_score":0.2585699,"about_ca_system_score_codex":0.0011012193,"about_ca_system_score_gemma":0.00051447883,"threshold_uncertainty_score":0.79992574},"labels":[],"label_agreement":null},{"id":"W2496664933","doi":"10.1007/978-3-319-41192-7_7","title":"Estimation of Distribution Algorithms","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"EDAS; Estimation of distribution algorithm; Implementation; Evolutionary computation; Computation; Computer science; Evolutionary algorithm; Artificial intelligence; Algorithm; Mathematical optimization; Machine learning; Mathematics","score_opus":0.014147139335641925,"score_gpt":0.23897915676466613,"score_spread":0.2248320174290242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496664933","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.0698708e-7,0.00007231371,0.70760566,0.00054696633,0.0000818524,0.00010562949,0.00009658028,0.00008201793,0.29140857],"genre_scores_gemma":[0.0028323291,0.00014528685,0.17315896,0.000047726917,0.00018476672,0.00002878617,0.00027507977,0.000020109905,0.823307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990903,0.0000043962823,0.00027746096,0.00028478447,0.00023779817,0.00010525211],"domain_scores_gemma":[0.9990585,0.00005344805,0.00019935574,0.00051997486,0.00011908525,0.000049650484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008602314,0.00014168485,0.00016152089,0.00005086028,0.00006287551,0.000017863053,0.000437295,0.0001411355,0.00013572791],"category_scores_gemma":[0.0000061464716,0.000110367306,0.000091993315,0.00003655048,0.0000654042,0.00021491508,0.00013893157,0.000076611264,0.00025837286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.2578507e-7,0.000007842549,1.4418873e-7,0.000004779555,0.0000075920184,3.6637147e-7,0.0000032531075,0.000017798748,0.000009785241,0.79478645,0.0022324286,0.20292932],"study_design_scores_gemma":[0.00015462171,0.00005511376,0.00005264426,0.00010712,0.000015194075,0.000014222201,5.0156274e-7,0.12561046,0.00037934072,0.69924283,0.17407876,0.000289171],"about_ca_topic_score_codex":0.0000029430976,"about_ca_topic_score_gemma":4.0699342e-7,"teacher_disagreement_score":0.5344467,"about_ca_system_score_codex":0.00006244938,"about_ca_system_score_gemma":0.00007259595,"threshold_uncertainty_score":0.4500648},"labels":[],"label_agreement":null},{"id":"W2497386324","doi":"10.4018/978-1-59140-941-1.ch007","title":"Fuzzy Logic Classifiers and Models in Quantitative Software Engineering","year":2007,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Software; Transparency (behavior); Artificial intelligence; Software sizing; Exploit; Software construction; Software quality; Machine learning; Software engineering; Data mining; Software development; Theoretical computer science; Programming language","score_opus":0.045611369903866744,"score_gpt":0.2688839482030021,"score_spread":0.22327257829913535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2497386324","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025869718,0.00071128254,0.48411942,0.000053908556,0.00009877128,0.00018929665,0.000034173783,0.00013434891,0.51463294],"genre_scores_gemma":[0.035326295,0.00006627632,0.9425996,0.0006836406,0.00017659146,0.0000706874,0.000010690726,0.00005801536,0.021008216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99874794,0.00000556781,0.00026733882,0.00049215544,0.00021647854,0.0002705102],"domain_scores_gemma":[0.99931973,0.00006872463,0.00009296419,0.0003364577,0.000064800064,0.00011731534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011388841,0.0002604106,0.00024654402,0.00011567139,0.00006839929,0.00006246175,0.00039691885,0.00024833975,0.0000011611763],"category_scores_gemma":[0.000011096951,0.0002737635,0.000067818444,0.00004644861,0.000070549664,0.00013753967,0.00022152349,0.00026969909,0.000020987378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020568218,0.0000051891916,0.0000054674424,0.000011670079,0.000012975478,0.00003073586,0.000058957172,0.0010052688,0.0000016720263,0.99478966,0.000087330096,0.0039890427],"study_design_scores_gemma":[0.00014918594,0.000041976615,0.00013764616,0.00009126108,0.0000069731777,0.00003274713,0.000007294817,0.0338596,0.0000012297282,0.9627423,0.002615588,0.00031414707],"about_ca_topic_score_codex":0.000036545236,"about_ca_topic_score_gemma":0.000033618402,"teacher_disagreement_score":0.49362472,"about_ca_system_score_codex":0.00018633928,"about_ca_system_score_gemma":0.00009153502,"threshold_uncertainty_score":0.99997145},"labels":[],"label_agreement":null},{"id":"W2504494579","doi":"10.1145/2908812.2908887","title":"Discovering Rubik's Cube Subgroups using Coevolutionary GP","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reuse; Task (project management); Computer science; Genetic programming; Reinforcement learning; Cube (algebra); Population; Decomposition; Process (computing); Genetic algorithm; Sequence (biology); Theoretical computer science; Artificial intelligence; Machine learning; Mathematics; Programming language; Engineering","score_opus":0.01973406502928331,"score_gpt":0.24889071332298685,"score_spread":0.22915664829370352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504494579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039885767,0.00009024651,0.95433044,0.0025156115,0.00015783502,0.00008600021,0.000005871006,0.00020403409,0.0027242065],"genre_scores_gemma":[0.81079125,0.000022401402,0.18711194,0.0001412923,0.00013840392,0.000016591346,0.0000014581271,0.000007367669,0.0017692986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907494,0.000018757342,0.00016199598,0.0003171951,0.00018558605,0.00024152685],"domain_scores_gemma":[0.9993014,0.00007166879,0.000043573542,0.0004494472,0.000049074053,0.00008488201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000796589,0.00009834475,0.000081597056,0.000056251545,0.00023380511,0.00004763235,0.00049487775,0.000035306864,0.00007568518],"category_scores_gemma":[0.000009601542,0.000066177905,0.000053550477,0.00027181813,0.00006004201,0.0009710053,0.0002833625,0.000041811167,0.00016288096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001557079,0.00009117107,0.0025638328,0.0000034451834,0.000011740968,0.0000045654956,0.000057470086,0.00026995232,0.027693048,0.95519257,0.001981764,0.012128884],"study_design_scores_gemma":[0.0012518936,0.00010831855,0.116391174,0.00012329109,0.000017106486,0.00030833198,0.00009730056,0.6952281,0.0056294748,0.11760614,0.06207763,0.0011612371],"about_ca_topic_score_codex":0.0000693352,"about_ca_topic_score_gemma":0.000005767472,"teacher_disagreement_score":0.8375864,"about_ca_system_score_codex":0.000098334815,"about_ca_system_score_gemma":0.000066096996,"threshold_uncertainty_score":0.26986566},"labels":[],"label_agreement":null},{"id":"W2516001042","doi":"10.1007/978-3-319-45823-6_95","title":"Tutorials at PPSN 2016","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Range (aeronautics); Field (mathematics); Computation; Operations research; Data science; Programming language; Engineering; Aerospace engineering","score_opus":0.014427402573505771,"score_gpt":0.24379239592993415,"score_spread":0.22936499335642838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516001042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010575025,0.0006160178,0.9787727,0.0027255132,0.0028420906,0.00033909452,0.00001836315,0.00019752666,0.014478144],"genre_scores_gemma":[0.014647745,0.00030317038,0.94542706,0.0025066424,0.0056079575,0.000077496225,0.000012741416,0.00008555319,0.031331662],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99638844,0.000025059584,0.0004991511,0.0015655141,0.00088426546,0.00063758116],"domain_scores_gemma":[0.99697316,0.00054111617,0.0002810959,0.0017515603,0.0002416513,0.00021143878],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006677471,0.00044584894,0.00042160647,0.00045482436,0.00045705336,0.00026001938,0.0034859418,0.0002998457,0.0000989929],"category_scores_gemma":[0.00005164418,0.00034162504,0.00014274986,0.00039257284,0.0006938111,0.00060111267,0.0020754554,0.00035333793,0.0006736382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030142696,0.000029698196,0.00001428241,0.000012659949,0.000010793549,0.000033988534,0.00015894124,0.0012113659,0.00056078774,0.28156117,0.0008971617,0.71550614],"study_design_scores_gemma":[0.0004127126,0.0001312127,0.00012721625,0.00043410764,0.000008364802,0.00012341789,2.7487442e-8,0.06550598,0.0021419846,0.79703444,0.13300994,0.0010705935],"about_ca_topic_score_codex":0.000008099685,"about_ca_topic_score_gemma":0.000016241303,"teacher_disagreement_score":0.7144355,"about_ca_system_score_codex":0.0005913619,"about_ca_system_score_gemma":0.0006406288,"threshold_uncertainty_score":0.99990356},"labels":[],"label_agreement":null},{"id":"W2519194527","doi":"10.22215/etd/2007-06411","title":"Towards scalable genetic programming","year":2007,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage","funders":"","keywords":"Computer science; Humanities; Art","score_opus":0.01269992822284226,"score_gpt":0.2913360969828676,"score_spread":0.27863616876002534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519194527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014406706,0.00072159444,0.8822665,0.00018083114,0.0005182173,0.00035364955,0.0000014384611,0.00038022015,0.114136875],"genre_scores_gemma":[0.0038000098,0.000054433083,0.9313601,0.00009502418,0.00019525272,0.00013412858,0.00017289836,0.000018506935,0.06416964],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876475,0.000008626001,0.00023612086,0.00041533288,0.00029810012,0.0002770507],"domain_scores_gemma":[0.9991951,0.000016627582,0.00008865671,0.00046218038,0.00014502663,0.00009242451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011634748,0.00015959598,0.00013160317,0.00012693138,0.00016889782,0.0001429175,0.000743621,0.00016506779,0.000073174546],"category_scores_gemma":[0.0000067531914,0.00015030037,0.00007775555,0.000488872,0.000013074214,0.00014725942,0.000052214717,0.00016614253,0.00021785543],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011093516,0.000089796886,0.000022064478,0.0000349049,0.000014196197,0.000006386362,0.00016386174,0.000018339788,0.00006067559,0.08898591,0.0028187037,0.90778404],"study_design_scores_gemma":[0.00054877443,0.0002164184,0.069298394,0.00018103722,0.000068438094,0.000075835385,0.00058432843,0.085919075,0.0052146385,0.040554866,0.7953593,0.001978851],"about_ca_topic_score_codex":0.00012437717,"about_ca_topic_score_gemma":0.00007674151,"teacher_disagreement_score":0.9058052,"about_ca_system_score_codex":0.000044427,"about_ca_system_score_gemma":0.00018634985,"threshold_uncertainty_score":0.6129071},"labels":[],"label_agreement":null},{"id":"W2528200391","doi":"","title":"System Closure in an Integrated Newsprint Mill, Practical Application of the Genetic Algorithm","year":2004,"lang":"en","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Mathematics; Computer science; Algorithm; Philosophy","score_opus":0.008216843736830145,"score_gpt":0.23900849732914048,"score_spread":0.23079165359231033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528200391","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015546655,0.0008654256,0.9927332,0.0018140604,0.00013425467,0.0017542841,0.0000872061,0.00071750005,0.0017386354],"genre_scores_gemma":[0.016689513,0.00011921543,0.9764725,0.00026594452,0.00019615405,0.0015307517,0.00004632753,0.00021588603,0.0044637113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974514,0.0002382021,0.00063399074,0.00074517267,0.00047450213,0.00045668893],"domain_scores_gemma":[0.9968266,0.000051599145,0.0006021731,0.0022065379,0.00012767049,0.00018545268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037050553,0.00039610727,0.00044143232,0.00045292985,0.000114662354,0.00009953722,0.001708283,0.00059218035,0.000011311855],"category_scores_gemma":[0.000032893502,0.00032669955,0.00016739387,0.0013064152,0.00013745372,0.00019353547,0.00039412276,0.0006566258,0.000018783454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019745245,0.0021538239,0.009579575,0.0003714822,0.00011857921,0.00007656361,0.00037844482,0.010321564,0.0010353418,0.719285,0.01991697,0.23674291],"study_design_scores_gemma":[0.0006257869,0.00012806243,0.039020307,0.0004765023,0.000047864763,0.00026414078,0.00011433841,0.89925677,0.0006158269,0.0029365756,0.055797506,0.0007163465],"about_ca_topic_score_codex":0.026193598,"about_ca_topic_score_gemma":0.0059946706,"teacher_disagreement_score":0.8889352,"about_ca_system_score_codex":0.00077166205,"about_ca_system_score_gemma":0.00088859873,"threshold_uncertainty_score":0.9999185},"labels":[],"label_agreement":null},{"id":"W253016372","doi":"10.1007/978-3-540-87700-4_45","title":"Nonsynonymous to Synonymous Substitution Ratio $k_{\\mathrm a}/k_{\\mathrm s}$ : Measurement for Rate of Evolution in Evolutionary Computation","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Nonsynonymous substitution; Substitution (logic); Rate of evolution; Synonymous substitution; Evolutionary algorithm; Interactive evolutionary computation; Computation; Evolutionary computation; Population; Computer science; Sequence (biology); Evolution strategy; Algorithm; Evolutionary programming; Biology; Artificial intelligence; Gene; Genetics; Phylogenetics; Genome; Codon usage bias","score_opus":0.027453539716488923,"score_gpt":0.25228110349356647,"score_spread":0.22482756377707755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W253016372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085764285,0.0011440681,0.99349594,0.0007322113,0.0011074487,0.0022051572,0.000047559857,0.00012308195,0.00028686607],"genre_scores_gemma":[0.43548876,0.000076900746,0.5635245,0.00024593275,0.0003288051,0.00017484136,0.000034485955,0.000040557486,0.00008527003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948309,0.000084652296,0.0012486303,0.0017378564,0.0013270902,0.0007708617],"domain_scores_gemma":[0.99632823,0.0003970503,0.000599789,0.0010634686,0.0013856357,0.00022581629],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015690869,0.00063079287,0.00075062696,0.0016187531,0.0005328639,0.00012676387,0.0018479022,0.00038514507,0.0000049357136],"category_scores_gemma":[0.00018375523,0.0006729768,0.00022080314,0.0015451987,0.0006033378,0.00083640055,0.0005784591,0.0005049144,0.000030644882],"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":[0.000044750606,0.0003518182,0.00006797161,0.00012399227,0.000025248513,0.000026014612,0.00080679933,0.87892634,0.0031016918,0.063889086,0.00013954287,0.052496724],"study_design_scores_gemma":[0.0006936064,0.00037631148,0.0015215282,0.00057053193,0.000019093497,0.000112199996,0.0000011645172,0.92917925,0.00083645876,0.06539291,0.0005623642,0.00073456665],"about_ca_topic_score_codex":0.00018069231,"about_ca_topic_score_gemma":0.00020260553,"teacher_disagreement_score":0.4346311,"about_ca_system_score_codex":0.002423439,"about_ca_system_score_gemma":0.00212614,"threshold_uncertainty_score":0.99957216},"labels":[],"label_agreement":null},{"id":"W2538348493","doi":"10.1109/mutation.2006.7","title":"Finding Sufficient Mutation Operators via Variable Reduction","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Reduction (mathematics); Mutation; Variable (mathematics); Computer science; Carry (investment); Mutation testing; Algorithm; Mathematics; Programming language","score_opus":0.008100574292593496,"score_gpt":0.22619814370027033,"score_spread":0.21809756940767683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538348493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023021441,0.000023439125,0.96664435,0.00058204524,0.00020159392,0.00010278053,7.196576e-7,0.00017184859,0.009251773],"genre_scores_gemma":[0.6801386,8.7799054e-7,0.3177071,0.000035594952,0.00011179433,0.000027578233,0.0000131912875,0.0000031809545,0.0019620971],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993781,0.000014079811,0.00013568465,0.00021578613,0.0001264871,0.00012986112],"domain_scores_gemma":[0.99966514,0.000013172779,0.000033461798,0.00019807361,0.000063032465,0.000027095753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118641765,0.00005653797,0.000043522385,0.000060538157,0.00024037696,0.00008386012,0.00018167903,0.000027342492,0.000044197575],"category_scores_gemma":[0.000002660328,0.000052969306,0.00001822061,0.0005152641,0.00001472948,0.00037661,0.00004372786,0.000043026896,0.00008922707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.198615e-7,0.0001169176,0.000057244808,0.0000016928564,0.0000021026647,8.939722e-7,0.00006112528,0.02417423,0.00899459,0.9602483,0.0034723752,0.0028701946],"study_design_scores_gemma":[0.00014944539,0.000031348445,0.003418609,0.0000041227167,0.0000032749713,0.00007185212,0.00004629558,0.95662034,0.006651042,0.02590058,0.0069403728,0.00016270406],"about_ca_topic_score_codex":0.000249464,"about_ca_topic_score_gemma":0.0000023088282,"teacher_disagreement_score":0.93434775,"about_ca_system_score_codex":0.00005450039,"about_ca_system_score_gemma":0.000035146622,"threshold_uncertainty_score":0.21600257},"labels":[],"label_agreement":null},{"id":"W2553828788","doi":"10.1109/ijcnn.2016.7727774","title":"Contribution of data complexity features on dynamic classifier selection","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Classifier (UML); Computer science; Machine learning; Artificial intelligence; Data mining; Test data; Quadratic classifier; Pattern recognition (psychology)","score_opus":0.04500460927729832,"score_gpt":0.30672469147853704,"score_spread":0.2617200822012387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553828788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032244776,0.000016072634,0.9895599,0.005455111,0.000060188402,0.00010227071,0.000042356256,0.00009268744,0.0014469766],"genre_scores_gemma":[0.94915545,0.000011915452,0.049810357,0.00008915965,0.00002563431,0.0000067936194,0.00003202386,0.0000021402332,0.0008665388],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99934876,0.000028107372,0.00011647152,0.00026183354,0.00013858642,0.00010626713],"domain_scores_gemma":[0.99915934,0.000081637045,0.000058787784,0.00056015427,0.000108369655,0.000031710166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013595544,0.000055505443,0.00006977819,0.000039681112,0.0000902672,0.000014605504,0.00057928707,0.000035264882,0.000032798],"category_scores_gemma":[0.000024390381,0.000034817556,0.000018673361,0.00019828405,0.0000586515,0.0004192522,0.00015888954,0.000041619514,0.00003920121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044777717,0.000090559406,0.00021809513,0.0000011499876,0.000007717367,1.1387853e-7,0.0000047723042,0.0000048843526,0.00910482,0.9467466,0.006532342,0.037284464],"study_design_scores_gemma":[0.0011504046,0.00027073323,0.42638832,0.000049240443,0.000011766452,0.00002934289,0.000008746796,0.3796106,0.009467702,0.15485744,0.027827505,0.0003282035],"about_ca_topic_score_codex":0.00002436555,"about_ca_topic_score_gemma":0.000060109363,"teacher_disagreement_score":0.94593096,"about_ca_system_score_codex":0.000044383953,"about_ca_system_score_gemma":0.000032659304,"threshold_uncertainty_score":0.14198187},"labels":[],"label_agreement":null},{"id":"W2559615222","doi":"10.1109/cec.2016.7743875","title":"evoVersion: Visualizing evolutionary histories","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Evolutionary computation; Game engine; Visualization; Interactive evolutionary computation; Data visualization; Evolutionary algorithm; Human-based evolutionary computation; Virtual reality; Field (mathematics); Human–computer interaction; Evolutionary programming; Artificial intelligence","score_opus":0.013577006584768457,"score_gpt":0.24491652655105875,"score_spread":0.2313395199662903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559615222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048031702,0.00019323805,0.96964,0.011613826,0.00024987254,0.000051789266,0.0000019302493,0.00029394004,0.01747505],"genre_scores_gemma":[0.682511,0.00004767818,0.29131278,0.0005087429,0.00021883984,0.000038854407,0.0000017776018,0.000007456143,0.02535286],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993426,0.000014681621,0.000105114246,0.00022789596,0.0001654884,0.00014417098],"domain_scores_gemma":[0.9994306,0.000076392535,0.00002975024,0.00033384122,0.00006611569,0.00006329188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000064643675,0.00006327808,0.00005265959,0.00004376466,0.00019644425,0.000017831038,0.0003822949,0.000027329499,0.00015740654],"category_scores_gemma":[0.000014406943,0.000041012332,0.000037612535,0.00020226282,0.000045630077,0.0006313176,0.0001638622,0.000023591221,0.0003162205],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.834112e-7,0.000027931583,0.00032291914,8.579461e-7,0.000002827719,0.0000011960398,0.000038465605,0.0000011766548,0.0014411089,0.9440729,0.042021934,0.01206808],"study_design_scores_gemma":[0.0002906588,0.000046479287,0.007903857,0.00001338833,0.0000024482338,0.00003405055,0.000031389216,0.0051703495,0.0008850931,0.054506026,0.93088734,0.0002289492],"about_ca_topic_score_codex":0.000009948331,"about_ca_topic_score_gemma":9.1594666e-7,"teacher_disagreement_score":0.8895669,"about_ca_system_score_codex":0.00012124373,"about_ca_system_score_gemma":0.00005466723,"threshold_uncertainty_score":0.40644804},"labels":[],"label_agreement":null},{"id":"W2570629401","doi":"10.1186/s13029-016-0061-y","title":"Erratum to: A bedr way of genomic interval processing","year":2017,"lang":"en","type":"erratum","venue":"Source Code for Biology and Medicine","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Computer science; Interval (graph theory); Data science; Information retrieval; Data mining; Mathematics","score_opus":0.025984224497836828,"score_gpt":0.3223325845965977,"score_spread":0.29634836009876087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570629401","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007598919,0.012584258,0.95382804,0.016314285,0.00785736,0.00077921967,0.00018230501,0.00009487902,0.0075997524],"genre_scores_gemma":[0.107910134,0.00301954,0.117991954,0.0049062776,0.017123573,0.0011534379,0.0024882897,0.00016190237,0.74524486],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99885327,0.000024526116,0.0003069008,0.00048756946,0.00007144686,0.0002562754],"domain_scores_gemma":[0.9988334,0.000066578534,0.00032208537,0.00050783734,0.00014973685,0.00012033918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003405068,0.00019644148,0.0004816541,0.00013331394,0.00033640052,0.000014696498,0.000887071,0.00032747156,0.000005460754],"category_scores_gemma":[0.000119983524,0.0001491757,0.00006134703,0.00006951946,0.0004258635,0.0000523809,0.00030269005,0.0003071976,0.000003841187],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021488402,0.000042492415,0.00006694301,0.00026839407,0.000055783785,0.0000010221949,0.0013873775,0.000004308969,0.001333456,0.009551993,0.89056563,0.0967011],"study_design_scores_gemma":[0.0003895931,0.0005748918,0.00088220864,0.00039940595,0.000042866275,0.000018852457,0.000057590158,0.0079983575,0.000018233868,0.010327981,0.97911215,0.00017789764],"about_ca_topic_score_codex":0.000032546126,"about_ca_topic_score_gemma":0.000034685367,"teacher_disagreement_score":0.8358361,"about_ca_system_score_codex":0.00002098539,"about_ca_system_score_gemma":0.00015776623,"threshold_uncertainty_score":0.60832083},"labels":[],"label_agreement":null},{"id":"W2586650997","doi":"","title":"Reactive Agents Learn to Add Epistemic Structures to the World","year":2004,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Max-Planck-Institut für Bildungsforschung; Strong","keywords":"Task (project management); Cognition; Computer science; Action (physics); Function (biology); Term (time); Artificial intelligence; Cognitive science; Psychology; Engineering","score_opus":0.01565578062012701,"score_gpt":0.2370991400940564,"score_spread":0.22144335947392937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586650997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43602696,0.00030363162,0.31390285,0.18288346,0.0008319435,0.0030926485,0.0033383674,0.0021237233,0.05749643],"genre_scores_gemma":[0.9581731,0.0000014060039,0.031288087,0.0058208704,0.00024370548,0.000116360236,0.00010570536,0.000037768994,0.004212993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818987,0.000042806794,0.00029653995,0.0005970642,0.0003996528,0.00047408039],"domain_scores_gemma":[0.9984976,0.00009985258,0.00007685082,0.0008327335,0.0000392383,0.0004536898],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000114484326,0.00023593992,0.00015938452,0.00018536166,0.00035963856,0.0014461492,0.0017820392,0.000055338427,0.000092508395],"category_scores_gemma":[0.0000966261,0.0001775009,0.00011176454,0.0014923094,0.000045650533,0.002829211,0.0007837597,0.0003629273,0.0050217295],"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":[0.0001086389,0.0006551098,0.014004283,0.000034383753,0.00014355833,0.00008733599,0.00081224163,0.012537674,0.00078483677,0.77034026,0.12318677,0.07730491],"study_design_scores_gemma":[0.0002907971,0.000087462904,0.026531145,0.000052862553,0.00000552784,0.000022838263,0.00003223617,0.00026492466,0.0019389704,0.15708715,0.8132601,0.00042600333],"about_ca_topic_score_codex":0.000008654102,"about_ca_topic_score_gemma":0.000011279229,"teacher_disagreement_score":0.6900733,"about_ca_system_score_codex":0.000092670125,"about_ca_system_score_gemma":0.00012065608,"threshold_uncertainty_score":0.99959046},"labels":[],"label_agreement":null},{"id":"W2601435252","doi":"10.1371/journal.pone.0174635","title":"Improving HybrID: How to best combine indirect and direct encoding in evolutionary algorithms","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Wyoming; Canadian Centre for Applied Research in Cancer Control; National Science Foundation","keywords":"Exploit; Encoding (memory); Computer science; Offset (computer science); Reuse; Algorithm; Evolutionary algorithm; Artificial intelligence; Engineering","score_opus":0.04094853997077438,"score_gpt":0.24069204830921767,"score_spread":0.1997435083384433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601435252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90347064,0.0010872949,0.066840544,0.02059961,0.00022238422,0.0011762445,0.000062092,0.00033521088,0.0062059923],"genre_scores_gemma":[0.8412083,0.00007277132,0.15763907,0.0000805441,0.000120569624,0.00011819678,0.000005012525,0.000010197388,0.0007453756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987721,0.000029224893,0.00015552327,0.000476267,0.00027595035,0.00029097602],"domain_scores_gemma":[0.99887526,0.00008573269,0.00010925887,0.0007099333,0.000074805364,0.00014499576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024196686,0.0001339117,0.00021676066,0.00015015631,0.000608284,0.00029245854,0.0007469733,0.000037976602,0.000003852948],"category_scores_gemma":[0.00017091268,0.00014314448,0.000025372276,0.0001639736,0.000066200635,0.0007765512,0.0006184863,0.00015261503,0.00002891265],"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":[0.000050896073,0.014300552,0.21770911,0.0005147499,0.00052405574,0.00038259654,0.003012687,0.00018716276,0.19818062,0.037867963,0.0043631345,0.5229065],"study_design_scores_gemma":[0.0014757271,0.0004935798,0.2961676,0.00058237143,0.000058148056,0.000046243684,0.00008103662,0.669176,0.024943674,0.004821272,0.001113007,0.0010413796],"about_ca_topic_score_codex":0.00021977906,"about_ca_topic_score_gemma":0.000029872293,"teacher_disagreement_score":0.6689888,"about_ca_system_score_codex":0.00008247142,"about_ca_system_score_gemma":0.0000507018,"threshold_uncertainty_score":0.5837262},"labels":[],"label_agreement":null},{"id":"W2604908377","doi":"10.1007/978-3-319-55696-3_5","title":"Emergent Tangled Graph Representations for Atari Game Playing Agents","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Graph; Benchmarking; Machine learning; Theoretical computer science","score_opus":0.03932165180850437,"score_gpt":0.3050081953230138,"score_spread":0.26568654351450943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604908377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034143333,0.00027192888,0.99273235,0.001946585,0.0016044449,0.0008350345,0.00003778447,0.00012896807,0.002408783],"genre_scores_gemma":[0.03869313,0.00012405026,0.95696795,0.0007781667,0.0008183808,0.00018982009,0.00006254598,0.000044119824,0.0023218358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673694,0.000015906371,0.00048639486,0.0015144158,0.00068275904,0.00056358625],"domain_scores_gemma":[0.9966446,0.0003122619,0.00041296362,0.0021279347,0.00031747174,0.00018477859],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004904635,0.00038782618,0.00036683472,0.0005236796,0.001068921,0.0005335151,0.0038477017,0.00020434117,0.00002507305],"category_scores_gemma":[0.000101327445,0.00038170617,0.00021567236,0.00026179955,0.00049999123,0.00070066575,0.001075127,0.0003984983,0.00003384741],"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":[0.000011251528,0.00018527095,0.00028376328,0.000089377514,0.00008475176,0.000065168446,0.0017741014,0.07315631,0.00039295264,0.2866338,0.0017964232,0.63552684],"study_design_scores_gemma":[0.0003078531,0.00007105569,0.0008696315,0.00013443513,0.0000135702,0.000027738826,1.8693208e-7,0.61860484,0.00014956592,0.36972362,0.009589162,0.0005083354],"about_ca_topic_score_codex":0.000046707664,"about_ca_topic_score_gemma":0.00007864015,"teacher_disagreement_score":0.6350185,"about_ca_system_score_codex":0.00017806339,"about_ca_system_score_gemma":0.00038666482,"threshold_uncertainty_score":0.9998635},"labels":[],"label_agreement":null},{"id":"W2611879961","doi":"","title":"Predicting Ten Thousand Bits from Ten Thousand Inputs","year":2006,"lang":"en","type":"report","venue":"UCL Discovery (University College London)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Genetic programming; Task (project management); Code (set theory); Computer science; Series (stratigraphy); Evolutionary computation; Binary number; Computation; Artificial intelligence; Machine learning; Algorithm; Mathematics; Arithmetic; Programming language; Biology; Set (abstract data type); Engineering","score_opus":0.011509050703601226,"score_gpt":0.21513668909123376,"score_spread":0.20362763838763254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611879961","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0897878,0.0037441156,0.57612973,0.0049366085,0.004266682,0.0029262435,0.030935675,0.0020228932,0.28525028],"genre_scores_gemma":[0.26054645,0.004262546,0.0644022,0.000548994,0.004811928,0.000048706475,0.004578924,0.0002849769,0.66051525],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958426,0.00014814716,0.00046618425,0.0015081587,0.0013501449,0.0006847819],"domain_scores_gemma":[0.9969196,0.00029389784,0.00063136383,0.0014646077,0.00044247883,0.00024808498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035836516,0.00060601515,0.0007734927,0.0005420602,0.0011882455,0.00029153668,0.0022630855,0.0005648584,0.000048787617],"category_scores_gemma":[0.000046239657,0.00066637044,0.00038569586,0.0016475895,0.00019708973,0.0025477798,0.0015949842,0.0007321116,0.00009113705],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011511132,0.0014459932,0.043120973,0.00029900752,0.0010993845,0.0026868198,0.00070245616,0.001658873,0.0002846501,0.04190282,0.89928657,0.007397337],"study_design_scores_gemma":[0.0023064811,0.0001817428,0.107066266,0.0005649654,0.0004752983,0.0002306797,0.00033159673,0.036651153,0.000085577754,0.002498989,0.8474883,0.0021189698],"about_ca_topic_score_codex":0.00548373,"about_ca_topic_score_gemma":0.0011421565,"teacher_disagreement_score":0.5117275,"about_ca_system_score_codex":0.00083792495,"about_ca_system_score_gemma":0.0021562127,"threshold_uncertainty_score":0.9995788},"labels":[],"label_agreement":null},{"id":"W2621359109","doi":"10.25088/complexsystems.15.3.183","title":"Evolving Distributed Control for an Object Clustering Task","year":2005,"lang":"en","type":"article","venue":"Complex Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Air Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Task (project management); Cluster analysis; Computer science; Scaling; Key (lock); Object (grammar); Population; Control (management); Distributed computing; Genetic algorithm; Sensitivity (control systems); Constant (computer programming); Robot; Cluster (spacecraft); Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.035838279984958386,"score_gpt":0.277652793157831,"score_spread":0.24181451317287259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621359109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006033366,0.00035294474,0.99665534,0.0006537068,0.00024580187,0.0006465306,0.00013352807,0.00028251688,0.00042630904],"genre_scores_gemma":[0.954829,0.0000011145881,0.044022396,0.00009202614,0.0005689624,0.00028410016,0.00007193204,0.000010137894,0.000120323726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988811,0.000053020627,0.00028729014,0.00034253724,0.00014927797,0.00028677366],"domain_scores_gemma":[0.9989772,0.00011725638,0.00010229284,0.0005386245,0.00014998305,0.00011464939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025500936,0.00012143647,0.00018458109,0.000047137033,0.00035730234,0.00024047284,0.00065817125,0.00004104801,0.0000052145065],"category_scores_gemma":[0.000018399918,0.000118349184,0.000062474486,0.00020193665,0.000024137538,0.0005107578,0.000078031706,0.00005552839,0.000034147644],"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":[0.00003346333,0.00082460244,0.0010421014,0.00030320362,0.00019394296,0.0000055596233,0.00145317,0.27776635,0.037239164,0.5594082,0.080127455,0.041602794],"study_design_scores_gemma":[0.00048691657,0.000055504075,0.0027740418,0.000015369993,0.0000048224542,0.000026061314,0.00003542287,0.9204712,0.00000943449,0.000256278,0.07572147,0.00014350825],"about_ca_topic_score_codex":0.00007811022,"about_ca_topic_score_gemma":0.0000317977,"teacher_disagreement_score":0.95422566,"about_ca_system_score_codex":0.00010093318,"about_ca_system_score_gemma":0.000037688802,"threshold_uncertainty_score":0.48261395},"labels":[],"label_agreement":null},{"id":"W2678408969","doi":"10.1007/978-94-017-7358-4_7-1","title":"Optimization Strategies in Design Space Exploration","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Benchmark (surveying); Computer science; Scalability; Heuristic; Set (abstract data type); Design space exploration; Software; Design of experiments; Convergence (economics); Space exploration; Space (punctuation); Mathematical optimization; Machine learning; Artificial intelligence; Engineering; Mathematics","score_opus":0.04272892741566849,"score_gpt":0.2447299224310419,"score_spread":0.2020009950153734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2678408969","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0402691e-8,0.00005754849,0.6233535,0.0014079784,0.000050262428,0.00018018762,0.0000015151555,0.00009310702,0.37485588],"genre_scores_gemma":[0.00016319014,0.0003534495,0.60910535,0.00005082007,0.00009901051,0.00005781792,0.000013265912,0.000016242622,0.39014086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991201,0.000015357205,0.00021289807,0.00035568047,0.00017140184,0.00012452366],"domain_scores_gemma":[0.99931395,0.000064433116,0.00010652416,0.00039218305,0.00008463454,0.000038302955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012216988,0.00016120993,0.00012680958,0.00015784483,0.00006325658,0.00012052733,0.0003659928,0.00014707379,0.00015426599],"category_scores_gemma":[0.000003408411,0.00013314867,0.000035104673,0.000060035058,0.000031221436,0.001252199,0.00008142245,0.00009492709,0.00020014672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.36007e-7,0.00000745099,1.1695598e-7,0.0000021025205,0.0000029095609,0.000001701408,0.00003554481,0.050426282,0.0000075392577,0.94511825,0.0009127443,0.0034846207],"study_design_scores_gemma":[0.00014714545,0.000038516257,0.000003815885,0.00006559599,0.0000031042055,0.0000037289108,0.000013354669,0.29987353,0.00002763716,0.67998755,0.019555412,0.00028059856],"about_ca_topic_score_codex":0.00000428866,"about_ca_topic_score_gemma":0.0000074341583,"teacher_disagreement_score":0.2651307,"about_ca_system_score_codex":0.000077202676,"about_ca_system_score_gemma":0.00017500989,"threshold_uncertainty_score":0.5429645},"labels":[],"label_agreement":null},{"id":"W27021769","doi":"10.1007/s11120-016-0245-y","title":"Real life applications of bio-inspired computing models: EAP and NEPs","year":2013,"lang":"en","type":"dissertation","venue":"Photosynthesis Research","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Natural computing; Von Neumann architecture; Field (mathematics); Programming paradigm; Unconventional computing; Turing machine; Theoretical computer science; Model of computation; Complex system; Parallelism (grammar); Artificial intelligence; Data science; Distributed computing; Computation; Programming language; Parallel computing; Mathematics","score_opus":0.06215257018466191,"score_gpt":0.3455819153192787,"score_spread":0.28342934513461676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W27021769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5007974,0.007211143,0.35543895,0.002223179,0.00039787227,0.0100780595,0.00027269416,0.00096554676,0.12261515],"genre_scores_gemma":[0.9460543,0.0027932038,0.045528144,0.000024762441,0.00024291952,0.0017864641,0.00022643845,0.00006915416,0.0032746305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692607,0.00022485515,0.0005645296,0.0008312723,0.0009447236,0.0005085504],"domain_scores_gemma":[0.9965675,0.00095164264,0.00025301822,0.001072787,0.00087698566,0.00027810503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009775052,0.0002484506,0.00040752869,0.0006176945,0.0006226344,0.0002035083,0.0015218622,0.00027346957,0.00004352822],"category_scores_gemma":[0.000119263794,0.00024971992,0.00010488938,0.0012005394,0.00017865663,0.00035981648,0.0003394354,0.00052475755,0.0000798372],"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":[0.000054613767,0.0013789245,0.00016348733,0.0015970436,0.0003572036,0.000006203944,0.0062014926,0.0004302397,0.046069,0.54370767,0.0067640073,0.39327008],"study_design_scores_gemma":[0.00031246903,0.000105067746,0.0058793966,0.00033674747,0.000036637888,0.0000080013615,0.0017836378,0.92401636,0.022803288,0.039922807,0.004069811,0.00072580774],"about_ca_topic_score_codex":0.0019396483,"about_ca_topic_score_gemma":0.00006129687,"teacher_disagreement_score":0.9235861,"about_ca_system_score_codex":0.00007255786,"about_ca_system_score_gemma":0.00049744063,"threshold_uncertainty_score":0.9999955},"labels":[],"label_agreement":null},{"id":"W2724616504","doi":"","title":"Improving Robustness in Social Fabric-Based Cultural Algorithms with Two New Approaches in Population and Belief Spaces.","year":2017,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Algorithm","score_opus":0.11114109451412298,"score_gpt":0.36274668935608123,"score_spread":0.25160559484195827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724616504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62078696,0.00025278926,0.32516694,0.052232455,0.00010753003,0.0011123344,0.000007617568,0.00009113488,0.00024221318],"genre_scores_gemma":[0.96023285,0.00001721379,0.038860977,0.000060011036,0.00046121585,0.000119843506,0.000010700817,0.000010986797,0.00022619024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820447,0.00013343073,0.00016697068,0.000439619,0.0005717818,0.00048371856],"domain_scores_gemma":[0.9990693,0.00012008583,0.000089687484,0.0005392524,0.00009371093,0.000087978864],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0015147635,0.00013433091,0.00015390849,0.00005728157,0.001746943,0.0007312679,0.0010286595,0.00007453978,0.0000017611168],"category_scores_gemma":[0.000042054533,0.000092235816,0.000053343305,0.0005293322,0.00034394296,0.0009188624,0.00045852506,0.0006352507,0.0000019213983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015806832,0.0009276103,0.2592604,0.0003120381,0.00013273279,0.00004650736,0.037865512,0.07329082,0.0010649754,0.20097907,0.012234379,0.4137279],"study_design_scores_gemma":[0.00089238,0.000029834307,0.22432111,0.000019956911,0.000002096426,0.000003137373,0.00044526224,0.7717037,0.000039452538,0.002304277,0.00010862131,0.00013013776],"about_ca_topic_score_codex":0.007004994,"about_ca_topic_score_gemma":0.00061730255,"teacher_disagreement_score":0.6984129,"about_ca_system_score_codex":0.00019238629,"about_ca_system_score_gemma":0.00023708899,"threshold_uncertainty_score":0.99960744},"labels":[],"label_agreement":null},{"id":"W2729584939","doi":"10.1145/3071178.3071316","title":"Coevolving deep hierarchies of programs to solve complex tasks","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Coevolution; Computer science; Modularity (biology); Task (project management); Genetic programming; Reinforcement learning; Artificial intelligence; Theoretical computer science; Tree (set theory); Code (set theory); Diversity (politics); Machine learning; Programming language","score_opus":0.0344071226984597,"score_gpt":0.26587920758743583,"score_spread":0.23147208488897614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729584939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32842124,0.00018508678,0.66522616,0.003629035,0.00012679277,0.0005522197,0.000008712427,0.000051346513,0.0017994354],"genre_scores_gemma":[0.70022774,0.000020969477,0.29961842,0.000032539076,0.000027036973,0.000027686698,0.0000017062985,0.0000039607753,0.00003994718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99888104,0.000006564758,0.0003194669,0.000313017,0.00029580502,0.00018412255],"domain_scores_gemma":[0.9985543,0.000040235467,0.0003971101,0.0002448834,0.00067515153,0.000088326124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012967449,0.00012794684,0.00018015534,0.0000757152,0.00069100404,0.00013982237,0.0011459695,0.000041091047,0.0000044640647],"category_scores_gemma":[0.00006240872,0.00010784213,0.000056397595,0.0001845033,0.00039001144,0.00033111032,0.0007802884,0.00008125361,0.00000298249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003587107,0.00035490628,0.0645475,0.0003223601,0.00009666264,5.0966344e-7,0.0044700913,0.004707501,0.018845182,0.67362404,0.002638437,0.23035695],"study_design_scores_gemma":[0.00017532197,0.00009440977,0.58161783,0.00007506435,0.000010227844,0.000016396094,0.0001187641,0.3471463,0.00025871472,0.0700822,0.00028155334,0.00012320564],"about_ca_topic_score_codex":0.000057052657,"about_ca_topic_score_gemma":0.000003682546,"teacher_disagreement_score":0.60354185,"about_ca_system_score_codex":0.000025592244,"about_ca_system_score_gemma":0.000095364216,"threshold_uncertainty_score":0.53147143},"labels":[],"label_agreement":null},{"id":"W2732956268","doi":"10.1109/cec.2017.7969310","title":"Lexicase selection promotes effective search and behavioural diversity of solutions in Linear Genetic Programming","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Selection (genetic algorithm); Tournament selection; Genetic programming; Machine learning; Computer science; Fitness proportionate selection; Benchmark (surveying); Artificial intelligence; Tournament; Symbolic regression; Genetic algorithm; Mathematics; Fitness function","score_opus":0.040994639854643355,"score_gpt":0.29455653701659645,"score_spread":0.2535618971619531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732956268","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7902864,0.000025226838,0.20890242,0.00033663746,0.000020704394,0.00032589913,0.0000016386127,0.00002878057,0.00007224348],"genre_scores_gemma":[0.89657277,0.0000045105185,0.10333563,0.000002756712,0.0000111343015,0.000025749685,6.9123115e-7,0.0000015035658,0.000045236917],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99946296,0.000028868928,0.00008222329,0.00018677246,0.00009731234,0.00014186579],"domain_scores_gemma":[0.9996213,0.000026014568,0.00003507636,0.00020358889,0.000071075905,0.000042976626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016207348,0.000049887367,0.00006652862,0.000056373512,0.0008023142,0.00004629127,0.0002565787,0.000030134217,0.0000020502923],"category_scores_gemma":[0.000017289285,0.000047402995,0.000020143181,0.00010949344,0.000108315944,0.00029470577,0.0006270409,0.000080802914,0.0000018888019],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005611472,0.0004793986,0.8105879,0.000022404389,0.000013792509,0.0000067651117,0.0010236161,0.0002377682,0.001471943,0.021803493,0.00001639482,0.1643309],"study_design_scores_gemma":[0.00016860937,0.000085945,0.80425155,0.000007276257,0.0000036241427,0.000012145738,0.000019469875,0.19375592,0.0008151358,0.00082255347,0.000005498574,0.000052252806],"about_ca_topic_score_codex":0.002872764,"about_ca_topic_score_gemma":0.0004419874,"teacher_disagreement_score":0.19351816,"about_ca_system_score_codex":0.000026294376,"about_ca_system_score_gemma":0.000025070593,"threshold_uncertainty_score":0.6170834},"labels":[],"label_agreement":null},{"id":"W2734849651","doi":"10.1145/3067695.3067726","title":"Evolutionary computation in network management and security","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","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":true,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Dalhousie University","keywords":"Citation; Evolutionary computation; Computer science; Computation; Operations research; Library science; Artificial intelligence; Engineering; Algorithm","score_opus":0.018798048576519847,"score_gpt":0.24817520331043738,"score_spread":0.22937715473391754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734849651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.761957,0.001362371,0.22709315,0.0045532063,0.00042586715,0.0009628838,0.000009561303,0.000099936515,0.0035360656],"genre_scores_gemma":[0.9043836,0.00024830602,0.09515918,0.000042687774,0.00006400662,0.000036987392,0.0000049157557,0.0000064596998,0.000053859658],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998534,0.000027566399,0.00039666056,0.0004737736,0.00031678186,0.00025119074],"domain_scores_gemma":[0.9989571,0.000052883446,0.0004069722,0.00020211416,0.00029980368,0.00008112207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023108958,0.00018127286,0.00020940976,0.00010543057,0.0009360153,0.00019017418,0.00067680975,0.00007210812,0.0000025692907],"category_scores_gemma":[0.000019361762,0.00016688742,0.00004421751,0.00024112713,0.00032499075,0.00062079786,0.0008907663,0.00015401724,0.000002657647],"study_design_candidate":"observational","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":[0.000039309372,0.00035760136,0.34392253,0.00032759257,0.00006923374,0.0000022534418,0.0012468051,0.023395214,0.00021314986,0.57674694,0.006119199,0.04756019],"study_design_scores_gemma":[0.00026268282,0.000021172229,0.49129948,0.00007519709,0.0000071635054,0.000022357615,0.00006132479,0.354115,0.0000046991904,0.15391795,0.000118021075,0.00009494776],"about_ca_topic_score_codex":0.000052422118,"about_ca_topic_score_gemma":0.0000045595184,"teacher_disagreement_score":0.42282897,"about_ca_system_score_codex":0.000060431015,"about_ca_system_score_gemma":0.00005019138,"threshold_uncertainty_score":0.71991676},"labels":[],"label_agreement":null},{"id":"W2736737873","doi":"10.1299/jsmeicam.2010.5.67","title":"A novel hybridization design principle for intelligent mechatronics systems","year":2010,"lang":"en","type":"article","venue":"The Abstracts of the international conference on advanced mechatronics toward evolutionary fusion of IT and mechatronics ICAM","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mechatronics; Compatibility (geochemistry); Hybrid system; Control engineering; Physical system; Computer science; Artificial intelligence; Engineering; Machine learning; Physics","score_opus":0.04521956076042407,"score_gpt":0.293682996712566,"score_spread":0.2484634359521419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736737873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021645887,0.0003460671,0.9603589,0.013132986,0.0020695382,0.0016871375,0.00025889618,0.00007324779,0.0004273503],"genre_scores_gemma":[0.8552798,0.00095779274,0.14288728,0.000121245714,0.00010581978,0.00021204233,0.000051954496,0.000024399245,0.0003596514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971904,0.000058630354,0.0008905157,0.00057025766,0.00088392367,0.00040627623],"domain_scores_gemma":[0.996417,0.00046518949,0.000984158,0.0010168283,0.0009977679,0.00011905257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011760697,0.00032206997,0.0003372357,0.00015712026,0.00040629317,0.00008276458,0.002492192,0.00017239539,0.000023352468],"category_scores_gemma":[0.0003069564,0.00023104777,0.00023250662,0.00026139218,0.00019960677,0.00046004244,0.00069533475,0.00054664456,0.000007698235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009485324,0.00025426582,0.0000025361046,0.000029679897,0.00005668312,8.8445525e-8,0.00010201855,0.052781723,0.11428121,0.8299802,0.00019038578,0.0022263457],"study_design_scores_gemma":[0.0013356431,0.00049004547,0.00024183886,0.00023296266,0.000056676843,0.000043889504,0.0005189284,0.7900733,0.03076461,0.14749049,0.028298873,0.00045278802],"about_ca_topic_score_codex":0.000031381212,"about_ca_topic_score_gemma":0.000014785856,"teacher_disagreement_score":0.8336339,"about_ca_system_score_codex":0.00018065459,"about_ca_system_score_gemma":0.0007672211,"threshold_uncertainty_score":0.94218546},"labels":[],"label_agreement":null},{"id":"W2741017123","doi":"10.1109/cem.2017.7991862","title":"A novel massively-parallel processing framework for real-time MIMO and smart antenna array beam control","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Dataflow; Computer science; Massively parallel; MIMO; Graphics processing unit; Graphics; Parallel processing; Parallel computing; Computational science; Computer hardware; Beamforming; Computer graphics (images); Telecommunications","score_opus":0.01869744357005282,"score_gpt":0.2733704878329659,"score_spread":0.2546730442629131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741017123","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053745013,0.00006684245,0.98598623,0.010617668,0.000059480422,0.00032174189,0.000020391151,0.000114158865,0.002276055],"genre_scores_gemma":[0.10782418,0.000021779508,0.8893683,0.00033562622,0.000119913886,0.00013933594,0.0000031062164,0.000009406492,0.0021783721],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903613,0.000006092435,0.00016738585,0.00041701834,0.00011984309,0.0002535386],"domain_scores_gemma":[0.9988208,0.00012701446,0.00016316502,0.0006062381,0.0001750451,0.000107752254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014313075,0.00012317962,0.00016402901,0.000028847146,0.0009903143,0.00042504416,0.000609365,0.000080301936,0.000007417534],"category_scores_gemma":[0.000087587265,0.00010685287,0.000050456074,0.00006109348,0.000110178065,0.0005563817,0.0001060736,0.00008477247,0.000021671207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040144754,0.0005119162,0.0014920864,0.000073843985,0.00007599505,0.0000052821038,0.00038776296,0.000044062017,0.13120528,0.8328974,0.0027246613,0.030541545],"study_design_scores_gemma":[0.0024652118,0.00013876343,0.060711272,0.00012852137,0.00003715172,0.00008469943,0.000046417532,0.7154661,0.0011215294,0.21021155,0.008971103,0.00061769696],"about_ca_topic_score_codex":0.000033591215,"about_ca_topic_score_gemma":0.0000043704526,"teacher_disagreement_score":0.71542203,"about_ca_system_score_codex":0.000014532042,"about_ca_system_score_gemma":0.000097679615,"threshold_uncertainty_score":0.7616797},"labels":[],"label_agreement":null},{"id":"W2755371172","doi":"10.1016/j.inffus.2017.09.010","title":"Dynamic classifier selection: Recent advances and perspectives","year":2017,"lang":"en","type":"article","venue":"Information Fusion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":429,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Machine learning; Oracle; Artificial intelligence; Categorization; Data mining; Probabilistic logic","score_opus":0.013256513296389086,"score_gpt":0.2633503160558346,"score_spread":0.2500938027594455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755371172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013838715,0.0010195462,0.93668765,0.016654171,0.000336694,0.00026041173,0.0000050733247,0.00020121812,0.030996501],"genre_scores_gemma":[0.897762,0.008808926,0.09231437,0.00029103504,0.00007061812,0.00006181765,0.000016193813,0.000004180192,0.0006708621],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99957055,0.0000074922923,0.000118795804,0.00009780263,0.0001226316,0.000082708],"domain_scores_gemma":[0.999428,0.000011252889,0.0001348112,0.0002494298,0.00013767803,0.000038867493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008671128,0.000055141816,0.00004527794,0.00005244215,0.0009357556,0.00029783137,0.0002516889,0.000030759235,0.000024573128],"category_scores_gemma":[0.000030386378,0.000049200895,0.000013469786,0.00007410421,0.00004764969,0.0042442237,0.00013965328,0.000060946277,0.000058298814],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003601497,0.000024778172,0.00042950842,0.000010562639,0.0000034937445,1.515043e-7,0.0015846543,0.000119966164,0.00010151409,0.08709321,0.0008638903,0.90976465],"study_design_scores_gemma":[0.00024361507,0.000037029182,0.0892997,0.0000109705925,0.0000019145377,0.000021373171,0.00037650298,0.34462744,0.00006076,0.0057708533,0.5594286,0.00012128413],"about_ca_topic_score_codex":0.000005646915,"about_ca_topic_score_gemma":0.000006987775,"teacher_disagreement_score":0.9096434,"about_ca_system_score_codex":0.000039402497,"about_ca_system_score_gemma":0.000027308226,"threshold_uncertainty_score":0.719717},"labels":[],"label_agreement":null},{"id":"W2761970546","doi":"10.1109/cibcb.2017.8058532","title":"Hybridization and ring optimization for larger sets of embeddable biomarkers","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Guelph","funders":"","keywords":"Computer science; Algorithm; Code (set theory); Metric (unit); Ring (chemistry); Set (abstract data type); Alphabet; Levenshtein distance; Point (geometry); Evolutionary algorithm; Genetic algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Machine learning; Engineering","score_opus":0.017591924693344724,"score_gpt":0.27656566580256553,"score_spread":0.2589737411092208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761970546","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003841321,0.000029091656,0.9932882,0.00079549017,0.00004551297,0.00014357328,0.000004355893,0.000027550377,0.0018248837],"genre_scores_gemma":[0.33286673,0.000019456606,0.66685313,0.00002189773,0.000012149048,0.000021406235,0.0000064093697,0.0000027085418,0.00019611784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996542,0.000004435862,0.00008729551,0.00013393608,0.000051388244,0.00006877649],"domain_scores_gemma":[0.9995107,0.000024486248,0.000086417225,0.00027733654,0.000077284654,0.000023750432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117432355,0.000036452013,0.000047926158,0.000027670842,0.0003072332,0.000082750514,0.00020282301,0.000017036491,0.0000066519838],"category_scores_gemma":[0.000029122964,0.000033609926,0.000014887699,0.000040026083,0.000027481543,0.00048412388,0.00008188967,0.000010294531,7.5681976e-7],"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":[0.000011446229,0.00020866278,0.00681074,0.00011025375,0.00007749342,6.534126e-7,0.00028712384,0.05585173,0.003613661,0.83648497,0.005968642,0.09057461],"study_design_scores_gemma":[0.00017104455,0.000010966213,0.0043371837,0.000005399594,0.0000026094315,0.0000014728646,0.0000059781137,0.9906253,0.0016585242,0.0026064594,0.0005299554,0.00004507321],"about_ca_topic_score_codex":0.000019687177,"about_ca_topic_score_gemma":0.000001957867,"teacher_disagreement_score":0.9347736,"about_ca_system_score_codex":0.0000056790823,"about_ca_system_score_gemma":0.00001569859,"threshold_uncertainty_score":0.23630203},"labels":[],"label_agreement":null},{"id":"W2765509542","doi":"","title":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","year":2009,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Computer science; Publishing; Evolutionary computation; Track (disk drive); Operations research; Political science; Law; Artificial intelligence; Mathematics","score_opus":0.016683599167998064,"score_gpt":0.24074105901689855,"score_spread":0.2240574598489005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765509542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48791122,0.0008904591,0.50217223,0.006272891,0.00020227671,0.0007145807,0.00004729546,0.00014793339,0.0016411125],"genre_scores_gemma":[0.898345,0.00017933502,0.10095768,0.00027392583,0.00007154457,0.000026268754,0.000012600847,0.0000076008546,0.00012607346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9980044,0.00006134447,0.00048741978,0.0006536238,0.000493396,0.00029986422],"domain_scores_gemma":[0.9984679,0.000103850536,0.0002912923,0.0002052695,0.00078017166,0.00015149005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001217709,0.00026615275,0.0002444044,0.00015261823,0.0005341994,0.00010958106,0.0004695659,0.00011256405,0.000009152412],"category_scores_gemma":[0.000027844211,0.00023394995,0.000060634502,0.0005324766,0.00034096197,0.00039828368,0.00021084909,0.00019798496,0.000010240469],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094885574,0.0010099127,0.016728794,0.00016622871,0.000092921204,0.000006971232,0.0048276004,0.04615755,0.0028214722,0.6142549,0.013662578,0.30017617],"study_design_scores_gemma":[0.00031530284,0.00022266994,0.5508526,0.000053958458,0.000012344216,0.00008737435,0.00013980306,0.3503281,0.000024090119,0.09760353,0.0001791871,0.00018103729],"about_ca_topic_score_codex":0.000020402276,"about_ca_topic_score_gemma":0.0000013023243,"teacher_disagreement_score":0.53412384,"about_ca_system_score_codex":0.000059845028,"about_ca_system_score_gemma":0.00024776862,"threshold_uncertainty_score":0.9540202},"labels":[],"label_agreement":null},{"id":"W2765557598","doi":"10.1109/tciaig.2017.2766980","title":"Discovering Agent Behaviors Through Code Reuse: Examples From Half-Field Offense and Ms. Pac-Man","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Games","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reuse; Computer science; Task (project management); Code (set theory); Code reuse; Field (mathematics); Modular design; Genetic programming; Domain (mathematical analysis); Artificial intelligence; Function (biology); State (computer science); Machine learning; Software engineering; Human–computer interaction; Programming language; Engineering; Systems engineering; Software","score_opus":0.03455042442923823,"score_gpt":0.28432642967877675,"score_spread":0.24977600524953852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765557598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.232349,0.000093175215,0.76294005,0.0037092972,0.0003339158,0.00013188113,0.000069336675,0.000105835235,0.00026752788],"genre_scores_gemma":[0.94950014,0.00029380174,0.049365837,0.0001868963,0.000057664,0.0000785741,0.0000027909862,0.00001221562,0.00050209224],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989665,0.000024745555,0.00017800373,0.00044156404,0.00018657668,0.00020260325],"domain_scores_gemma":[0.9982683,0.00013089471,0.000092572045,0.0013926264,0.000028616796,0.00008695983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060237737,0.00015723784,0.00014703916,0.00004168779,0.0010633016,0.00032190452,0.00078234257,0.00006912201,0.000038611364],"category_scores_gemma":[0.000008019237,0.00014991187,0.000075638905,0.00006261854,0.00011058921,0.00080717227,0.000019423132,0.0001846003,0.000024042973],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002296196,0.0073354137,0.0073262365,0.00017474187,0.00086808664,0.00026270864,0.046211243,0.018400766,0.08383522,0.06638764,0.02316934,0.745799],"study_design_scores_gemma":[0.0054385574,0.0018941904,0.26844618,0.0007354319,0.00061293563,0.00019616428,0.0018555295,0.08535567,0.32659307,0.055474773,0.24919222,0.004205277],"about_ca_topic_score_codex":0.0026137913,"about_ca_topic_score_gemma":0.00068918965,"teacher_disagreement_score":0.7415937,"about_ca_system_score_codex":0.00003037803,"about_ca_system_score_gemma":0.000030370307,"threshold_uncertainty_score":0.8178163},"labels":[],"label_agreement":null},{"id":"W2765566141","doi":"10.1109/cig.2017.8080423","title":"Evolved communication strategies and emergent behaviour of multi-agents in pursuit domains","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"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; Outcome (game theory); Synchronization (alternating current); Domain (mathematical analysis); Simple (philosophy); Reinforcement learning; Tracking (education); Artificial intelligence; Human–computer interaction; Distributed computing; Channel (broadcasting); Psychology; Computer network","score_opus":0.06137127864165906,"score_gpt":0.3347600200252422,"score_spread":0.27338874138358316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765566141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70326316,0.0004901725,0.29009724,0.0026176684,0.00006377735,0.00032462282,0.000005985486,0.000049766233,0.0030876342],"genre_scores_gemma":[0.8893965,0.00017472118,0.11017496,0.000012082404,0.00000357779,0.000024357032,0.0000028839922,0.0000019938188,0.00020895503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9994941,0.000020945328,0.0001546008,0.00014153557,0.000100553225,0.000088306006],"domain_scores_gemma":[0.9989833,0.00001451608,0.00010331038,0.000818703,0.000046076475,0.00003404425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015553093,0.0000539859,0.000073599,0.00003791643,0.00022695282,0.0001346258,0.00088381034,0.00002755441,0.0000128332085],"category_scores_gemma":[0.000008165944,0.000050316623,0.000017893959,0.00005000108,0.000082667175,0.000703142,0.00037501144,0.000051223265,0.0000037436403],"study_design_candidate":"observational","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":[0.0000035557123,0.0010143584,0.16169907,0.000020782905,0.000021772325,0.00000470614,0.0015443126,0.00031010454,0.0027594506,0.8048409,0.00069947913,0.027081462],"study_design_scores_gemma":[0.00032377942,0.000013949128,0.91572696,0.000009405443,0.0000022273496,0.0000019735342,0.000198247,0.07925235,0.0001622208,0.004011088,0.00023260842,0.00006521125],"about_ca_topic_score_codex":0.0008233671,"about_ca_topic_score_gemma":0.00052934146,"teacher_disagreement_score":0.8008299,"about_ca_system_score_codex":0.000012006095,"about_ca_system_score_gemma":0.000030066138,"threshold_uncertainty_score":0.20518523},"labels":[],"label_agreement":null},{"id":"W2766605685","doi":"10.1007/978-3-319-77583-8_3","title":"Non-photorealistic Rendering with Cartesian Genetic Programming Using Graphics Processing Units","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Brock University","funders":"","keywords":"Rendering (computer graphics); Computer science; Computer graphics (images); Genetic programming; Population; Evolutionary algorithm; Artificial intelligence; Computer vision","score_opus":0.02509104614888687,"score_gpt":0.2526769971125446,"score_spread":0.22758595096365772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766605685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006005596,0.00026299694,0.997721,0.00011451738,0.00026140024,0.00050219457,0.0000040685454,0.00016257915,0.0003706903],"genre_scores_gemma":[0.12309881,0.000011922117,0.87604123,0.00023998988,0.00047770134,0.000023742574,0.000006939282,0.000044896115,0.000054739663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965566,0.000014851824,0.00043609354,0.001427377,0.0008681946,0.0006969203],"domain_scores_gemma":[0.99769366,0.00009635418,0.0003350153,0.0010754466,0.00059694913,0.00020257026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038036334,0.0004906918,0.00036914795,0.0006442145,0.0007489438,0.00067840033,0.0020615235,0.00021496009,0.000004383537],"category_scores_gemma":[0.000019848216,0.000436912,0.000053076576,0.00178966,0.0010048031,0.000517026,0.00062587037,0.00055549963,0.0000047458516],"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":[0.000007174622,0.000074604424,0.00026745582,0.00023284779,0.00003031347,0.00022433136,0.0031218114,0.06765341,0.00023042737,0.009769336,0.000013560077,0.9183747],"study_design_scores_gemma":[0.00015279064,0.0001680251,0.0002974259,0.00073765864,0.000018189428,0.00030029553,7.4256167e-7,0.9734402,0.00016181075,0.023392914,0.0007052892,0.0006247176],"about_ca_topic_score_codex":0.00009998265,"about_ca_topic_score_gemma":0.0001524042,"teacher_disagreement_score":0.91775,"about_ca_system_score_codex":0.00026146777,"about_ca_system_score_gemma":0.0012238149,"threshold_uncertainty_score":0.99980825},"labels":[],"label_agreement":null},{"id":"W2768421170","doi":"10.1016/j.ins.2017.11.041","title":"Automatic feature engineering for regression models with machine learning: An evolutionary computation and statistics hybrid","year":2017,"lang":"en","type":"article","venue":"Information Sciences","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":37,"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":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universal","keywords":"Genetic programming; Computer science; Feature engineering; Symbolic regression; Interpretability; Machine learning; Artificial intelligence; Randomness; Feature (linguistics); Benchmark (surveying); Evolutionary computation; Regression; Data mining; Statistics; Mathematics; Deep learning","score_opus":0.019995423766872505,"score_gpt":0.2768263841359542,"score_spread":0.25683096036908165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768421170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008164526,0.00004330667,0.990256,0.0009204172,0.000064844426,0.0002156462,0.000034111836,0.00011684362,0.00018431494],"genre_scores_gemma":[0.38080502,0.000009115023,0.6190384,0.00003342523,0.000015372903,0.000029072113,0.000046460165,0.0000018849113,0.000021275422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992617,0.000011868541,0.00015373484,0.00015359913,0.00027770924,0.00014141819],"domain_scores_gemma":[0.99926263,0.00007774688,0.00023890445,0.00018922858,0.00016164401,0.00006983548],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00028972785,0.00009182981,0.00008161336,0.00010454355,0.001543971,0.00066976144,0.00043588856,0.000023933462,0.0000011563783],"category_scores_gemma":[0.000056254932,0.00006872068,0.0000113447195,0.000100176985,0.00012338914,0.006802212,0.00008816165,0.00007427437,0.0000026734642],"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":[0.0000073494557,0.000039635062,0.00084198255,0.00007821299,0.000009959135,9.2517234e-7,0.0012818439,0.43419242,0.00003480272,0.32042557,0.0012561763,0.24183111],"study_design_scores_gemma":[0.00020481013,0.00013125387,0.006492174,0.000026811067,0.0000029118191,0.00003231605,0.000046057692,0.9808126,0.000018911072,0.011136012,0.0009889588,0.0001071616],"about_ca_topic_score_codex":0.000024929634,"about_ca_topic_score_gemma":0.0000024185867,"teacher_disagreement_score":0.5466202,"about_ca_system_score_codex":0.000021154812,"about_ca_system_score_gemma":0.00007771754,"threshold_uncertainty_score":0.99975586},"labels":[],"label_agreement":null},{"id":"W2771070736","doi":"10.15353/vsnl.v3i1.161","title":"Polyploidism in Deep Neural Networks: m-Parent Evolutionary Synthesis of Deep Neural Networks in Varying Population Sizes","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"MNIST database; Artificial neural network; Artificial intelligence; Modern evolutionary synthesis; Population; Deep neural networks; Computer science; Evolutionary algorithm; Evolutionary acquisition of neural topologies; Biology; Evolutionary biology; Time delay neural network; Demography","score_opus":0.011659559114029314,"score_gpt":0.27425315886240553,"score_spread":0.2625935997483762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771070736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15558589,0.0043695737,0.83731383,0.0017071716,0.0007776889,0.00018647162,0.0000019936226,0.000016556623,0.00004080653],"genre_scores_gemma":[0.9918832,0.000043999902,0.007794611,0.000040969822,0.00021216676,0.000008122878,0.0000051448483,0.000009370071,0.0000023857858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979992,0.00018328335,0.0009579832,0.00021273519,0.00042905548,0.00021777171],"domain_scores_gemma":[0.99797964,0.0004766569,0.0009795518,0.00023042868,0.00023603103,0.00009767139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063551,0.00015841215,0.00036443205,0.00034045058,0.00031199507,0.00025565765,0.0005225684,0.0000504028,0.0000017050182],"category_scores_gemma":[0.00009696565,0.0001413537,0.00009558389,0.00022679954,0.00007469847,0.0014025634,0.00015911451,0.00024414706,3.1552085e-7],"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":[0.000017509863,0.00008645172,0.069084875,0.0000165887,0.0000088873585,0.000013062326,0.00010519268,0.8954886,0.000009903843,0.0022194467,0.000024063713,0.032925427],"study_design_scores_gemma":[0.00030775525,0.000022115333,0.4025641,0.000120092,0.000004695331,0.00013752728,0.00005000978,0.5955799,4.0330312e-7,0.001128718,0.000005762902,0.00007887816],"about_ca_topic_score_codex":0.00024765695,"about_ca_topic_score_gemma":0.000011381659,"teacher_disagreement_score":0.83629733,"about_ca_system_score_codex":0.000099598044,"about_ca_system_score_gemma":0.000026640239,"threshold_uncertainty_score":0.57642365},"labels":[],"label_agreement":null},{"id":"W2775486426","doi":"10.11575/prism/24736","title":"A Framework for Improving Systems Performance by Minimizing Burstiness","year":2017,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Burstiness; Computer science; Computer network","score_opus":0.012194241518858428,"score_gpt":0.226191948495836,"score_spread":0.21399770697697756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775486426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009187128,0.0007008979,0.986286,0.00014279144,0.00055057975,0.00045468306,0.0000037852412,0.0000744813,0.0025996633],"genre_scores_gemma":[0.03266718,0.00023706249,0.9261122,0.000012806499,0.00009147879,0.000021096464,0.00046665463,0.000031163006,0.04036041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989979,0.000014882438,0.00014604836,0.00040723637,0.0002187713,0.00021513936],"domain_scores_gemma":[0.99848956,0.000092358016,0.00054662646,0.0006078582,0.00018667334,0.000076924574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001379823,0.00016494852,0.00027708663,0.00010212463,0.0007641633,0.00007244545,0.0014190885,0.00027567171,0.0000045156703],"category_scores_gemma":[0.000030534942,0.00021086917,0.000116637064,0.000093807954,0.00004902988,0.00047553948,0.00008771777,0.00022128427,0.0000071350632],"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":[0.00006582685,0.00016729618,0.00012973916,0.0018069531,0.000101196056,0.000006383608,0.002565357,0.000008948493,0.00088960904,0.07555158,0.0058226218,0.9128845],"study_design_scores_gemma":[0.00049715006,0.000102364706,0.003915405,0.0005149816,0.00009681879,0.0000039832535,0.00027627422,0.9620088,0.00021795904,0.002056908,0.029782658,0.0005267094],"about_ca_topic_score_codex":0.00040880553,"about_ca_topic_score_gemma":0.0000035688693,"teacher_disagreement_score":0.96199983,"about_ca_system_score_codex":0.000053941385,"about_ca_system_score_gemma":0.00016545507,"threshold_uncertainty_score":0.8598995},"labels":[],"label_agreement":null},{"id":"W2783272115","doi":"10.1109/icmla.2017.00-41","title":"Evolving Adaptive Traffic Signal Controllers for a Real Scenario Using Genetic Programming with an Epigenetic Mechanism","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Mechanism (biology); Computer science; Genetic programming; Traffic signal; Epigenetics; SIGNAL (programming language); Real-time computing; Artificial intelligence; Biology; Programming language; Genetics","score_opus":0.034315909369502746,"score_gpt":0.27313176824835095,"score_spread":0.2388158588788482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783272115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062084235,0.00004078357,0.9364674,0.00019127704,0.000047716007,0.0008825855,0.000003115784,0.00012743197,0.00015546929],"genre_scores_gemma":[0.50080144,0.000002310948,0.49894446,0.00001632008,0.000061756364,0.00010562613,0.0000011309342,0.000009312429,0.000057653815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866956,0.000028543538,0.00019210276,0.0005130967,0.00020892217,0.00038777853],"domain_scores_gemma":[0.9987654,0.000052751795,0.00018131295,0.00067769346,0.00019118332,0.00013167213],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0001871049,0.00016638073,0.00017805299,0.000059637394,0.0014356483,0.0004814393,0.0008509222,0.000055868542,0.0000075748844],"category_scores_gemma":[0.000007583024,0.00013892936,0.000063812244,0.00008334148,0.00009745693,0.0005788179,0.00009948422,0.000075306605,0.0000027235292],"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":[0.00018747206,0.0009320397,0.000449498,0.000041720643,0.0002641473,0.00004576622,0.0023406388,0.124632195,0.006298539,0.37024862,0.000058256624,0.4945011],"study_design_scores_gemma":[0.00085920363,0.0006245812,0.0019034351,0.000020740097,0.000025739031,0.00002419264,0.00012131369,0.9917624,0.00018756569,0.0041952534,0.00004825306,0.00022733225],"about_ca_topic_score_codex":0.00023754044,"about_ca_topic_score_gemma":0.00018671698,"teacher_disagreement_score":0.8671302,"about_ca_system_score_codex":0.00007095521,"about_ca_system_score_gemma":0.00022877978,"threshold_uncertainty_score":0.99986434},"labels":[],"label_agreement":null},{"id":"W2784330914","doi":"10.1007/978-3-030-36568-4_14","title":"A Random Walk Through Experimental Mathematics","year":2020,"lang":"en","type":"preprint","venue":"Springer proceedings in mathematics & statistics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mathematics education; Presentation (obstetrics); Adventure; Sister; Course (navigation); Random walk; Computer science; Pedagogy; Mathematics; Psychology; Sociology; Engineering; Artificial intelligence; Statistics","score_opus":0.033325143667175816,"score_gpt":0.29629727922955795,"score_spread":0.26297213556238214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784330914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025176539,0.0004536003,0.9873649,0.0008760421,0.00039211358,0.0014208785,0.00013587954,0.00042987775,0.0064090695],"genre_scores_gemma":[0.024702994,0.00021153876,0.9737703,0.0001409172,0.0002195138,0.00061652914,0.000030697694,0.000088700195,0.0002187603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960953,0.000015141724,0.0013164463,0.0010693249,0.0008896169,0.00061414513],"domain_scores_gemma":[0.9977079,0.00028739648,0.0007973322,0.0007203327,0.00029809237,0.00018893935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048725953,0.00066756405,0.0009468199,0.0001694801,0.00019373272,0.00059945474,0.0022401423,0.00032046592,0.000035077617],"category_scores_gemma":[0.00032033588,0.0006883033,0.0001626349,0.00042307252,0.00015703711,0.0003731539,0.0028901298,0.0011108256,0.00014482612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051799207,0.0009628935,0.000025414574,0.0017319821,0.000068345726,0.000035895806,0.02296689,0.00013209743,0.00083813135,0.9694981,0.0033097358,0.0004253323],"study_design_scores_gemma":[0.00067071745,0.000047965605,0.000041577994,0.0004181776,0.00003863236,0.000024645713,0.00082601287,0.33847314,0.001415062,0.65607786,0.0013278597,0.0006383424],"about_ca_topic_score_codex":0.000024192794,"about_ca_topic_score_gemma":0.0000024887797,"teacher_disagreement_score":0.33834103,"about_ca_system_score_codex":0.00028276155,"about_ca_system_score_gemma":0.00022739287,"threshold_uncertainty_score":0.99955684},"labels":[],"label_agreement":null},{"id":"W2786183742","doi":"10.1109/crv.2018.00058","title":"Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Nature versus nurture; MNIST database; Modern evolutionary synthesis; Artificial neural network; Computer science; Process (computing); Artificial intelligence; Evolutionary algorithm; Deep neural networks; Machine learning; Biology; Evolutionary biology; Genetics","score_opus":0.00489399679339767,"score_gpt":0.2192119511954768,"score_spread":0.21431795440207912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786183742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098450065,0.045196302,0.81085914,0.013885856,0.0015188726,0.0024692107,0.00022913684,0.00038817205,0.027003253],"genre_scores_gemma":[0.93914473,0.0003060232,0.059786044,0.00022192033,0.00018693898,0.00009083026,0.0000216973,0.000008805372,0.0002330386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984351,0.0000792206,0.000350382,0.00056156015,0.00036722494,0.00020652698],"domain_scores_gemma":[0.9985244,0.00012533195,0.00017835016,0.0010916336,0.000031758125,0.000048481743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022571557,0.0001945507,0.00017851318,0.00010232761,0.00012951475,0.00004182067,0.0022953805,0.00034799683,0.00012673366],"category_scores_gemma":[0.000014767969,0.00014140092,0.000107759355,0.00024562108,0.00030426765,0.00012879266,0.0021034342,0.0008548099,0.00005147062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005751284,0.0023385491,0.018745437,0.00010953576,0.00020626253,0.000013938023,0.013273852,0.01605428,0.0011776042,0.77454376,0.008790442,0.16468886],"study_design_scores_gemma":[0.000066545865,0.000058936053,0.117189005,0.000069986876,0.000011016673,0.000024515448,0.0004907296,0.4977116,0.0013260543,0.34175524,0.0409154,0.00038096384],"about_ca_topic_score_codex":0.000076646116,"about_ca_topic_score_gemma":0.000019930008,"teacher_disagreement_score":0.84069467,"about_ca_system_score_codex":0.00010291334,"about_ca_system_score_gemma":0.00006603443,"threshold_uncertainty_score":0.5766162},"labels":[],"label_agreement":null},{"id":"W2791595938","doi":"10.1007/978-3-319-77583-8_8","title":"evoExplore: Multiscale Visualization of Evolutionary Histories in Virtual Reality","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Virtual reality; Game engine; Visualization; Human–computer interaction; Evolutionary algorithm; Virtual machine; Artificial intelligence","score_opus":0.022578637477935783,"score_gpt":0.27175204969171174,"score_spread":0.24917341221377595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791595938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001618884,0.00034813912,0.99661505,0.00050280755,0.00088825246,0.00032964093,0.000016948508,0.00008958577,0.0010477086],"genre_scores_gemma":[0.47238603,0.00013587659,0.52504295,0.00040422982,0.00088626903,0.00006182714,0.0000709028,0.000051116196,0.0009607987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969285,0.00004689631,0.00068095454,0.0011265585,0.0008321583,0.00038493445],"domain_scores_gemma":[0.99777836,0.00028688207,0.00034798693,0.0010770318,0.00040999192,0.00009977259],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059019116,0.0003281921,0.00040991823,0.0007070873,0.00019780686,0.00007074326,0.0018867363,0.0002664024,0.000032419208],"category_scores_gemma":[0.00009079636,0.00033267014,0.0000892513,0.0009734667,0.0012032028,0.0007340612,0.0008556688,0.0003351846,0.00001958667],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025282927,0.00043731066,0.0009080561,0.00010320646,0.00002017431,0.00003468924,0.0069366456,0.08418442,0.00042324432,0.639776,0.0008546251,0.2662964],"study_design_scores_gemma":[0.00026281687,0.00021539179,0.0019024456,0.00022765931,0.000004398112,0.000021721073,8.544054e-7,0.7671322,0.0003813952,0.22574127,0.0036507868,0.0004590309],"about_ca_topic_score_codex":0.00007751067,"about_ca_topic_score_gemma":0.0001560125,"teacher_disagreement_score":0.6829478,"about_ca_system_score_codex":0.00060838676,"about_ca_system_score_gemma":0.00059396104,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W2792319327","doi":"10.3390/w10020142","title":"Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Cluster analysis; Gene expression programming; Term (time); Computer science; Scale (ratio); Unsupervised learning; Process (computing); Stage (stratigraphy); Machine learning; Artificial intelligence; Expression (computer science); Series (stratigraphy); Data mining; Geography","score_opus":0.022487061769244494,"score_gpt":0.27463007173298404,"score_spread":0.25214300996373956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792319327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6878747,0.000013872968,0.31143814,0.00011564515,0.000029691877,0.00033616895,3.5939937e-7,0.00014609419,0.00004538774],"genre_scores_gemma":[0.9359584,0.0000025462175,0.06267197,0.000025000303,0.00008817309,0.00025810223,0.00004173596,0.000025468897,0.0009286508],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820083,0.00007136224,0.0002646787,0.0005975513,0.0002664453,0.00059912296],"domain_scores_gemma":[0.99937445,0.000014026351,0.00003629863,0.00031073825,0.0001487155,0.00011577149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003041426,0.00021452525,0.0001875889,0.0001172784,0.00043027935,0.00018332242,0.00043943507,0.00007422408,0.000042596526],"category_scores_gemma":[0.0000039865768,0.00012352545,0.000038390404,0.00019889195,0.00010862851,0.0005901306,0.00023667661,0.00028569275,0.00006807036],"study_design_candidate":"bench_or_experimental","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":[0.00015364088,0.00087810535,0.18399322,0.00014855132,0.00004365817,0.00011960593,0.04248892,0.033597615,0.72342545,0.0000590563,0.000014644769,0.015077554],"study_design_scores_gemma":[0.0012820595,0.0002633823,0.0053995056,0.000082565566,0.000007056646,0.000033609755,0.00017566672,0.7126291,0.2783182,0.00004206921,0.0014040561,0.00036276248],"about_ca_topic_score_codex":0.000027506729,"about_ca_topic_score_gemma":0.00005778349,"teacher_disagreement_score":0.67903143,"about_ca_system_score_codex":0.000035971658,"about_ca_system_score_gemma":0.000023170003,"threshold_uncertainty_score":0.50372213},"labels":[],"label_agreement":null},{"id":"W2792671280","doi":"10.1155/2019/7293193","title":"Image Evolution Using 2D Power Spectra","year":2019,"lang":"en","type":"article","venue":"Complexity","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"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; Artificial intelligence; Heuristics; Genetic programming; Metric (unit); Pattern recognition (psychology); Image (mathematics); Perception; Field (mathematics); Machine learning; Computer vision; Mathematics","score_opus":0.0378402936208699,"score_gpt":0.2797179422517128,"score_spread":0.2418776486308429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792671280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12782213,0.00005812625,0.8570519,0.0005454678,0.00024959032,0.00017029214,0.000006091954,0.00016015222,0.013936223],"genre_scores_gemma":[0.64615476,7.8195507e-7,0.35349336,0.00007256148,0.000051642826,0.0000034709906,0.0000034185568,0.0000046280697,0.00021535392],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991622,0.000028053435,0.00013278876,0.00030150518,0.00016730507,0.0002081594],"domain_scores_gemma":[0.99920356,0.000022259128,0.000054477405,0.0005846851,0.000072397794,0.00006259878],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000117191994,0.00008824806,0.000098210105,0.000048592923,0.000165573,0.00006742729,0.00050002587,0.000030741234,0.00030359172],"category_scores_gemma":[0.000005981691,0.00008961748,0.000056707428,0.0003142903,0.000071059374,0.00045474834,0.00019910454,0.000099794655,0.0010331273],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015059604,0.00009849355,0.0014903393,0.000005973228,0.0000064383507,0.0000016888953,0.00010473296,0.00005205875,0.015308373,0.98143935,0.0011961893,0.00029485417],"study_design_scores_gemma":[0.00025040982,0.000041789175,0.14056343,0.000009996872,0.0000025850802,0.000038462174,0.000025092933,0.70703804,0.00029962417,0.14657976,0.004914309,0.00023648313],"about_ca_topic_score_codex":0.00006294474,"about_ca_topic_score_gemma":0.000002373987,"teacher_disagreement_score":0.8348596,"about_ca_system_score_codex":0.00012116459,"about_ca_system_score_gemma":0.00006096548,"threshold_uncertainty_score":0.9997447},"labels":[],"label_agreement":null},{"id":"W2801596509","doi":"10.1139/tcsme-2005-0014","title":"SENSOR BASED ROBOT LOCALISATION AND NAVIGATION: USING INTERVAL ANALYSIS AND NONLINEAR KALMAN FILTERS.","year":2005,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Extended Kalman filter; Kalman filter; Invariant extended Kalman filter; Inertial measurement unit; Control theory (sociology); Computer science; Robot; Fast Kalman filter; Encoder; Sensor fusion; Mobile robot; Monte Carlo localization; Artificial intelligence; Computer vision","score_opus":0.013532198304187807,"score_gpt":0.22325940936403857,"score_spread":0.20972721105985076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801596509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012484614,0.000034615663,0.98456156,0.0026608896,0.000041802276,0.00014057837,0.000049618247,0.000025117313,0.0000011920455],"genre_scores_gemma":[0.54393387,0.000002083498,0.45591557,0.00009791525,0.000022893197,0.000009910129,0.0000046309992,0.0000044930525,0.00000866469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994697,0.0000067894644,0.00014811216,0.00015934538,0.000083479565,0.00013258008],"domain_scores_gemma":[0.9995613,0.000051833365,0.00003343178,0.00018466491,0.000048761318,0.000120002784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012709752,0.000075579876,0.00010899851,0.00005571338,0.0002483626,0.000033055672,0.00015122452,0.000061811195,0.000004074998],"category_scores_gemma":[0.0000051509214,0.00007225297,0.000207432,0.00043849205,0.000031028627,0.00015661994,0.000007795739,0.000094056144,1.18929194e-7],"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":[0.0000013659626,0.00002321186,0.000017424314,0.00004867767,0.00024257846,1.10751905e-7,0.00027740272,0.98249346,0.0046409885,0.005367201,0.000013919014,0.0068736617],"study_design_scores_gemma":[0.000139592,0.000011489465,0.00015776034,0.000014077223,0.00012462749,0.000004240646,0.000026404112,0.99629,0.0025881804,0.00006698723,0.00049927476,0.00007732973],"about_ca_topic_score_codex":0.0025869925,"about_ca_topic_score_gemma":0.0024752053,"teacher_disagreement_score":0.53144926,"about_ca_system_score_codex":0.00012868161,"about_ca_system_score_gemma":0.00007396652,"threshold_uncertainty_score":0.39107758},"labels":[],"label_agreement":null},{"id":"W2802803714","doi":"","title":"When Machines Design Machines!: ICED11 Futurology Keynote","year":2011,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cochrane","funders":"","keywords":"Computer science; Engineering ethics; Engineering","score_opus":0.02394983062227794,"score_gpt":0.2075804330920825,"score_spread":0.18363060246980456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802803714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015715303,0.0002900227,0.9553456,0.0023189122,0.00015778828,0.0008602738,0.00007561885,0.0005702131,0.024666272],"genre_scores_gemma":[0.5228437,0.0003673046,0.47405398,0.00013688343,0.000047573274,8.8095317e-7,0.000017848448,0.00003149048,0.0025003043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9961518,0.00042563703,0.0005914748,0.0012806004,0.0007618948,0.0007885832],"domain_scores_gemma":[0.99588436,0.0006075946,0.00066115765,0.0018447135,0.0004982498,0.0005039037],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008655354,0.00054324156,0.00096886395,0.00059182156,0.0006494175,0.000023523731,0.005233159,0.000813773,0.0016934075],"category_scores_gemma":[0.00011925086,0.00064843,0.0006231905,0.0013692786,0.0015180123,0.0009208258,0.0025367038,0.0010033422,0.00031670326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006243005,0.0122202085,0.013599103,0.00063137384,0.0015500122,0.0033776944,0.0041611153,0.0008703923,0.06973233,0.64846987,0.1452258,0.0939191],"study_design_scores_gemma":[0.021257956,0.009034858,0.4713758,0.00093536644,0.0024717646,0.0022402452,0.0019434998,0.106784865,0.0054890458,0.20225605,0.16695057,0.009260001],"about_ca_topic_score_codex":0.0012012407,"about_ca_topic_score_gemma":0.0004510784,"teacher_disagreement_score":0.5071284,"about_ca_system_score_codex":0.0002629092,"about_ca_system_score_gemma":0.00031870024,"threshold_uncertainty_score":0.9995967},"labels":[],"label_agreement":null},{"id":"W2806746572","doi":"10.1016/j.tcs.2018.05.033","title":"Unary patterns under permutations","year":2018,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Evolutionary Algorithms and Applications","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 Winnipeg","funders":"","keywords":"Unary operation; Combinatorics; Permutation (music); Mathematics; Alphabet; Integer (computer science); Word (group theory); Discrete mathematics; Function (biology); Combinatorics on words; Computer science","score_opus":0.01175273181836696,"score_gpt":0.2700564735631998,"score_spread":0.25830374174483284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806746572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026654193,0.00000821578,0.964094,0.0039551957,0.0004001071,0.00010801742,0.0000029458283,0.00024377162,0.0045335866],"genre_scores_gemma":[0.8111624,0.000001727116,0.18748172,0.001033996,0.0002792577,0.000010947184,0.0000012065931,0.000004496825,0.000024232202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982532,0.00004577882,0.00018919872,0.0006054549,0.00047774072,0.0004285915],"domain_scores_gemma":[0.9985246,0.000120810786,0.000039716626,0.0007987431,0.0002855329,0.00023059477],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00048725784,0.000123131,0.00009765467,0.0001368201,0.0009284223,0.00029012092,0.0020170466,0.0000334872,0.00012559786],"category_scores_gemma":[0.0000168477,0.00010470575,0.000044406377,0.0011993912,0.002960325,0.0006859506,0.0008187976,0.0001182191,0.0004353534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.535072e-7,0.000053020292,0.00006401241,9.798705e-7,0.0000017562708,0.000001715721,0.00021214847,0.00006680067,0.00022228008,0.9899177,0.000097152755,0.00936178],"study_design_scores_gemma":[0.000090966656,0.00012367057,0.011738421,0.000008308036,0.0000020923737,0.000046148798,0.0000104027995,0.58084184,0.0008176793,0.40591317,0.0002590859,0.00014822204],"about_ca_topic_score_codex":0.000004173607,"about_ca_topic_score_gemma":8.4995247e-7,"teacher_disagreement_score":0.7845082,"about_ca_system_score_codex":0.000052076593,"about_ca_system_score_gemma":0.00014261084,"threshold_uncertainty_score":0.99975306},"labels":[],"label_agreement":null},{"id":"W2808118317","doi":"10.22214/ijraset.2018.4686","title":"Design and Implementation of a Low Power Vedic Multiplier","year":2018,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Multiplier (economics); Computer science; Arithmetic; Mathematics; Economics","score_opus":0.037975967247133535,"score_gpt":0.39198389520560883,"score_spread":0.3540079279584753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808118317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12289847,0.000052971303,0.87361735,0.002933453,0.00017300723,0.0002463626,0.0000012658368,0.000026285028,0.000050831255],"genre_scores_gemma":[0.8395328,0.000041476684,0.1603205,0.000012341985,0.000032589312,0.000055025626,1.6675598e-7,0.0000025584577,0.0000025495328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899226,0.000005419133,0.00016098646,0.00019166223,0.0004110707,0.00023862607],"domain_scores_gemma":[0.9992021,0.00009527802,0.000036705875,0.00010557789,0.0005059633,0.000054430828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018468107,0.000047158243,0.00006033736,0.001091016,0.00016444472,0.000085077685,0.0007224187,0.0000365802,0.0000021648355],"category_scores_gemma":[0.000076419805,0.000043909167,0.0000070306914,0.0008060687,0.00044993663,0.0002075625,0.00026560307,0.00016661931,0.0000011528791],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016398684,0.000051641953,0.00028234764,0.000009274594,0.000014302712,0.0000044530016,0.00057259295,0.00037912512,0.26256418,0.59874487,0.00016768258,0.13719314],"study_design_scores_gemma":[0.0017209062,0.0005705012,0.0065419436,0.00008831384,0.0000017280527,0.00031485755,0.00083247497,0.7431119,0.087777786,0.15337943,0.005427323,0.00023282776],"about_ca_topic_score_codex":0.0000037970865,"about_ca_topic_score_gemma":9.3012636e-7,"teacher_disagreement_score":0.74273276,"about_ca_system_score_codex":0.000085149404,"about_ca_system_score_gemma":0.00014735572,"threshold_uncertainty_score":0.17905639},"labels":[],"label_agreement":null},{"id":"W2809097245","doi":"10.1162/evco_a_00232","title":"Emergent Solutions to High-Dimensional Multitask Reinforcement Learning","year":2018,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Task (project management); Neuroevolution; Representation (politics); Face (sociological concept); Machine learning; A priori and a posteriori; Artificial neural network","score_opus":0.018052462115047116,"score_gpt":0.26334435724358846,"score_spread":0.24529189512854133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809097245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012724499,0.00008103146,0.9796234,0.0051584127,0.0008359863,0.00039846965,0.000005441335,0.00043097456,0.0007418345],"genre_scores_gemma":[0.75427324,0.000004107632,0.2439647,0.00040251337,0.00041207258,0.000090625326,0.00012082903,0.000011437985,0.0007204933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790853,0.000095412484,0.0004189473,0.0005778601,0.00055044633,0.00044878488],"domain_scores_gemma":[0.99866253,0.00008145271,0.00012572625,0.0003439489,0.00055835187,0.0002279705],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027464883,0.00019564143,0.00014294761,0.00026543828,0.0013506501,0.00006164236,0.00044014124,0.00006689722,0.00013971956],"category_scores_gemma":[0.000055089447,0.00021370039,0.00007929519,0.00096678076,0.000094321214,0.00059140456,0.00045491324,0.00016406171,0.0018465053],"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":[0.000013844183,0.00021051076,0.00018109535,0.0000052809933,0.000037435428,0.0000029784742,0.00039395742,0.79144526,0.0019836898,0.14971992,0.04368608,0.012319926],"study_design_scores_gemma":[0.00034610284,0.00032111572,0.029259583,0.000018622133,0.000008172581,0.000023941642,0.00002454386,0.9467399,0.0001692454,0.009381225,0.013415832,0.0002916905],"about_ca_topic_score_codex":0.00012243954,"about_ca_topic_score_gemma":0.0000094731895,"teacher_disagreement_score":0.7415487,"about_ca_system_score_codex":0.00027917727,"about_ca_system_score_gemma":0.00016367823,"threshold_uncertainty_score":0.99994946},"labels":[],"label_agreement":null},{"id":"W2864378606","doi":"10.24963/ijcai.2018/740","title":"Emergent Tangled Program Graphs in Multi-Task Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reinforcement learning; Computer science; Task (project management); Modularity (biology); Representation (politics); Simple (philosophy); Artificial intelligence; Process (computing); Genetic programming; Control (management); Task analysis; Human–computer interaction; Machine learning; Distributed computing; Programming language","score_opus":0.024566346383513543,"score_gpt":0.2990231576891648,"score_spread":0.27445681130565125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2864378606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039915133,0.00007699231,0.95201033,0.0012222044,0.00016417915,0.0003888867,4.1467356e-7,0.00053725706,0.0056846254],"genre_scores_gemma":[0.6298347,0.000011372576,0.36865917,0.000074474956,0.000031300384,0.00011945056,0.0000022375425,0.0000036840481,0.0012636078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993067,0.000025044565,0.00013513585,0.00024287622,0.000102800994,0.00018747526],"domain_scores_gemma":[0.999624,0.000012614723,0.000028449935,0.00022120471,0.000060044233,0.000053701217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011983561,0.00006389153,0.00005830122,0.00007402978,0.00013439979,0.000036523943,0.00033391372,0.000027224496,0.000056899025],"category_scores_gemma":[0.000010783417,0.000056222587,0.000030722367,0.0006171791,0.000044734403,0.00017011988,0.00011641744,0.00008486629,0.00017969504],"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":[0.000003485683,0.0018368649,0.031434536,0.000008575533,0.00002263185,0.00000954816,0.0016925406,0.00034963115,0.0030120488,0.4554457,0.0043741814,0.50181025],"study_design_scores_gemma":[0.000371167,0.0001818886,0.061197475,0.000006866914,0.0000014350607,0.000004971674,0.00006523297,0.8630567,0.00036405798,0.0036324945,0.070939265,0.00017843825],"about_ca_topic_score_codex":0.00007457367,"about_ca_topic_score_gemma":0.000095292926,"teacher_disagreement_score":0.8627071,"about_ca_system_score_codex":0.000016359403,"about_ca_system_score_gemma":0.000020787225,"threshold_uncertainty_score":0.23096761},"labels":[],"label_agreement":null},{"id":"W2872295371","doi":"10.1145/3205651.3208282","title":"EvoNN","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Evolutionary computation; Activation function; Process (computing); Computation; Function (biology); Artificial intelligence; Algorithm; Programming language; Artificial neural network; Evolutionary biology","score_opus":0.01971042689048337,"score_gpt":0.24282985155771283,"score_spread":0.22311942466722945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2872295371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46488604,0.00025385572,0.52760774,0.0031422896,0.00032227757,0.00030460508,0.0000041773114,0.000112104804,0.0033668955],"genre_scores_gemma":[0.8996113,0.000029383003,0.10003009,0.00007904869,0.00011747491,0.000014172546,0.0000014118694,0.000004471866,0.000112660804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99899936,0.000012137398,0.0002607006,0.00030517057,0.00025891967,0.00016371046],"domain_scores_gemma":[0.998857,0.00003360766,0.00018689776,0.00012535675,0.00073239795,0.000064689186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010592889,0.00011710261,0.00012045839,0.00007202215,0.00040785814,0.00006679972,0.00057545357,0.000045804838,0.00001146629],"category_scores_gemma":[0.000019340448,0.00009450295,0.000043000862,0.0004331496,0.00032053055,0.00030657605,0.0003707455,0.00008006105,0.000015890773],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016749493,0.00025521417,0.03340394,0.00006907598,0.000053885087,1.7809253e-7,0.0016979338,0.0005442102,0.011447287,0.88114357,0.01455462,0.056813326],"study_design_scores_gemma":[0.00019540587,0.00008746973,0.44011757,0.000039466122,0.000010465773,0.000044967255,0.00008570656,0.43186784,0.0005705023,0.12567462,0.0011760284,0.0001299556],"about_ca_topic_score_codex":0.000013884023,"about_ca_topic_score_gemma":6.8827984e-7,"teacher_disagreement_score":0.75546896,"about_ca_system_score_codex":0.000027343594,"about_ca_system_score_gemma":0.00007595991,"threshold_uncertainty_score":0.38537186},"labels":[],"label_agreement":null},{"id":"W2887329137","doi":"10.1007/978-3-319-98812-2_34","title":"Learning Ranking Functions by Genetic Programming Revisited","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"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":"Singapore Management University; University of Science and Technology of China; Libera Università di Bolzano; Zayed University; University of the Aegean; Universidade Federal de Minas Gerais; Università degli Studi di Brescia; Università della Calabria; Università di Bologna; Turun Yliopisto; Université de Bourgogne; Högskolan i Skövde; Universität Wien; Massey University; Huazhong University of Science and Technology; Victoria University; Dalian University of Technology; Università degli Studi di Milano-Bicocca; Kangwon National University; Silesian University of Technology; Université de Fribourg; Sun Yat-sen University; Universidad de Zaragoza; National University of Singapore; University of Auckland; Griffith University; Université de Nantes; Monash University; Indian Council of Agricultural Research; Technische Universität Kaiserslautern; Macquarie University; Technische Universität Darmstadt; Aalborg Universitet; Università degli Studi di Napoli Federico II; Universidade de Coimbra; Memorial University of Newfoundland; Universität Passau; Georgia Southern University; University of Missouri-Kansas City; Northern Kentucky University; Ostravská Univerzita v Ostravě; Tallinna Tehnikaülikool; Victoria University of Wellington; Nanzan University; Université de Montpellier; University of Cyprus; National Taipei University of Technology; National Sun Yat-sen University; University of South Australia; University of Missouri; Universidad de Málaga; České Vysoké Učení Technické v Praze; Università degli Studi di Milano","keywords":"Genetic programming; Computer science; Ranking (information retrieval); Context (archaeology); Information retrieval; State (computer science); Artificial intelligence; Machine learning; Theoretical computer science; World Wide Web; Programming language","score_opus":0.010931864353635157,"score_gpt":0.2339228040835175,"score_spread":0.22299093972988235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887329137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007951363,0.00094534026,0.995842,0.00049369194,0.00054335024,0.0004109902,0.0000042727274,0.00030600783,0.0013748495],"genre_scores_gemma":[0.022955343,0.00008408572,0.97267485,0.0005575511,0.00096725073,0.000046496865,0.00003244588,0.00004615294,0.0026358448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966958,0.0000358853,0.00046428174,0.0014654308,0.00073128915,0.0006073234],"domain_scores_gemma":[0.9980037,0.00024182917,0.0002760271,0.0010129459,0.00030744547,0.00015800579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056225184,0.0004010534,0.00033677986,0.00048493582,0.0008313973,0.0006039369,0.0021125944,0.00023210526,0.00006216676],"category_scores_gemma":[0.000057410773,0.00039450199,0.000116379204,0.00095304335,0.0006125247,0.00044012576,0.0008966119,0.0008013875,0.00015577205],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012764687,0.00003022641,0.000078989884,0.000019899058,0.000011410899,0.000012858201,0.00042768952,0.008479506,0.00015480982,0.0028442948,0.00032367595,0.98761535],"study_design_scores_gemma":[0.0002604402,0.00032574442,0.0001849704,0.0004127942,0.00001791274,0.00012930499,5.443417e-7,0.8389553,0.00015723173,0.052023754,0.10661832,0.00091369863],"about_ca_topic_score_codex":0.000012306597,"about_ca_topic_score_gemma":0.0000076580545,"teacher_disagreement_score":0.98670167,"about_ca_system_score_codex":0.00022390466,"about_ca_system_score_gemma":0.00027325907,"threshold_uncertainty_score":0.9998507},"labels":[],"label_agreement":null},{"id":"W2892621690","doi":"10.5220/0006927800550066","title":"Expansion: A Novel Mutation Operator for Genetic Programming","year":2018,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","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":"Concordia University","funders":"","keywords":"Genetic programming; Computer science; Operator (biology); Mutation; Artificial intelligence; Genetics; Biology","score_opus":0.02213526835623724,"score_gpt":0.27822016451985343,"score_spread":0.2560848961636162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892621690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007486664,0.000021046384,0.9905503,0.0010558967,0.00010241126,0.00032316704,0.0000014502257,0.00012606205,0.0003330453],"genre_scores_gemma":[0.21878134,5.8123896e-7,0.78025854,0.00022257725,0.00017941074,0.00021492752,0.000002526952,0.000003296442,0.00033682262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994774,0.000003712686,0.00010292401,0.00021006406,0.00007941911,0.00012649506],"domain_scores_gemma":[0.9995344,0.000023451546,0.000022382894,0.00021417179,0.00016117824,0.00004441296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006149616,0.000050407205,0.000041171334,0.000026337362,0.00021514921,0.000075937845,0.00023738018,0.000021749629,0.000012007216],"category_scores_gemma":[0.000009496885,0.000043315755,0.00002369869,0.00019912873,0.00003063953,0.00018939658,0.000035698005,0.000017439446,0.00004558244],"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":[0.000003350814,0.00029594346,0.000093265226,0.000011810582,0.000013507769,0.0000010324567,0.00078522833,0.00013954598,0.012735034,0.4132974,0.0035193579,0.5691045],"study_design_scores_gemma":[0.00045612425,0.0002687031,0.003798245,0.0000068371787,0.0000042682386,0.00003676219,0.000062183644,0.9001691,0.0063310373,0.0037456872,0.0849436,0.00017748546],"about_ca_topic_score_codex":0.000009541635,"about_ca_topic_score_gemma":0.0000059204294,"teacher_disagreement_score":0.90002954,"about_ca_system_score_codex":0.00001278285,"about_ca_system_score_gemma":0.000049671187,"threshold_uncertainty_score":0.17663652},"labels":[],"label_agreement":null},{"id":"W2893797465","doi":"10.1007/978-3-030-16667-0_1","title":"Deep Learning Concepts for Evolutionary Art","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Brock University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Genetic programming; Deep learning; Classifier (UML); Pattern recognition (psychology); Evolutionary algorithm; Feature (linguistics); Contextual image classification; Heuristic; Machine learning; Image (mathematics)","score_opus":0.013516434680253545,"score_gpt":0.25900320158066004,"score_spread":0.2454867669004065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893797465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005053752,0.00084308704,0.9902816,0.0011677094,0.0011727286,0.00069738383,0.000006228252,0.00018154003,0.0056447093],"genre_scores_gemma":[0.014207927,0.00007419538,0.97577703,0.0010813164,0.00078326976,0.00006513904,0.000047904214,0.000042592834,0.007920609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969153,0.000022209984,0.0004164961,0.0014157462,0.0006450029,0.0005852466],"domain_scores_gemma":[0.99758023,0.00070889253,0.00025249578,0.000984117,0.00033835619,0.00013588423],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048265155,0.0003918696,0.0003881001,0.00042752005,0.0004649068,0.00023700485,0.0023525832,0.00027157972,0.000034157787],"category_scores_gemma":[0.000069511465,0.00038906175,0.00017236339,0.0004252674,0.0004975219,0.00057284,0.00081354525,0.00065732456,0.00026815408],"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":[0.0000046301534,0.000042191048,0.000068609945,0.000044108245,0.00001477525,0.000009373691,0.00030308738,0.24652694,0.000072353076,0.28743085,0.00040190853,0.46508116],"study_design_scores_gemma":[0.00021453122,0.00014154485,0.00017741873,0.000102809245,0.000004959488,0.000039313403,1.6030519e-7,0.8103543,0.000040169387,0.13896036,0.049539305,0.00042512122],"about_ca_topic_score_codex":0.0000034150978,"about_ca_topic_score_gemma":0.000008663355,"teacher_disagreement_score":0.56382734,"about_ca_system_score_codex":0.00031152408,"about_ca_system_score_gemma":0.000633586,"threshold_uncertainty_score":0.9998561},"labels":[],"label_agreement":null},{"id":"W2895736968","doi":"10.3389/fpls.2018.01412","title":"Minimal-Risk Seed Heteromorphism: Proportions of Seed Morphs for Optimal Risk-Averse Heteromorphic Strategies","year":2018,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alexander von Humboldt-Stiftung","keywords":"Biology; Botany","score_opus":0.012707443289538278,"score_gpt":0.23975346731188624,"score_spread":0.22704602402234797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895736968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2303283,0.000051197203,0.7666806,0.00013960106,0.00069760514,0.0005640804,0.0011930419,0.000081215374,0.00026436208],"genre_scores_gemma":[0.5860849,0.000020695725,0.41362292,0.000019608178,0.000054387772,0.000098246106,0.000030451185,0.000006172872,0.00006262596],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977115,0.000055342,0.00048327766,0.00073572196,0.0004888551,0.0005253393],"domain_scores_gemma":[0.9983739,0.0000965254,0.00044475007,0.0006648925,0.00026687572,0.00015304725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088796427,0.00019422612,0.0002762758,0.00041917685,0.0006593524,0.0001441606,0.0015403748,0.000074349104,0.000013610223],"category_scores_gemma":[0.00012839113,0.00018155081,0.00008613127,0.0013404054,0.0015629923,0.0012321152,0.00023026932,0.00018074829,0.000014364187],"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":[0.00095843186,0.005458177,0.44746026,0.00039932108,0.00046221344,0.00017163511,0.025524035,0.048144676,0.08440294,0.1300922,0.23455013,0.022375975],"study_design_scores_gemma":[0.0007340323,0.0005730372,0.08081891,0.00005457087,0.000018841518,0.000048941693,0.00081726344,0.90227395,0.0048401803,0.00677293,0.0026614596,0.00038590436],"about_ca_topic_score_codex":0.00014115665,"about_ca_topic_score_gemma":0.000026082416,"teacher_disagreement_score":0.85412925,"about_ca_system_score_codex":0.00010002945,"about_ca_system_score_gemma":0.00061880786,"threshold_uncertainty_score":0.74034274},"labels":[],"label_agreement":null},{"id":"W2905077346","doi":"10.1109/ictai.2018.00097","title":"Inferring Stochastic L-Systems Using a Hybrid Greedy Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Rewriting; Computer science; A priori and a posteriori; Algorithm; Context (archaeology); Theoretical computer science; Sequence (biology); Formal grammar; Greedy algorithm; Set (abstract data type); Software system; Process (computing); Software; Programming language; Artificial intelligence; Rule-based machine translation","score_opus":0.028344967868779436,"score_gpt":0.26972802123930795,"score_spread":0.2413830533705285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905077346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003969628,0.000060216513,0.99336356,0.00014019686,0.00042304175,0.0001514446,0.000004224737,0.00023717144,0.0016505341],"genre_scores_gemma":[0.5823436,8.703364e-7,0.41644216,0.00010433332,0.00055577385,0.000023853841,0.0000019485199,0.0000086210575,0.00051887834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990535,0.00001895892,0.00018329272,0.00030460165,0.00019319556,0.00024647254],"domain_scores_gemma":[0.9992257,0.000034275636,0.00005460858,0.000445396,0.00014394267,0.00009608049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013950259,0.00010177799,0.000098365934,0.00008396467,0.00031144734,0.00014353797,0.00049144967,0.000023453007,0.00002303567],"category_scores_gemma":[0.000008476064,0.0000937117,0.000033454708,0.0003252353,0.000069437585,0.0004289216,0.00023386262,0.000066429515,0.00020111773],"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":[0.0000030176059,0.0004356082,0.00025584115,0.000024643774,0.00010731002,0.00003264731,0.0008706101,0.015375379,0.0039705434,0.76081675,0.007319382,0.21078824],"study_design_scores_gemma":[0.00010439803,0.000035436944,0.00022690026,0.000011112059,0.0000031624365,0.00012798252,0.000018576764,0.9948863,0.00012160317,0.002413402,0.0019177799,0.00013335963],"about_ca_topic_score_codex":0.00027264474,"about_ca_topic_score_gemma":0.0000033310582,"teacher_disagreement_score":0.9795109,"about_ca_system_score_codex":0.00005145413,"about_ca_system_score_gemma":0.00006810549,"threshold_uncertainty_score":0.38214523},"labels":[],"label_agreement":null},{"id":"W2910950947","doi":"10.1162/978-0-262-31050-5-ch024","title":"An ecology-based evolutionary algorithm to evolve solutions to complex problems","year":2012,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Army Research Office; College of Engineering, Michigan State University; Defense Advanced Research Projects Agency; Michigan State University; Ford Motor Company; National Science Foundation","keywords":"Evolutionary algorithm; Computer science; Suite; Population; Evolutionary computation; Ecology; Natural (archaeology); Artificial intelligence; Evolutionary dynamics; Theoretical computer science; Geography; Biology","score_opus":0.03954680335559447,"score_gpt":0.2837504091749189,"score_spread":0.24420360581932443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910950947","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011208139,0.00006502753,0.98449326,0.0109924255,0.00030955853,0.0007798256,0.000068080495,0.0005520691,0.0016189188],"genre_scores_gemma":[0.22895168,0.0000011365754,0.76645064,0.0034111587,0.00026005943,0.000501435,0.00005929526,0.000013221679,0.0003513591],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978929,0.00008186344,0.00030709204,0.00053567317,0.00032323666,0.0008592454],"domain_scores_gemma":[0.99783003,0.00008439736,0.0000514218,0.000952775,0.00023554159,0.00084583863],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038421023,0.00020711873,0.00017781876,0.00026363798,0.00068829744,0.00007582722,0.0010599714,0.000087442335,0.00024196968],"category_scores_gemma":[0.000018166902,0.00020384399,0.0000798237,0.0011174802,0.000057680758,0.0008555644,0.00031894143,0.00012655821,0.001344958],"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":[0.00000743623,0.0043663117,0.0057909787,0.000017269816,0.000053520085,0.0000030281667,0.0012165845,0.04680165,0.006151362,0.59355223,0.26129308,0.08074656],"study_design_scores_gemma":[0.00024544972,0.00029933837,0.19474946,0.000007158578,0.000008234586,0.000022330183,0.00005317575,0.6887026,0.00008805747,0.0031200938,0.11227147,0.00043263927],"about_ca_topic_score_codex":0.00010367949,"about_ca_topic_score_gemma":0.000039057068,"teacher_disagreement_score":0.64190096,"about_ca_system_score_codex":0.00021955813,"about_ca_system_score_gemma":0.00017401513,"threshold_uncertainty_score":0.9994326},"labels":[],"label_agreement":null},{"id":"W2912336406","doi":"10.32470/ccn.2018.1109-0","title":"Evidence for chunking vs. statistical learning in motor sequence production","year":2018,"lang":"en","type":"article","venue":"2018 Conference on Cognitive Computational Neuroscience","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Chunking (psychology); Computer science; Sequence (biology); Production (economics); Artificial intelligence; Statistical learning; Natural language processing; Speech recognition","score_opus":0.18488984138996217,"score_gpt":0.37914123268283073,"score_spread":0.19425139129286856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912336406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029525647,0.000015525748,0.9660286,0.002697221,0.00055166247,0.000653518,0.000030674768,0.00013892111,0.0003582426],"genre_scores_gemma":[0.94511247,0.000027678103,0.05328317,0.000927674,0.00018195907,0.00021060936,0.000014110865,0.000009545071,0.0002328148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974721,0.00014626925,0.00032823754,0.001083574,0.00055481464,0.0004149746],"domain_scores_gemma":[0.997489,0.0011290826,0.00016762153,0.00020425193,0.0008880509,0.00012202444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055518077,0.00019479511,0.0001655451,0.00022013905,0.0006330784,0.00024161286,0.0006784507,0.00004732831,0.000016839609],"category_scores_gemma":[0.0022763738,0.00020046458,0.00003838512,0.0008154658,0.00066208,0.0010245385,0.00016668449,0.00024193728,0.00008876517],"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":[0.0003010696,0.0006356121,0.0040315366,0.00006954882,0.000007291554,0.000022511773,0.0010691489,0.010359969,0.023108285,0.84194595,0.0007565374,0.11769254],"study_design_scores_gemma":[0.00029254943,0.0009239789,0.07991524,0.00030898608,0.0000035745659,0.000024369816,0.000031936383,0.8783609,0.0006555465,0.03894408,0.00027549217,0.0002633378],"about_ca_topic_score_codex":0.000012864839,"about_ca_topic_score_gemma":0.000007431381,"teacher_disagreement_score":0.91558677,"about_ca_system_score_codex":0.00008646762,"about_ca_system_score_gemma":0.00050313055,"threshold_uncertainty_score":0.8174708},"labels":[],"label_agreement":null},{"id":"W2912577640","doi":"10.1007/978-1-4842-3988-9_10","title":"Searching and Sorting","year":2019,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hamilton Health Sciences","funders":"","keywords":"Sorting; Computer science; Data structure; Sorting algorithm; Theoretical computer science; Linked list; Algorithm","score_opus":0.024667387864618435,"score_gpt":0.24505025491596594,"score_spread":0.2203828670513475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912577640","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035021127,0.00033490002,0.04862431,0.00013577168,0.00009516295,0.00021044195,0.0000046391415,0.00011228869,0.95044744],"genre_scores_gemma":[0.0056147515,0.000027427704,0.024071619,0.00018323156,0.0001807442,0.000015561714,0.0000058552355,0.000028291648,0.96987253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909496,0.0000074092027,0.00016541005,0.00038741244,0.00018147356,0.00016334506],"domain_scores_gemma":[0.9991675,0.00011390064,0.000108622124,0.0005055552,0.000037825244,0.000066601286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102156846,0.0001486523,0.00015017406,0.00006289717,0.00015934935,0.0001082757,0.00045350112,0.00010418246,0.000008256928],"category_scores_gemma":[0.0000044138624,0.0001473775,0.00004928134,0.000005273914,0.000057762227,0.000082459665,0.0005221497,0.0002707988,0.00007840108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.5409048e-7,0.000001398076,0.000002000487,0.000015612804,0.000010934955,0.0000024359156,0.00009471112,0.000004492211,0.000062198145,0.92230517,0.00014699988,0.07735383],"study_design_scores_gemma":[0.00016705929,0.000031661966,0.0000853418,0.00017827407,0.000016333403,0.00004166745,0.000004106022,0.01343941,0.000104458144,0.38972852,0.5957457,0.00045750567],"about_ca_topic_score_codex":0.000015849737,"about_ca_topic_score_gemma":0.0000022178392,"teacher_disagreement_score":0.5955987,"about_ca_system_score_codex":0.000014610154,"about_ca_system_score_gemma":0.00006488073,"threshold_uncertainty_score":0.60098803},"labels":[],"label_agreement":null},{"id":"W2913620949","doi":"","title":"Proceedings of the 9th annual conference companion on Genetic and evolutionary computation","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Evolutionary computation; Computer science; Computation; Artificial intelligence; Programming language","score_opus":0.013130967192827133,"score_gpt":0.24147634732260345,"score_spread":0.22834538012977632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913620949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37109038,0.000043303928,0.6214695,0.0012062375,0.000130628,0.00022556563,0.0000041971466,0.0000619721,0.005768192],"genre_scores_gemma":[0.9172546,0.0000044453977,0.0823564,0.00009320682,0.000024415946,0.0000038448784,8.0394034e-7,0.0000021120175,0.0002601689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993636,0.0000055391424,0.00015400087,0.0001800122,0.00019041549,0.00010644909],"domain_scores_gemma":[0.9995264,0.000050101124,0.00007583981,0.00008769339,0.00022054104,0.000039441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012269951,0.00006269048,0.000060719194,0.000042633066,0.0001442966,0.000022641016,0.00025433363,0.000027920893,0.0000032054504],"category_scores_gemma":[0.0000067392266,0.000045018623,0.000019531148,0.00026685442,0.00009022439,0.00017278834,0.000118968695,0.00006157308,0.000004711544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000583187,0.00020240032,0.011171184,0.000017004835,0.000008432953,3.9908235e-7,0.0009412526,0.0004984921,0.0017810795,0.955417,0.0052791103,0.024677813],"study_design_scores_gemma":[0.00017060863,0.00007390557,0.8538115,0.000017698232,0.0000027183978,0.000033743952,0.00021997398,0.12432589,0.0006691946,0.019653143,0.0009367233,0.000084917956],"about_ca_topic_score_codex":0.000015460877,"about_ca_topic_score_gemma":0.000001678544,"teacher_disagreement_score":0.93576384,"about_ca_system_score_codex":0.000019709352,"about_ca_system_score_gemma":0.00002853013,"threshold_uncertainty_score":0.18358062},"labels":[],"label_agreement":null},{"id":"W2914338467","doi":"","title":"Revised Selected Papers of the 17th European Conference on Genetic Programming - Volume 8599","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Volume (thermodynamics); Computer science","score_opus":0.011571052860941627,"score_gpt":0.21031619780821895,"score_spread":0.19874514494727732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914338467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027440747,0.000027900978,0.86321414,0.0048179897,0.00012116416,0.00055909436,0.000001670316,0.00028861657,0.103528656],"genre_scores_gemma":[0.9283459,0.0000046260884,0.06968004,0.0002041908,0.000033892105,0.000011921202,0.0000010599683,0.0000051728252,0.0017131675],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99920845,0.00012627362,0.0001510234,0.0002149765,0.00016215575,0.0001371337],"domain_scores_gemma":[0.99922687,0.000029697361,0.000075298165,0.00051655044,0.000108951266,0.00004263734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540124,0.00007243708,0.00007172609,0.000019140767,0.00013025616,0.0000435257,0.000713174,0.000015066696,0.000029414927],"category_scores_gemma":[0.000033383913,0.000047928646,0.00003630083,0.00041088118,0.000057233232,0.000047030404,0.00011827646,0.000072442635,0.000054062097],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021764304,0.00022294643,0.0036567603,0.000022983848,0.000018613984,7.0004563e-7,0.00032978432,0.0004395892,0.008912333,0.44251892,0.0027603644,0.54111487],"study_design_scores_gemma":[0.00035926342,0.00022788996,0.5223042,0.00006258214,0.000011342573,0.0000090362555,0.000020055926,0.3865097,0.0009586776,0.0014619802,0.087812066,0.00026324872],"about_ca_topic_score_codex":0.00001353879,"about_ca_topic_score_gemma":0.000003378425,"teacher_disagreement_score":0.9009052,"about_ca_system_score_codex":0.000010037946,"about_ca_system_score_gemma":0.000038100545,"threshold_uncertainty_score":0.19544734},"labels":[],"label_agreement":null},{"id":"W2918004253","doi":"10.1007/978-3-030-03730-7_2","title":"Origins of Stochastic Computing","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","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 Calgary; University of Victoria","funders":"","keywords":"Stochastic computing; Computer science; Constructive; Terminology; Stochastic process; Implementation; Architecture; Stochastic modelling; Theoretical computer science; Software engineering; Algorithm; Mathematics; Programming language; Computation","score_opus":0.01810799894685636,"score_gpt":0.24161918417950437,"score_spread":0.223511185232648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918004253","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003763915,0.00007489542,0.6648939,0.00011713447,0.0001358397,0.00011829524,0.000006452107,0.000062339896,0.33458737],"genre_scores_gemma":[0.029243758,0.000012884282,0.18382677,0.00016994719,0.00024250428,0.0000027428475,0.000024722694,0.000031244374,0.78644544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991493,0.0000028025875,0.00023576089,0.00029668445,0.00020214966,0.00011326446],"domain_scores_gemma":[0.99902,0.0000916934,0.00016792279,0.0005777514,0.0001016792,0.000040950268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000064610824,0.00013865466,0.00020704542,0.00008439083,0.000046635174,0.000017977782,0.00063615775,0.000097885764,0.000102551756],"category_scores_gemma":[0.0000022639024,0.00012820053,0.00009032112,0.000038136277,0.000040466486,0.00007458381,0.00025520183,0.00014338666,0.00043634287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.5242021e-7,0.000007968993,4.4524674e-7,0.000009533548,0.000012241242,4.0385237e-7,0.000014908606,0.0006566702,0.0000062967656,0.993842,0.00057380495,0.004875587],"study_design_scores_gemma":[0.00028256082,0.00011579848,0.00008666134,0.00021854746,0.000029401795,0.000033444863,0.000004564282,0.39942637,0.000023755232,0.26899788,0.33013317,0.0006478296],"about_ca_topic_score_codex":0.000020599951,"about_ca_topic_score_gemma":0.0000014267655,"teacher_disagreement_score":0.7248441,"about_ca_system_score_codex":0.0001013769,"about_ca_system_score_gemma":0.0001542024,"threshold_uncertainty_score":0.560845},"labels":[],"label_agreement":null},{"id":"W2919452396","doi":"10.1162/978-0-262-32621-6-ch108","title":"Evolving Autonomous Agent Controllers as Analytical Mathematical Models","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Autonomous agent; Control engineering; Artificial intelligence; Engineering","score_opus":0.01984280675682549,"score_gpt":0.25598515191446486,"score_spread":0.23614234515763938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919452396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066407723,0.000016630545,0.85217905,0.0034774907,0.00003568958,0.00010304382,3.6019074e-7,0.0001857308,0.1433379],"genre_scores_gemma":[0.77586186,0.0000019472177,0.22006124,0.00061935285,0.00005689769,0.000032106516,9.80997e-7,0.0000053131475,0.0033603278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990282,0.00003059852,0.00021265284,0.00028713408,0.00021431215,0.00022709786],"domain_scores_gemma":[0.9991175,0.00016039735,0.000033919783,0.00046962936,0.00006201123,0.00015652228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025481379,0.00009591167,0.00014726364,0.000048133465,0.0001417169,0.000112966525,0.0004946394,0.000042436426,0.00020054595],"category_scores_gemma":[0.00004125264,0.00007784707,0.000078905075,0.00016325033,0.0000444115,0.00032408364,0.00014950507,0.00007901271,0.00068887096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.3695184e-7,0.00006426713,0.000004907657,0.0000018580371,0.000008323456,9.227005e-7,0.000050250812,0.0032788075,0.000014882767,0.99132586,0.0016672405,0.0035822473],"study_design_scores_gemma":[0.00015183409,0.000022623677,0.00009657616,0.0000026902248,0.0000040045243,0.000011919603,0.000008133387,0.70799196,0.000012059884,0.29005527,0.0015665799,0.00007631062],"about_ca_topic_score_codex":0.000012414897,"about_ca_topic_score_gemma":6.9536515e-7,"teacher_disagreement_score":0.77519774,"about_ca_system_score_codex":0.00004458898,"about_ca_system_score_gemma":0.00005097776,"threshold_uncertainty_score":0.88542724},"labels":[],"label_agreement":null},{"id":"W2937081431","doi":"10.1007/978-3-030-16670-0_11","title":"A Model of External Memory for Navigation in Partially Observable Visual Reinforcement Learning Tasks","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Reinforcement learning; Observability; Recall; Auxiliary memory; State (computer science); Probabilistic logic; Artificial intelligence; Memory model; Process (computing); Human–computer interaction; Shared memory; Cognitive psychology; Programming language","score_opus":0.02948238713968614,"score_gpt":0.2753781425012197,"score_spread":0.24589575536153357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937081431","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087755383,0.00012977101,0.99743,0.00018509361,0.00029533458,0.0006611607,0.0000031805398,0.00004040785,0.00037749455],"genre_scores_gemma":[0.45396262,0.000017044547,0.5447531,0.00020659629,0.00013652247,0.00004648618,0.00001655043,0.00001812811,0.0008429954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757916,0.000016327,0.0005700577,0.0008274946,0.0006111722,0.00039576527],"domain_scores_gemma":[0.99853104,0.0002407052,0.00035139255,0.0005513387,0.0002553646,0.000070128845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007300967,0.0002635828,0.00036191303,0.00033724628,0.00014153417,0.00011393361,0.0013629376,0.0001755031,0.000004528231],"category_scores_gemma":[0.00003278518,0.0002651938,0.000101457255,0.00031819218,0.0001949753,0.00053687155,0.0005453442,0.00043273668,0.0000073122615],"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":[0.000005376171,0.00002253914,0.00004252757,0.00004211032,0.0000023830878,0.0000015410079,0.00031855455,0.90215886,0.0014474969,0.02907916,0.000002443658,0.06687704],"study_design_scores_gemma":[0.00029144695,0.00016706905,0.00006506494,0.00034890938,0.0000036993163,0.000005115423,2.5369985e-7,0.9193213,0.0014536615,0.07802428,0.00006712568,0.0002520514],"about_ca_topic_score_codex":0.000032938915,"about_ca_topic_score_gemma":0.00002860676,"teacher_disagreement_score":0.45308506,"about_ca_system_score_codex":0.00022236041,"about_ca_system_score_gemma":0.0007560699,"threshold_uncertainty_score":0.99998003},"labels":[],"label_agreement":null},{"id":"W2939728762","doi":"10.1109/cvprw.2019.00070","title":"Assessing Architectural Similarity in Populations of Deep Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Similarity (geometry); Computer science; Artificial intelligence; Artificial neural network; Selection (genetic algorithm); Process (computing); Architecture; Architectural design; Deep neural networks; Space (punctuation); Machine learning; Geography","score_opus":0.04659981070802097,"score_gpt":0.3181682499864549,"score_spread":0.2715684392784339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2939728762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051631905,0.00015348851,0.9451089,0.0011000073,0.00044291228,0.00029552952,0.0000032880305,0.00008011195,0.0011838574],"genre_scores_gemma":[0.789946,0.0000022578645,0.20982896,0.00006829366,0.00006008057,0.000023451694,0.00003815603,0.0000056665313,0.000027140413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866444,0.00007864503,0.00040467927,0.00044828,0.00018613797,0.00021780141],"domain_scores_gemma":[0.9987934,0.000099961246,0.00019067782,0.00079359155,0.00007516171,0.00004721345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018668854,0.0001608773,0.00025069062,0.00015364215,0.00006697027,0.00014521457,0.0008811909,0.00016218697,0.000012842552],"category_scores_gemma":[0.000015581414,0.0001531387,0.0001124365,0.0003317339,0.000040949228,0.0002925452,0.0012014128,0.0006081409,0.0000023129614],"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":[4.3957607e-7,0.000052712236,0.012596662,0.000017306043,0.000004217091,8.489554e-7,0.00006873698,0.9400671,0.0000056090284,0.026410602,0.000017916747,0.020757837],"study_design_scores_gemma":[0.00006342789,0.000005152054,0.20170374,0.000016992926,0.0000031475588,0.000004496697,0.000008920655,0.7806396,0.0000040115474,0.017420024,0.000006046872,0.00012446177],"about_ca_topic_score_codex":0.000359454,"about_ca_topic_score_gemma":0.0001916047,"teacher_disagreement_score":0.7383141,"about_ca_system_score_codex":0.000053658194,"about_ca_system_score_gemma":0.000058939524,"threshold_uncertainty_score":0.6244815},"labels":[],"label_agreement":null},{"id":"W2940223619","doi":"10.1007/978-3-030-16670-0_12","title":"Fault Detection and Classification for Induction Motors Using Genetic Programming","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Induction motor; Genetic programming; Feature selection; Artificial intelligence; Fault detection and isolation; Machine learning; Support vector machine; Fault (geology); Genetic algorithm; Decision tree; Feature (linguistics); Feature vector; Pattern recognition (psychology); Feature extraction; Engineering","score_opus":0.031873550370144145,"score_gpt":0.26661555339224285,"score_spread":0.23474200302209872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940223619","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001240638,0.00019124172,0.9963975,0.00024558383,0.00080820743,0.00095574587,0.000002865657,0.000089470166,0.000068769434],"genre_scores_gemma":[0.13420469,0.000023610468,0.8651446,0.00008256186,0.00036419297,0.000040848998,0.0000041884414,0.000020141575,0.0001151884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979903,0.000013509631,0.00030933422,0.0010283911,0.00034884232,0.00030963574],"domain_scores_gemma":[0.9987434,0.00012064067,0.00024040126,0.00057940267,0.00023964711,0.00007649779],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032070727,0.00025103657,0.00020791698,0.00043903923,0.00036210177,0.0003366874,0.0006683218,0.00023396772,0.0000011941279],"category_scores_gemma":[0.000023627234,0.00025164787,0.000058983318,0.00037916205,0.00025532444,0.0005084327,0.00026352395,0.00029008204,0.0000051390475],"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":[0.0000016949446,0.000013384891,0.000021439668,0.000029565455,0.000004193962,6.07733e-7,0.00015162362,0.017225023,0.002494338,0.0062616756,9.309787e-7,0.97379553],"study_design_scores_gemma":[0.0001286807,0.00010138388,0.0008430592,0.00007642232,0.000008461174,0.000053654043,3.6607227e-7,0.9588637,0.00044162382,0.03780504,0.0013954903,0.00028217214],"about_ca_topic_score_codex":0.00001342829,"about_ca_topic_score_gemma":0.000017111723,"teacher_disagreement_score":0.97351336,"about_ca_system_score_codex":0.00026680387,"about_ca_system_score_gemma":0.0002576979,"threshold_uncertainty_score":0.99999356},"labels":[],"label_agreement":null},{"id":"W2942631528","doi":"10.13164/mendel.2018.1.017","title":"The Effects of CMA-ES Style Selection and Restart Criteria on DE","year":2018,"lang":"en","type":"article","venue":"MENDEL","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Selection (genetic algorithm); Population; Set (abstract data type); Operator (biology); Differential evolution; Function (biology); Computer science; Mathematics; Artificial intelligence; Evolutionary biology; Sociology; Biology; Demography","score_opus":0.008410064329425423,"score_gpt":0.26269854920294256,"score_spread":0.25428848487351713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942631528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63527435,0.00018520263,0.3598123,0.0024197083,0.00024144688,0.00019849982,0.0000016776902,0.0000771795,0.0017896551],"genre_scores_gemma":[0.9855957,0.000027104239,0.013895826,0.00008767058,0.000078713565,0.000019414034,2.6982642e-7,0.0000020765683,0.0002932254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967295,0.000028661329,0.00005315191,0.00009890336,0.000062992935,0.000083368686],"domain_scores_gemma":[0.99967456,0.00011145093,0.00002276667,0.00013794842,0.000035230245,0.000018069673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120691715,0.00003317188,0.00003122838,0.000015585607,0.00021081821,0.000026893522,0.00012497976,0.00001461877,0.0000017069202],"category_scores_gemma":[0.000021105256,0.000023715933,0.000008698784,0.000097266064,0.000042691667,0.0000647442,0.00003968535,0.000029638371,0.00000596974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018809475,0.00018187451,0.0006772457,0.00003923281,0.000025154344,0.0000012999353,0.0017637685,0.000015079032,0.09101127,0.80054796,0.027414534,0.07830378],"study_design_scores_gemma":[0.000708175,0.0017664854,0.11038184,0.0000755732,0.000015206586,0.00003885616,0.000086902444,0.57970816,0.13718536,0.1002883,0.0694724,0.00027271127],"about_ca_topic_score_codex":0.000015678728,"about_ca_topic_score_gemma":0.000010687464,"teacher_disagreement_score":0.7002597,"about_ca_system_score_codex":0.000012508729,"about_ca_system_score_gemma":0.00001751617,"threshold_uncertainty_score":0.16214645},"labels":[],"label_agreement":null},{"id":"W2949948747","doi":"10.48550/arxiv.1408.2314","title":"A Tentative Role for FOXP2 in the Evolution of Dual Processing Modes and\\n Generative Abilities","year":2014,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"FOXP2; Dual (grammatical number); Generative grammar; Cognitive science; Psychology; Computer science; Cognitive psychology; Artificial intelligence; Biology; Philosophy; Linguistics; Genetics; Gene","score_opus":0.054381765751771116,"score_gpt":0.2094161924522375,"score_spread":0.15503442670046638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949948747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2860165,0.00032653508,0.71190953,0.00029935854,0.00003612546,0.00087370997,0.000053022548,0.00001938568,0.00046583463],"genre_scores_gemma":[0.9871014,0.00008712941,0.012325052,0.00003284888,0.000089458226,0.000036792768,0.000014844219,0.00001064511,0.00030183652],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978044,0.00033389477,0.00034029144,0.0010610957,0.00012445373,0.00033585203],"domain_scores_gemma":[0.99796057,0.0004582247,0.00045808667,0.00058090535,0.00047016292,0.00007204407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006821707,0.0003137664,0.00038107598,0.00023527951,0.0005510133,0.000099098135,0.0008676636,0.00018967067,0.0000029388818],"category_scores_gemma":[0.000049457714,0.00029204573,0.00016196439,0.0007049452,0.0006400755,0.00060380873,0.00057879806,0.0003370566,0.0000020735683],"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":[0.00005443798,0.00025203227,0.0016439079,0.00020164387,0.00003735164,0.0000024905635,0.008649529,0.25320315,0.00023149149,0.7337667,0.000014237728,0.0019430531],"study_design_scores_gemma":[0.00048959703,0.00014762068,0.004649894,0.00009012266,0.000061022085,0.0000049537916,0.016170925,0.77204657,0.00010310366,0.20595123,0.000046081655,0.00023890566],"about_ca_topic_score_codex":0.0005137539,"about_ca_topic_score_gemma":0.00013391845,"teacher_disagreement_score":0.7010849,"about_ca_system_score_codex":0.00033798118,"about_ca_system_score_gemma":0.0004894969,"threshold_uncertainty_score":0.99995315},"labels":[],"label_agreement":null},{"id":"W2950598025","doi":"10.48550/arxiv.1607.05213","title":"mpEAd: Multi-Population EA Diagrams","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Notation; Formalism (music); Computer science; Simple (philosophy); Theoretical computer science; Population; Artificial intelligence; Mathematics; Epistemology","score_opus":0.07301346330097529,"score_gpt":0.1971488351184071,"score_spread":0.12413537181743182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950598025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040558767,0.0000515298,0.9569124,0.0003340275,0.00042491552,0.000271346,0.000027205888,0.0003406972,0.0010791194],"genre_scores_gemma":[0.9857674,0.0000808573,0.01080554,0.000049096718,0.00013558097,0.000003906623,0.00004487691,0.000012923818,0.0030997694],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845874,0.00007281059,0.00017208223,0.00094256137,0.000079086836,0.00027471525],"domain_scores_gemma":[0.99828625,0.00006455435,0.00019992283,0.0011867803,0.00011768276,0.00014481631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012687861,0.00022892645,0.00019346026,0.000149429,0.00022319435,0.000076127275,0.001390297,0.00022463195,0.00002272603],"category_scores_gemma":[0.000014618582,0.00022911289,0.00016456441,0.00036505528,0.00006634867,0.0004070223,0.0013176869,0.0002779338,0.00026018076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046537402,0.00022333962,0.012347415,0.000034027405,0.00004978952,0.00005398191,0.00008706085,0.032361783,0.000042212785,0.9487636,0.0005421178,0.005490038],"study_design_scores_gemma":[0.00044465202,0.000019235184,0.042012755,0.000057532434,0.000029610997,0.0000036021513,0.000012265848,0.77015656,0.00002935872,0.18525933,0.0014865709,0.00048852136],"about_ca_topic_score_codex":0.00015344158,"about_ca_topic_score_gemma":0.000037765294,"teacher_disagreement_score":0.94610685,"about_ca_system_score_codex":0.0002009251,"about_ca_system_score_gemma":0.00008184212,"threshold_uncertainty_score":0.9342953},"labels":[],"label_agreement":null},{"id":"W2952356721","doi":"10.1038/npre.2009.3913","title":"Design of a dynamic model of genes with multiple autonomous regulatory modules by evolution in silico","year":2010,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"In silico; Benchmark (surveying); Crossover; Computer science; Exploit; Evolutionary algorithm; Genetic algorithm; Computational biology; Artificial intelligence; Gene; Machine learning; Biology; Genetics","score_opus":0.007717461571683098,"score_gpt":0.22871241982902168,"score_spread":0.2209949582573386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952356721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19365336,0.0017127396,0.8035229,0.00018862492,0.00014938413,0.00058462954,0.000069334026,0.00008326815,0.000035787918],"genre_scores_gemma":[0.6442429,0.00004537434,0.35549286,0.000008087372,0.000013947305,0.00010831114,0.000023535385,0.00001387196,0.00005115086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982359,0.00004092715,0.00042381152,0.0006935453,0.00037472942,0.0002310973],"domain_scores_gemma":[0.9982216,0.00010687957,0.00048507532,0.00082325295,0.00030116417,0.00006206194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034780707,0.0002661707,0.00038165174,0.0002492399,0.000054838765,0.00002392001,0.0011585703,0.00092121627,0.0000011960112],"category_scores_gemma":[0.00003793675,0.00024935493,0.00007695339,0.00031361787,0.00014505639,0.00023257635,0.00055127224,0.00132396,5.375485e-7],"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":[0.000049892173,0.0004275486,0.0010512418,0.00025628653,0.00005757445,6.2745596e-7,0.00085687963,0.5693397,0.40350887,0.017796278,0.00030383188,0.0063512577],"study_design_scores_gemma":[0.0002787991,0.00003928485,0.003873421,0.00013442211,0.000014072024,0.000003919278,0.000008888089,0.9708791,0.010251295,0.014265915,0.00001391627,0.00023697955],"about_ca_topic_score_codex":0.00010671083,"about_ca_topic_score_gemma":0.00005403931,"teacher_disagreement_score":0.4505895,"about_ca_system_score_codex":0.00018801977,"about_ca_system_score_gemma":0.0003440189,"threshold_uncertainty_score":0.9999959},"labels":[],"label_agreement":null},{"id":"W2955923207","doi":"10.1002/cjce.23590","title":"Prediction of pressure drop and minimum spouting velocity in draft tube conical spouted beds using genetic programming approach","year":2019,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dimensionless quantity; Pressure drop; Conical surface; Genetic programming; Draft tube; Mechanics; Tube (container); Mathematics; Physics; Geometry; Mechanical engineering; Computer science; Engineering; Artificial intelligence","score_opus":0.011526684272667672,"score_gpt":0.19266883955347494,"score_spread":0.18114215528080727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955923207","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9321413,0.00038427892,0.067107975,0.00015522746,0.00006598619,0.00011880051,0.0000031518825,0.0000077335935,0.000015543117],"genre_scores_gemma":[0.93617845,0.0000010840332,0.06374192,0.000007265946,0.000060318285,0.0000016034558,6.9077396e-7,0.0000058898345,0.0000027851486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925524,0.0000141566225,0.0002960127,0.00010857323,0.00013165076,0.0001943907],"domain_scores_gemma":[0.99948597,0.000054120035,0.00009523457,0.00013857165,0.00006788138,0.00015824234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027299134,0.00007657303,0.00015067193,0.000092286995,0.000037217684,0.000038028717,0.00030504496,0.00006361676,0.0000018685796],"category_scores_gemma":[0.00004500028,0.00006550514,0.000037305792,0.00026091494,0.000048847665,0.00012849961,0.000037030753,0.0002715925,1.8766347e-7],"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":[0.000023522094,0.00015925203,0.029775329,0.0005242174,0.00027255516,0.000046295834,0.00580933,0.6483662,0.2819404,0.011984231,0.000046794226,0.021051856],"study_design_scores_gemma":[0.00022427166,0.000016748943,0.0079415245,0.00007385299,0.000013970055,0.00018193749,0.000017522138,0.9895014,0.0017451444,0.000077127166,0.00014157541,0.00006488469],"about_ca_topic_score_codex":0.00038406815,"about_ca_topic_score_gemma":0.000008075112,"teacher_disagreement_score":0.3411352,"about_ca_system_score_codex":0.00007278571,"about_ca_system_score_gemma":0.00023280577,"threshold_uncertainty_score":0.2671222},"labels":[],"label_agreement":null},{"id":"W2961864530","doi":"10.1145/3319619.3322029","title":"Benchmarking genetic programming in dynamic insider threat detection","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmarking; Insider threat; Computer science; Dynamic programming; Insider; Genetic programming; Computer security; Artificial intelligence; Business; Algorithm; Political science","score_opus":0.011019953629421791,"score_gpt":0.22740538863839432,"score_spread":0.21638543500897253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961864530","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81817955,0.00028740958,0.18015829,0.00040860046,0.00020432274,0.000523818,0.0000011577798,0.000053717486,0.0001831105],"genre_scores_gemma":[0.91369843,0.000063284606,0.08610085,0.00002701021,0.000027745715,0.000039544546,0.0000024245835,0.000007497548,0.000033223383],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986371,0.000022003222,0.0003864031,0.00043549176,0.0002919714,0.00022704952],"domain_scores_gemma":[0.9992241,0.00005242452,0.00023874071,0.00013799612,0.00029419406,0.000052531337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001377121,0.00016119429,0.00018215175,0.00016352291,0.00019157253,0.00008355738,0.00041570506,0.0000725653,0.0000065468034],"category_scores_gemma":[0.000011601175,0.00014291017,0.00005285157,0.00061827136,0.00009501022,0.00037250714,0.00030192142,0.00015717011,0.000007682657],"study_design_candidate":"observational","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":[0.00003415822,0.00045211095,0.4242276,0.00037944195,0.000053548756,9.3236275e-7,0.0024294087,0.036900602,0.034541372,0.03851877,0.00011829503,0.46234378],"study_design_scores_gemma":[0.00019555374,0.00004839646,0.49840567,0.000059895905,0.00000439554,0.000032152322,0.00008451663,0.48563388,0.00009538345,0.015285924,0.000056166355,0.000098077995],"about_ca_topic_score_codex":0.000046495137,"about_ca_topic_score_gemma":0.000009806762,"teacher_disagreement_score":0.4622457,"about_ca_system_score_codex":0.00009277631,"about_ca_system_score_gemma":0.00007373352,"threshold_uncertainty_score":0.58277076},"labels":[],"label_agreement":null},{"id":"W2963131153","doi":"10.1162/isal_a_00204","title":"A-life Evolution with Human Proxies","year":2019,"lang":"en","type":"article","venue":"The 2019 Conference on Artificial Life","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Geology","score_opus":0.032208085897254976,"score_gpt":0.26134538385550937,"score_spread":0.22913729795825438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963131153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59834856,0.00008767998,0.31152853,0.049144756,0.0006208515,0.0017722799,0.000026489803,0.0006266582,0.03784419],"genre_scores_gemma":[0.9957842,0.000002960352,0.001308749,0.0007189919,0.00016483698,0.00005108901,0.00000824339,0.000009271578,0.0019516543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871117,0.00007478485,0.000215078,0.00037008632,0.00036087938,0.0002680287],"domain_scores_gemma":[0.99868673,0.000060940456,0.00012112257,0.0008315837,0.00014379792,0.00015581466],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025332265,0.00015000596,0.00014765795,0.00006071767,0.00040058434,0.00017870989,0.0008519121,0.000050027076,0.00012702173],"category_scores_gemma":[0.00003280465,0.00009599415,0.000041650066,0.00030244706,0.00012421867,0.00030121405,0.000120121724,0.00018944903,0.002311081],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012767531,0.00008276495,0.0002797559,0.0000027012766,0.000009852334,2.987626e-7,0.00017337757,0.00026436482,0.0030527285,0.99327916,0.0020386789,0.0008035523],"study_design_scores_gemma":[0.0015269251,0.0024320788,0.13577773,0.0002484464,0.00006324221,0.000024232493,0.0013692988,0.40121126,0.005703334,0.4266503,0.022940295,0.0020528617],"about_ca_topic_score_codex":0.000095304116,"about_ca_topic_score_gemma":0.000024061921,"teacher_disagreement_score":0.5666289,"about_ca_system_score_codex":0.000035122634,"about_ca_system_score_gemma":0.0003885485,"threshold_uncertainty_score":0.9984657},"labels":[],"label_agreement":null},{"id":"W2963190675","doi":"","title":"Symbolic regression by random search","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Symbolic regression; Hyperparameter; Genetic programming; Random search; Mathematics; Regression; Statistics; Binary number; Regression analysis; Random forest; Computer science; Algorithm; Artificial intelligence; Arithmetic","score_opus":0.05094565646964204,"score_gpt":0.20201521114838875,"score_spread":0.15106955467874672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963190675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08141039,0.00027198135,0.9125355,0.00045107724,0.0003566705,0.00042662682,0.000036318164,0.00023162902,0.0042797863],"genre_scores_gemma":[0.98636174,0.00034832372,0.0020871582,0.0000723023,0.00007050693,0.0000023626387,0.000057676887,0.000013413755,0.010986507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982762,0.00012576181,0.00014987776,0.0010138026,0.00012351775,0.00031089399],"domain_scores_gemma":[0.9980534,0.00010805315,0.00012620387,0.0014244574,0.00013458884,0.0001533525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021346235,0.00023439477,0.00026723137,0.00015144993,0.00021489846,0.000094941366,0.0019386089,0.00024670822,0.00003864629],"category_scores_gemma":[0.000008228097,0.000240249,0.0001708362,0.0004922203,0.000081055936,0.00027873644,0.0020210335,0.0005659256,0.00043323956],"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":[0.00007487388,0.0005762148,0.0038285705,0.00020480357,0.00016443894,0.00013166026,0.00050724024,0.33043858,0.0006389269,0.6184713,0.0409528,0.004010573],"study_design_scores_gemma":[0.000990957,0.0000281043,0.0008284321,0.00007983762,0.000025788784,0.0000049042883,0.000034527326,0.9685284,0.00022934026,0.022878256,0.0059550684,0.00041639688],"about_ca_topic_score_codex":0.00012377386,"about_ca_topic_score_gemma":0.0000021564779,"teacher_disagreement_score":0.9104484,"about_ca_system_score_codex":0.00014417003,"about_ca_system_score_gemma":0.00020720574,"threshold_uncertainty_score":0.97970706},"labels":[],"label_agreement":null},{"id":"W2967706363","doi":"10.1109/cec.2019.8789964","title":"EvoDNN - An Evolutionary Deep Neural Network with Heterogeneous Activation Functions","year":2019,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial neural network; Activation function; Backpropagation; Flexibility (engineering); Artificial intelligence; Evolutionary algorithm; Deep learning; Feature (linguistics); Differentiable function; Evolutionary computation; Function (biology); Stochastic neural network; Time delay neural network; Mathematics; Biology","score_opus":0.00948284211519159,"score_gpt":0.21385724989163354,"score_spread":0.20437440777644195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967706363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10130622,0.000052126252,0.8932039,0.0010771903,0.00020592847,0.00031974821,0.0000018248221,0.00038589197,0.0034471685],"genre_scores_gemma":[0.8883741,0.0000020504524,0.10938349,0.00043016113,0.00021411992,0.00006854038,0.000044775396,0.000011079365,0.0014716721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998878,0.000040090665,0.00014876084,0.0004273191,0.00023134657,0.00027446865],"domain_scores_gemma":[0.99901676,0.000048556434,0.00006170988,0.00065135397,0.000111503425,0.00011011869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066707864,0.00012969814,0.00009583087,0.00005315143,0.00031064978,0.00007749278,0.00040510684,0.000050142713,0.00017333674],"category_scores_gemma":[0.0000019804806,0.00010693229,0.000040245304,0.00052665255,0.00003151301,0.0011015661,0.000094099996,0.00011121296,0.00027915667],"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":[0.000027557639,0.00036594432,0.010019333,0.000006873393,0.0000462947,0.0000036878769,0.00012645333,0.8382605,0.0006314429,0.12199457,0.0030880533,0.02542932],"study_design_scores_gemma":[0.00022546649,0.00026589562,0.043518346,0.0000043498103,0.0000048248003,0.00008432469,0.000027024656,0.94784415,0.000045423334,0.0025270458,0.0052516884,0.000201459],"about_ca_topic_score_codex":0.000037916263,"about_ca_topic_score_gemma":0.00001617445,"teacher_disagreement_score":0.7870679,"about_ca_system_score_codex":0.00005863046,"about_ca_system_score_gemma":0.000049728264,"threshold_uncertainty_score":0.4360572},"labels":[],"label_agreement":null},{"id":"W2967780548","doi":"10.3233/jifs-179361","title":"Optimizing predictability of rating scales by differential evolution for the use by collective intelligent information and database systems","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Predictability; Rating scale; Differential evolution; Computer science; Differential (mechanical device); Scale (ratio); Rating system; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Engineering","score_opus":0.021141157886429993,"score_gpt":0.24563167539529773,"score_spread":0.22449051750886773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967780548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06894302,0.0043768017,0.9240216,0.00012624185,0.0010567483,0.0012505522,0.00019140932,0.000015861784,0.00001775904],"genre_scores_gemma":[0.99455756,0.0002910668,0.0048189694,0.000010243407,0.00011824474,0.00006751208,0.000026556843,0.000007548138,0.000102295664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776655,0.00015138787,0.0012085566,0.00018349793,0.0004920352,0.00019795039],"domain_scores_gemma":[0.9966544,0.0008721473,0.0011793058,0.00035970434,0.00083314296,0.00010131604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010014846,0.00016158761,0.0003360482,0.00013065587,0.00022292636,0.00032784932,0.0005145968,0.00007352705,0.000001756733],"category_scores_gemma":[0.0001627017,0.000111382156,0.000118705226,0.00027979026,0.000072245806,0.0016020035,0.0001326634,0.00018984986,0.0000025445856],"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":[0.001564965,0.0033284961,0.046244994,0.0060349638,0.0030887518,0.000003911492,0.024869522,0.47036543,0.08308066,0.1824253,0.15308502,0.025907988],"study_design_scores_gemma":[0.00046220428,0.0004246326,0.0004975321,0.00038770976,0.00005130111,0.00008218154,0.002851367,0.98734134,0.0019152603,0.000111992485,0.00569662,0.00017785678],"about_ca_topic_score_codex":0.0002254105,"about_ca_topic_score_gemma":0.000003559971,"teacher_disagreement_score":0.92561454,"about_ca_system_score_codex":0.00029158793,"about_ca_system_score_gemma":0.00014424715,"threshold_uncertainty_score":0.45420328},"labels":[],"label_agreement":null},{"id":"W2971785444","doi":"10.1017/s0140525x01244161","title":"A single-process learning theory","year":2001,"lang":"en","type":"article","venue":"Behavioral and Brain Sciences","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Analogy; Natural selection; Process (computing); Extension (predicate logic); Punishment (psychology); Reinforcement learning; Selection (genetic algorithm); Learning theory; Psychology; Cognitive science; Adaptation (eye); Computer science; Artificial intelligence; Cognitive psychology; Epistemology; Social psychology; Neuroscience; Philosophy","score_opus":0.05305033914085085,"score_gpt":0.32296632030111017,"score_spread":0.26991598116025933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971785444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91217107,0.00021842308,0.079777576,0.003674909,0.000062499435,0.00006713021,5.388554e-7,0.0001344454,0.003893392],"genre_scores_gemma":[0.9888134,0.000013502092,0.009537302,0.00021958024,0.000037295238,0.000013919269,6.3731875e-7,0.0000018149859,0.001362532],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992224,0.00003768238,0.000089430294,0.00028768784,0.00018268391,0.00018013074],"domain_scores_gemma":[0.99970436,0.00007114918,0.000036552407,0.00009383258,0.000029923374,0.000064169835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003974428,0.00006561625,0.000058437185,0.000053524174,0.00058878155,0.00017572267,0.00040870035,0.000022893008,0.000020115882],"category_scores_gemma":[0.000015155765,0.000050296458,0.000019472458,0.0005459501,0.000272274,0.0005412718,0.00009596975,0.00006738068,0.00001140721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028598363,0.00031138927,0.030171096,0.0000034754964,0.000001751195,0.0000159703,0.0016249967,0.000118113676,0.003997777,0.26115903,0.0002737129,0.70231986],"study_design_scores_gemma":[0.001148119,0.0026345241,0.16743144,0.00010343107,0.000030787345,0.0011216238,0.006958492,0.10856109,0.0022662715,0.5712821,0.13667205,0.0017900672],"about_ca_topic_score_codex":0.000017789002,"about_ca_topic_score_gemma":0.000003879882,"teacher_disagreement_score":0.70052975,"about_ca_system_score_codex":0.000006242246,"about_ca_system_score_gemma":0.00002973553,"threshold_uncertainty_score":0.45284912},"labels":[],"label_agreement":null},{"id":"W2975651211","doi":"10.1109/iccse.2019.8845515","title":"Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting","year":2019,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Genetic programming; Computer science; Diabetes mellitus; Symbolic regression; Predictive modelling; Machine learning; Voting; Genetic algorithm; Majority rule; Computation; Artificial intelligence; Data mining; Medicine; Algorithm","score_opus":0.013768260042193723,"score_gpt":0.2396370995375081,"score_spread":0.2258688394953144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975651211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42544216,0.00030817723,0.57371825,0.000047813675,0.00008184123,0.00022816967,0.0000013242568,0.00011006992,0.00006219491],"genre_scores_gemma":[0.58429164,0.000005291546,0.41558513,0.000016827911,0.000031175674,0.000012886316,9.3170587e-7,0.000003143879,0.00005300627],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922657,0.000023862534,0.00012964697,0.00031972703,0.000107762935,0.00019242182],"domain_scores_gemma":[0.99961275,0.000041417567,0.00004727933,0.00018162985,0.00006190715,0.000054997625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011782423,0.000080601945,0.0000794055,0.00004438096,0.00017447675,0.00009307214,0.00014746006,0.0000373568,0.000004789079],"category_scores_gemma":[0.0000062003573,0.00007537487,0.000023165334,0.00021814526,0.000028073053,0.00034334254,0.00013889045,0.000073470736,0.000013376498],"study_design_candidate":"observational","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":[0.0000011887103,0.00024060601,0.783201,0.000059231585,0.000045327855,0.000001287745,0.0008263486,0.0020284753,0.0073554046,0.028595973,0.0000132385385,0.17763197],"study_design_scores_gemma":[0.00014083789,0.000027617274,0.19503164,0.000010248845,0.000004373138,0.0000049785476,0.000044228287,0.80373955,0.0004588473,0.00032610726,0.00013207621,0.00007950914],"about_ca_topic_score_codex":0.00006337551,"about_ca_topic_score_gemma":0.000003934421,"teacher_disagreement_score":0.8017111,"about_ca_system_score_codex":0.000036021094,"about_ca_system_score_gemma":0.000023783607,"threshold_uncertainty_score":0.30736977},"labels":[],"label_agreement":null},{"id":"W2977089467","doi":"10.1109/iccse.2019.8845381","title":"Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models","year":2019,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Symbolic regression; Genetic programming; Computer science; Machine learning; Artificial intelligence; Field (mathematics); Data mining; Regression analysis; Data modeling; Regression; Predictive modelling; Statistics; Mathematics","score_opus":0.05274053156540136,"score_gpt":0.26396420923016234,"score_spread":0.21122367766476097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977089467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020888485,0.00001795506,0.9667883,0.00016916048,0.000024759467,0.00030458477,0.0000027109572,0.00040966136,0.011394414],"genre_scores_gemma":[0.37145996,0.0000016938548,0.628101,0.00006881042,0.000030159417,0.00003394809,0.0000073511487,0.0000056811655,0.00029139165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992211,0.000010688334,0.00010610327,0.00036519923,0.00016206474,0.00013486658],"domain_scores_gemma":[0.99901235,0.000019673256,0.000039146234,0.00086662726,0.000028767432,0.000033462326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009670389,0.0000742644,0.000069728565,0.000045733024,0.00007933005,0.000080804406,0.0006560667,0.000023716877,0.000006611838],"category_scores_gemma":[8.8122357e-7,0.000057328914,0.000009166909,0.00017852813,0.00001568358,0.0004970825,0.00020944493,0.000056992354,0.00002304029],"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":[0.00002009752,0.0006614639,0.05619987,0.00004836953,0.00008177203,0.0000034916493,0.0008739874,0.039492484,0.0010913095,0.32879132,0.0026833916,0.57005244],"study_design_scores_gemma":[0.00008576065,0.00010061352,0.0023409019,0.000032784563,0.000002646348,0.000009432291,0.000025390673,0.9899281,0.00018267262,0.0005307078,0.006673371,0.00008760459],"about_ca_topic_score_codex":0.000038439655,"about_ca_topic_score_gemma":0.0000079464635,"teacher_disagreement_score":0.95043564,"about_ca_system_score_codex":0.000017737053,"about_ca_system_score_gemma":0.000023412684,"threshold_uncertainty_score":0.23378052},"labels":[],"label_agreement":null},{"id":"W2991199419","doi":"10.6084/m9.figshare.7990835.v2","title":"Nigeria’s 2015 Presidential Election: A Spatial and Econometric Perspective Based on a Framing Strategy - Online Appendix","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Polytechnique Montréal","funders":"","keywords":"Framing (construction); Presidential system; Perspective (graphical); Presidential election; Political science; Computer science; Geography; Politics; Law; Artificial intelligence","score_opus":0.017808995329811225,"score_gpt":0.266649383999074,"score_spread":0.24884038866926278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991199419","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09198202,0.0076703685,0.22193065,0.019476486,0.0029761477,0.014021762,0.5601293,0.0038497786,0.07796352],"genre_scores_gemma":[0.9804307,0.0000027575786,0.0030911597,0.00020056005,0.00037292516,0.0001376023,0.015144724,0.000014521284,0.0006050385],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991415,0.000024519484,0.00011712929,0.00040263816,0.00014705188,0.00016716478],"domain_scores_gemma":[0.99938285,0.00008960072,0.00007384001,0.00028829882,0.000092336675,0.00007308009],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000031504722,0.00010873294,0.000103787475,0.00017781604,0.00011382903,0.00015250748,0.00027387476,0.000058449106,0.026360027],"category_scores_gemma":[0.000055917688,0.00011123205,0.000040144874,0.00036484704,0.000004816936,0.00031081386,0.000096967524,0.00013838428,0.0025189535],"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":[0.00013131491,0.002819077,0.0025574707,0.00044457125,0.00022132132,0.00007440839,0.001506428,0.045049045,0.0004954074,0.024507701,0.83981454,0.08237872],"study_design_scores_gemma":[0.0006679341,0.00043577375,0.03715605,0.00015555292,0.0000038870435,0.000017619399,0.00010922364,0.93579185,0.00017029057,0.0010805767,0.024078839,0.00033241513],"about_ca_topic_score_codex":0.00009901876,"about_ca_topic_score_gemma":0.000046580513,"teacher_disagreement_score":0.8907428,"about_ca_system_score_codex":0.0000812741,"about_ca_system_score_gemma":0.00014375288,"threshold_uncertainty_score":0.9982577},"labels":[],"label_agreement":null},{"id":"W2992884018","doi":"10.1109/cw.2019.00053","title":"Person Identification from Visual Aesthetics Using Gene Expression Programming","year":2019,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Genetic programming; Artificial intelligence; Classifier (UML); Biometrics; Identification (biology); Curse of dimensionality; Expression (computer science); Dimensionality reduction; Domain (mathematical analysis); Machine learning; Computer vision; Mathematics","score_opus":0.024319413464244106,"score_gpt":0.278049378117753,"score_spread":0.2537299646535089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992884018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38748783,0.000041772353,0.61188304,0.00015977754,0.00009923089,0.00013009,9.487746e-7,0.00009195232,0.000105369414],"genre_scores_gemma":[0.5983947,0.0000023997957,0.40121418,0.000028864617,0.00004295966,0.000010075093,0.000012485088,0.000004444509,0.00028990698],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992198,0.000020598693,0.0001292477,0.00031125208,0.0001856177,0.00013352577],"domain_scores_gemma":[0.9994552,0.00001906539,0.000069926726,0.00035294215,0.000056084056,0.00004675732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008713176,0.00007176916,0.000063294465,0.000039840175,0.00013405681,0.00013421944,0.0003154675,0.000041357944,0.000035109588],"category_scores_gemma":[0.0000027338258,0.00006558496,0.000034762077,0.00019215819,0.000014267258,0.00040771105,0.00008353975,0.000054822463,0.00018215444],"study_design_candidate":"bench_or_experimental","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":[0.0000021783123,0.0002525105,0.004041586,0.000005929792,0.000008027535,0.0000014260568,0.00081787707,0.00083703274,0.8952948,0.016202062,0.00018764324,0.082348935],"study_design_scores_gemma":[0.00015956518,0.00002648117,0.0054247254,0.000014709627,0.0000043030227,0.0000067514998,0.00018331675,0.9225808,0.068588644,0.00096140814,0.0018814481,0.00016788983],"about_ca_topic_score_codex":0.000068966474,"about_ca_topic_score_gemma":7.2891606e-7,"teacher_disagreement_score":0.92174375,"about_ca_system_score_codex":0.000035349676,"about_ca_system_score_gemma":0.000027889559,"threshold_uncertainty_score":0.2674477},"labels":[],"label_agreement":null},{"id":"W2994040121","doi":"","title":"Vergence Adaptation And Convergence Accommodation In Convergence Insufficiency","year":2012,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Accommodation; Convergence insufficiency; Vergence (optics); Convergence (economics); Adaptation (eye); Computer science; Psychology; Medicine; Artificial intelligence; Neuroscience; Economics; Ophthalmology; Macroeconomics","score_opus":0.05396535678715794,"score_gpt":0.32675206097774645,"score_spread":0.2727867041905885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994040121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860257,0.00028095866,0.0116532575,0.00057606434,0.00050098455,0.00028437193,0.0000025549582,0.00006482155,0.0006112869],"genre_scores_gemma":[0.9685237,0.000030128316,0.031091979,0.0001677521,0.000043038723,0.00009326587,0.000003341546,0.000005916113,0.00004087112],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99760526,0.00015003908,0.00038580954,0.0007093557,0.00046262593,0.0006868902],"domain_scores_gemma":[0.99878544,0.00014354939,0.00019160371,0.00035968522,0.0002037404,0.00031600377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012666371,0.00020591362,0.00018708855,0.0003002523,0.00051441207,0.00007208913,0.00102729,0.000088377616,0.000051146573],"category_scores_gemma":[0.00033534536,0.00020608991,0.000025244362,0.0027456088,0.0023041272,0.0034930513,0.0005140462,0.00022959159,0.00011943901],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009582176,0.0010703208,0.5133978,0.000025154715,0.000008863575,0.000026898093,0.01095689,0.0009598425,0.2957377,0.17189567,0.00007893846,0.0058322866],"study_design_scores_gemma":[0.00018343236,0.0002013094,0.7669868,0.00002159401,0.0000031336956,0.00015440374,0.00043877133,0.20232797,0.02002055,0.009235478,0.00010467487,0.00032190318],"about_ca_topic_score_codex":0.00018720028,"about_ca_topic_score_gemma":0.0000035961154,"teacher_disagreement_score":0.27571717,"about_ca_system_score_codex":0.00014238009,"about_ca_system_score_gemma":0.0003176572,"threshold_uncertainty_score":0.8489652},"labels":[],"label_agreement":null},{"id":"W3004052295","doi":"","title":"MADMS: Mesh adaptive direct multisearch for blackbox multiobjective optimization","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Multi-objective optimization; Mathematical optimization; Mathematics; Machine learning","score_opus":0.019181359863907614,"score_gpt":0.2527260271919862,"score_spread":0.2335446673280786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004052295","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047118342,0.0005816719,0.972181,0.0054834527,0.00032918726,0.0021061795,0.0002623121,0.0004589288,0.018126037],"genre_scores_gemma":[0.18281862,0.00043710074,0.806201,0.000091602524,0.00005759587,0.00096994726,0.0006932699,0.00006191035,0.008668953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99428874,0.0024878173,0.0005983726,0.0015651789,0.0005313377,0.00052854297],"domain_scores_gemma":[0.9894912,0.002551597,0.0005931355,0.0028941627,0.004251223,0.00021867645],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036112226,0.00044904966,0.00048612774,0.00029074622,0.0006148865,0.0005601752,0.002516665,0.00037976325,0.000045184966],"category_scores_gemma":[0.00089716323,0.0004971532,0.00035093396,0.0006183412,0.0002174673,0.00044831072,0.0021738664,0.000627052,0.00007125718],"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":[0.000054848337,0.0023534994,0.0004287937,0.00036554388,0.00043650644,0.0000043805803,0.017367383,0.22243942,0.00060038705,0.67407554,0.0059452816,0.07592843],"study_design_scores_gemma":[0.00070506876,0.0000015005331,0.00074616785,0.00052133575,0.00003410754,0.0000040367113,0.00009096533,0.9835978,0.0041434043,0.0064598755,0.003174661,0.0005210457],"about_ca_topic_score_codex":0.00053043064,"about_ca_topic_score_gemma":0.000150029,"teacher_disagreement_score":0.7611584,"about_ca_system_score_codex":0.00032373064,"about_ca_system_score_gemma":0.00066673715,"threshold_uncertainty_score":0.999748},"labels":[],"label_agreement":null},{"id":"W3004224429","doi":"10.1145/3381343.3381345","title":"Big data driven genetic improvement for maintenance of legacy software systems","year":2020,"lang":"en","type":"article","venue":"ACM SIGEVOlution","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Software; Conversation; Artificial intelligence; Computer science; Library science; Sociology; Operating system","score_opus":0.06133430161237951,"score_gpt":0.263458571486809,"score_spread":0.2021242698744295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004224429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049837986,0.0006724445,0.99071395,0.0024296073,0.0003342971,0.0006073048,0.00012981931,0.000113592294,0.000015167777],"genre_scores_gemma":[0.74214154,0.000044109736,0.2570434,0.00016587244,0.0002980947,0.0001398873,0.00009814293,0.000010193413,0.00005874618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998776,0.00002018528,0.0003083443,0.0004800351,0.00019857952,0.00021681127],"domain_scores_gemma":[0.9981141,0.000095799725,0.00017499363,0.0013694367,0.0001731217,0.00007251642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011596548,0.00010493899,0.00014584254,0.00003333226,0.00012774499,0.00005031985,0.0019568356,0.00004537983,0.0000013543665],"category_scores_gemma":[0.0002467863,0.00010193024,0.000044892,0.00030164325,0.00004202375,0.00044833895,0.0007955964,0.000059663154,0.000015597925],"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":[0.000071325965,0.0007659588,0.0042626145,0.0010956046,0.0002624586,0.00000794489,0.0013416271,0.04700141,0.05479728,0.18072356,0.111912146,0.59775805],"study_design_scores_gemma":[0.0004939345,0.00024897532,0.007139458,0.000033588607,0.000019020717,0.0000035031376,0.00006296474,0.9544515,0.00031323853,0.0024292287,0.03461402,0.0001905691],"about_ca_topic_score_codex":0.000076806,"about_ca_topic_score_gemma":0.0000031882578,"teacher_disagreement_score":0.9074501,"about_ca_system_score_codex":0.00005254613,"about_ca_system_score_gemma":0.000118017204,"threshold_uncertainty_score":0.4156595},"labels":[],"label_agreement":null},{"id":"W3007116216","doi":"10.1145/3365953.3365955","title":"<i>BENIN</i>","year":2019,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inference; Computer science; Benchmark (surveying); Resampling; Machine learning; Artificial intelligence; Feature selection; Complementarity (molecular biology); Data mining; Gene regulatory network; Gene expression; Gene; Biology","score_opus":0.006178601518985226,"score_gpt":0.21177739072335558,"score_spread":0.20559878920437036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007116216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065716533,0.000021510077,0.8129796,0.002190745,0.00009397726,0.0000607194,2.2690857e-7,0.00014500135,0.1779366],"genre_scores_gemma":[0.7266608,0.0000034460766,0.24959525,0.0009019972,0.00003177182,0.000009919891,9.5026877e-7,0.0000020225018,0.022793856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997366,0.000002905853,0.000038230293,0.00010422103,0.000055982524,0.00006207978],"domain_scores_gemma":[0.99969596,0.000012173431,0.0000078535295,0.0002485516,0.000013861411,0.000021574926],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002824109,0.00002296111,0.000023488214,0.0000112939,0.000024499044,0.000020615891,0.0002625101,0.000009239197,0.0001443381],"category_scores_gemma":[6.306787e-7,0.000018917084,0.000014228994,0.000114793605,0.000003942544,0.00016445892,0.00006531829,0.000021270875,0.00247247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.8211135e-8,0.000011780179,0.00051586167,4.25318e-7,7.7609684e-7,1.268606e-7,0.000011154565,0.00002333181,0.0005122728,0.9890301,0.004744994,0.0051491233],"study_design_scores_gemma":[0.0002780802,0.000048730464,0.022475237,0.000003029163,9.332442e-7,0.000019122022,0.000017724818,0.31823134,0.0022640855,0.05808345,0.5983626,0.00021565324],"about_ca_topic_score_codex":0.0000047157173,"about_ca_topic_score_gemma":3.1676873e-7,"teacher_disagreement_score":0.93094665,"about_ca_system_score_codex":0.0000042731695,"about_ca_system_score_gemma":0.000011187555,"threshold_uncertainty_score":0.99830425},"labels":[],"label_agreement":null},{"id":"W3021883800","doi":"10.1007/978-3-030-39958-0_17","title":"Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model","year":2020,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Probabilistic logic; Observability; Recall; Encoding (memory); Markov decision process; Genetic programming; HERO; Machine learning; Theoretical computer science; Markov process","score_opus":0.018144377941250137,"score_gpt":0.212850511694543,"score_spread":0.19470613375329288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021883800","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000099293015,0.0031479371,0.96109813,0.0012776706,0.00011402633,0.00079632335,0.0000669596,0.00030940224,0.03309024],"genre_scores_gemma":[0.05580782,0.0002403643,0.9075372,0.000525541,0.00055904774,0.00020487444,0.0004334883,0.000103862294,0.034587797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978025,0.000025830344,0.00042718858,0.0009850016,0.00049017544,0.00026931154],"domain_scores_gemma":[0.9986144,0.000085391235,0.00025450392,0.00040756806,0.0004161048,0.00022205268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007158755,0.00041082955,0.00033831067,0.00014101503,0.00039990884,0.000136437,0.00042370707,0.00018964037,0.000028466524],"category_scores_gemma":[0.000017122551,0.0004055791,0.00008602385,0.00013692367,0.00017113423,0.00031599894,0.00031481092,0.0002997858,0.00007560994],"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":[0.000047540376,0.00014955037,0.00003249527,0.00029131945,0.00023386432,0.000057221525,0.0009773824,0.6493883,0.00003276392,0.29139733,0.027645448,0.029746734],"study_design_scores_gemma":[0.00031172906,0.0001424749,0.0014099495,0.000096699434,0.000055468445,0.00008676788,0.0000075012495,0.81633854,2.8229505e-7,0.17928553,0.0018515963,0.00041347367],"about_ca_topic_score_codex":0.000010298206,"about_ca_topic_score_gemma":0.0000043290656,"teacher_disagreement_score":0.1669502,"about_ca_system_score_codex":0.00016170324,"about_ca_system_score_gemma":0.00045341483,"threshold_uncertainty_score":0.9998396},"labels":[],"label_agreement":null},{"id":"W3023259985","doi":"10.1007/978-3-030-39958-0_4","title":"Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?","year":2020,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Queen's University","funders":"","keywords":"Genetic programming; Computer science; Artificial intelligence; Machine learning","score_opus":0.014642432566419242,"score_gpt":0.21641990840556938,"score_spread":0.20177747583915012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023259985","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010132708,0.003709772,0.9881429,0.0016107268,0.00016432792,0.001056104,0.000043842916,0.00025301622,0.0049179476],"genre_scores_gemma":[0.016952707,0.0006375014,0.9648375,0.00019822226,0.000401612,0.00022470202,0.0007862779,0.000057842983,0.015903654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827385,0.000016531398,0.00051811035,0.0005646602,0.0003089361,0.00031792224],"domain_scores_gemma":[0.99895656,0.00010269826,0.0003089478,0.000224835,0.0002178065,0.00018912958],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009271463,0.0003463295,0.00029464922,0.00017088778,0.0007676611,0.0001378767,0.00032789417,0.00018438368,0.000015879687],"category_scores_gemma":[0.000014895198,0.00037598025,0.000120350516,0.00010688376,0.00009708587,0.0001924076,0.00024507925,0.0002734632,0.00003783003],"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":[0.000021067986,0.00006081352,0.00019971946,0.0004933114,0.00016592989,0.000013127769,0.0010240773,0.061290175,0.000008334251,0.15666488,0.0037380967,0.77632046],"study_design_scores_gemma":[0.0003309714,0.00028136687,0.0012607864,0.000044888857,0.00004639723,0.00009170317,0.000027060916,0.8510703,9.541642e-7,0.067258224,0.07920465,0.00038273534],"about_ca_topic_score_codex":0.000018433055,"about_ca_topic_score_gemma":0.0000035971914,"teacher_disagreement_score":0.7897801,"about_ca_system_score_codex":0.00013094985,"about_ca_system_score_gemma":0.00021294181,"threshold_uncertainty_score":0.9998692},"labels":[],"label_agreement":null},{"id":"W3046276070","doi":"10.1145/3377929.3390007","title":"DarwiNN","year":2020,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Toolbox; Leverage (statistics); Cloud computing; Inference; Kernel (algebra); Neuroevolution; Deep neural networks; Artificial intelligence; Computation; Deep learning; Variety (cybernetics); Artificial neural network; Distributed computing; Machine learning; Theoretical computer science; Parallel computing; Algorithm; Programming language; Operating system","score_opus":0.022252824562664426,"score_gpt":0.23375072193385518,"score_spread":0.21149789737119076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046276070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001591649,0.000015129922,0.92134356,0.040575724,0.000015148029,0.000021828999,2.619124e-7,0.00015401159,0.03771517],"genre_scores_gemma":[0.5137614,0.00000308334,0.47843856,0.0070974124,0.000101600635,0.000007957244,9.45767e-7,0.0000016996976,0.0005873762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997755,0.0000027329836,0.000035379337,0.00009469208,0.000046017452,0.000045662913],"domain_scores_gemma":[0.999823,0.0000068092872,0.00000575498,0.000104004284,0.0000109174,0.000049533508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000012427893,0.000019348006,0.000019410758,0.0000042083825,0.000036562316,0.000019587009,0.0002581228,0.0000062921154,0.000062258994],"category_scores_gemma":[0.0000031254115,0.000016399825,0.000011116948,0.00015261066,0.000005570481,0.000119390825,0.0000724686,0.000020338255,0.00043691386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.94382e-8,0.0000075833705,0.000043487187,4.8314365e-7,9.2126174e-7,6.1936294e-7,0.00006923376,0.000021896862,0.00032290738,0.9638428,0.023630554,0.01205943],"study_design_scores_gemma":[0.000100886056,0.000030785803,0.003146897,5.4852535e-7,6.8573206e-7,0.000003875494,0.00001627832,0.56874055,0.0009831227,0.013416797,0.41346407,0.000095504656],"about_ca_topic_score_codex":0.0000017653831,"about_ca_topic_score_gemma":8.716235e-8,"teacher_disagreement_score":0.95042604,"about_ca_system_score_codex":0.0000019450742,"about_ca_system_score_gemma":0.000010712064,"threshold_uncertainty_score":0.5615789},"labels":[],"label_agreement":null},{"id":"W3068884712","doi":"10.48550/arxiv.2008.09017","title":"A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Machine learning; Algorithm; Computer science; Artificial intelligence; Field (mathematics); Genetic algorithm; Support vector machine; Variety (cybernetics); Population; Data mining; Medicine; Mathematics","score_opus":0.04775831902471871,"score_gpt":0.1959859082601826,"score_spread":0.14822758923546392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3068884712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13265936,0.00036620532,0.8648698,0.00040027426,0.0002428855,0.00046044326,0.00037029508,0.000047990583,0.00058274885],"genre_scores_gemma":[0.94972515,0.00044973617,0.04949122,0.00003647496,0.000028109163,0.0000017136732,0.000008666686,0.000006409822,0.00025251726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988977,0.00007382966,0.00033365627,0.00042099154,0.00012969576,0.000144159],"domain_scores_gemma":[0.9982387,0.00008351083,0.00039774575,0.0010973518,0.000117084055,0.000065608474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010329497,0.0001566315,0.00023941006,0.00009979836,0.000043709886,0.00001323496,0.002353503,0.0001351443,0.000011325133],"category_scores_gemma":[0.00001974673,0.00014864946,0.0001481566,0.00072179304,0.0001582074,0.00016663816,0.0022749219,0.00031891148,0.00000829419],"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":[0.000043853855,0.0010067776,0.1290742,0.0029067814,0.00029681332,0.00008400849,0.0047669034,0.7093021,0.00037098842,0.1411836,0.0027090148,0.008254891],"study_design_scores_gemma":[0.00019758084,0.000021242395,0.098264545,0.00014638047,0.000038615704,7.183069e-7,0.000039563496,0.86691886,0.00013176003,0.033987343,0.00010654748,0.00014683374],"about_ca_topic_score_codex":0.00042395826,"about_ca_topic_score_gemma":0.00002025358,"teacher_disagreement_score":0.8170658,"about_ca_system_score_codex":0.00007664067,"about_ca_system_score_gemma":0.00033029972,"threshold_uncertainty_score":0.60617495},"labels":[],"label_agreement":null},{"id":"W3078478003","doi":"10.1109/tcds.2020.3017100","title":"Accurate and Fast Deep Evolutionary Networks Structured Representation Through Activating and Freezing Dense Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Variety (cybernetics); Convergence (economics); Construct (python library); Artificial neural network; Training (meteorology); Artificial intelligence; Deep learning; Representation (politics); Point (geometry); Evolutionary algorithm; Computer network","score_opus":0.027304680677245216,"score_gpt":0.2512561656906926,"score_spread":0.22395148501344736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3078478003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03756777,0.0008229167,0.9604358,0.00024932524,0.00019237545,0.00044608957,0.000017979832,0.00010443541,0.00016333163],"genre_scores_gemma":[0.98768604,0.00040384504,0.011370493,0.00028296688,0.00009470264,0.00011006052,0.000014402459,0.000012785245,0.000024691033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868834,0.00008865261,0.00028673143,0.0005441141,0.00016986861,0.00022228801],"domain_scores_gemma":[0.9992867,0.00028711002,0.00010508727,0.00007689798,0.000091574235,0.00015260486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000072935094,0.00019990091,0.00018958533,0.000048081914,0.0007840804,0.00019449768,0.000096574695,0.00009359583,0.0000046529526],"category_scores_gemma":[0.000008621732,0.00019941322,0.000027448474,0.00038238565,0.00010124335,0.0007841171,0.000016861703,0.00022905032,0.0000029952757],"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":[0.0005090045,0.0004061389,0.0050686905,0.00035851786,0.0011031333,0.00011200379,0.02878582,0.21916564,0.0054299603,0.006553678,0.0006609579,0.73184645],"study_design_scores_gemma":[0.00080210227,0.00008506449,0.006391659,0.00013698798,0.000023353386,0.00021595035,0.0035595796,0.98759174,0.0005992979,0.00015394505,0.000107844746,0.0003324857],"about_ca_topic_score_codex":0.00008996112,"about_ca_topic_score_gemma":0.00001591783,"teacher_disagreement_score":0.9501183,"about_ca_system_score_codex":0.000043444772,"about_ca_system_score_gemma":0.000034819186,"threshold_uncertainty_score":0.8131835},"labels":[],"label_agreement":null},{"id":"W3097916346","doi":"10.1109/icpr48806.2021.9412137","title":"Classifier Pool Generation based on a Two-level Diversity Approach","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Classifier (UML); Computer science; Machine learning; Artificial intelligence; Data mining; Pattern recognition (psychology)","score_opus":0.09982846445948788,"score_gpt":0.26875265787152736,"score_spread":0.16892419341203946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097916346","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020197,0.000022203216,0.9804232,0.0016588846,0.00034007934,0.00029706713,0.000026939964,0.00016969781,0.015042236],"genre_scores_gemma":[0.422305,0.000004791821,0.57467186,0.000905767,0.00023192448,0.00009371044,0.00032249463,0.0000065223,0.0014579409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982624,0.000086137254,0.00018308716,0.0008626537,0.0004206626,0.00018506785],"domain_scores_gemma":[0.9984728,0.000035727815,0.0000955765,0.00112798,0.00017094762,0.00009694018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019716781,0.00020039962,0.00017402151,0.00010018507,0.00045073623,0.00026630505,0.0009580236,0.00016657871,0.000067708475],"category_scores_gemma":[0.000010292262,0.00019372316,0.00014627023,0.00024077459,0.000030581716,0.00016672935,0.002527359,0.00039008143,0.000032093965],"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":[0.000006803653,0.0022874016,0.00092462363,0.00009369848,0.0001181129,0.000024343486,0.0007208072,0.5868046,0.00070655707,0.35138234,0.024953289,0.03197742],"study_design_scores_gemma":[0.00020096438,0.000012604499,0.0041712066,0.000010216527,0.000009904622,0.0000021753388,0.000016881502,0.99398285,0.00021499986,0.00087124074,0.0002712412,0.0002357426],"about_ca_topic_score_codex":0.00018356436,"about_ca_topic_score_gemma":0.000018944274,"teacher_disagreement_score":0.42028528,"about_ca_system_score_codex":0.0001353134,"about_ca_system_score_gemma":0.00029307214,"threshold_uncertainty_score":0.7899801},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005713755,0.00009580299,0.9831202,0.0092278365,0.00023085851,0.00027638685,0.000011666806,0.0004304014,0.00089306704],"genre_scores_gemma":[0.66188323,0.000023466007,0.33720708,0.00031158008,0.00039323492,0.00004307296,0.00011719046,0.0000078832145,0.000013290908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985526,0.00007101075,0.00026329648,0.00055465783,0.00035522503,0.00020319376],"domain_scores_gemma":[0.9991557,0.000057070567,0.00014854256,0.00013694668,0.0003356218,0.00016614306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000080170415,0.00016578044,0.00014044222,0.000060299106,0.00045515335,0.00012401259,0.00021650268,0.00008760844,0.0000062095514],"category_scores_gemma":[0.000022784809,0.00016801518,0.00003296317,0.001217899,0.00007999045,0.000831696,0.000077860925,0.000102817125,0.000025181087],"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":[0.000059910868,0.00030542634,0.005001078,0.000043994154,0.000064903616,0.0000026769244,0.00029108272,0.85572445,0.0002746127,0.10813243,0.011436337,0.018663088],"study_design_scores_gemma":[0.00045158868,0.00016991101,0.10550031,0.00001068307,0.000016391761,0.00003380909,0.000038913404,0.8894346,0.0000109782,0.003035637,0.0011030439,0.00019415472],"about_ca_topic_score_codex":0.000023556393,"about_ca_topic_score_gemma":0.0000048386046,"teacher_disagreement_score":0.6561695,"about_ca_system_score_codex":0.00013380082,"about_ca_system_score_gemma":0.0001694538,"threshold_uncertainty_score":0.68514603},"labels":[],"label_agreement":null},{"id":"W3105201135","doi":"","title":"Elaboration on Two Points Raised in “Classifier Technology and the Illusion of Progress”","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Elaboration; Illusion; Classifier (UML); Artificial intelligence; Computer science; Cognitive psychology; Machine learning; Psychology; Art; Humanities","score_opus":0.011590967297132979,"score_gpt":0.24818625818777018,"score_spread":0.2365952908906372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105201135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6801037,0.0002903339,0.2477905,0.06359048,0.00007113528,0.0006776809,0.0000016081344,0.00013695592,0.007337559],"genre_scores_gemma":[0.96181244,0.00003331737,0.03785438,0.00011201887,0.000008365463,0.00004026306,6.8723807e-7,0.0000015081879,0.00013700209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995618,0.000025797885,0.00011998047,0.00013464462,0.000090476795,0.00006729436],"domain_scores_gemma":[0.9996331,0.000044213695,0.000044052147,0.00021814158,0.000048888203,0.00001163724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012831354,0.00004113161,0.000065461034,0.00012060907,0.00009367584,0.0000061596884,0.00018414995,0.000029532477,0.000004736227],"category_scores_gemma":[0.000015133231,0.000025312522,0.000009714032,0.00068328285,0.0002537035,0.000106428604,0.0000848873,0.00006047493,0.000005764902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075446087,0.00006699266,0.0017857567,0.0000010231015,0.0000014854617,0.0000011131781,0.00017225055,0.000016524951,0.00027538248,0.98575366,0.00013119497,0.011787104],"study_design_scores_gemma":[0.006619864,0.0002550845,0.19653085,0.000044821187,0.0000036428896,0.0000778946,0.00016532483,0.5923501,0.0063312184,0.19144538,0.0059098033,0.00026598174],"about_ca_topic_score_codex":0.000007071451,"about_ca_topic_score_gemma":0.000004723932,"teacher_disagreement_score":0.79430825,"about_ca_system_score_codex":0.00000902343,"about_ca_system_score_gemma":0.00002726649,"threshold_uncertainty_score":0.10322147},"labels":[],"label_agreement":null},{"id":"W3107241399","doi":"10.1109/ccece47787.2020.9255685","title":"Hybridizing UFO with Other ML Tools to Locate Faults by Just Knowing Relay Operating Times","year":2020,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Support vector machine; Artificial neural network; Margin (machine learning); Relay; Transformation (genetics); Nonlinear system; Artificial intelligence; Overcurrent; Algorithm; Power (physics); Machine learning; Computer engineering","score_opus":0.034963676424808875,"score_gpt":0.252868454785901,"score_spread":0.2179047783610921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107241399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008557386,0.00007616598,0.9610377,0.019012392,0.000027485323,0.00023319545,0.0000065804247,0.00027853396,0.010770554],"genre_scores_gemma":[0.40135974,0.0000032818753,0.58681357,0.007883557,0.00014798564,0.00006718162,0.0000066290318,0.000021751908,0.00369632],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989566,0.000020759397,0.00017552741,0.00043722705,0.00017951636,0.00023036743],"domain_scores_gemma":[0.9994061,0.000054325556,0.000034323162,0.0002763339,0.000059914182,0.00016897713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098753204,0.0001239997,0.00011302752,0.000022712686,0.000260822,0.00019580933,0.0005005434,0.000023216633,0.000053291104],"category_scores_gemma":[0.000021924518,0.000098327924,0.000025810581,0.0004521522,0.000017835822,0.0005974575,0.00019562112,0.00010247117,0.0003374002],"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":[0.00002554441,0.0004038422,0.0015918396,0.00008353754,0.00018365897,0.000046516314,0.011884097,0.0960069,0.124156974,0.27797773,0.295112,0.19252734],"study_design_scores_gemma":[0.00048374568,0.0002545466,0.00035725508,0.000073498566,0.0000108262375,0.000025288717,0.00051130605,0.7532102,0.024294365,0.000092788425,0.22005957,0.00062659854],"about_ca_topic_score_codex":0.00005774197,"about_ca_topic_score_gemma":0.0000042525166,"teacher_disagreement_score":0.6572033,"about_ca_system_score_codex":0.000024386423,"about_ca_system_score_gemma":0.000049075054,"threshold_uncertainty_score":0.43367097},"labels":[],"label_agreement":null},{"id":"W3111380077","doi":"10.1109/cibcb48159.2020.9277714","title":"Effective Side Effect Machines for Decoding","year":2020,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"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":"Decoding methods; Metric (unit); Computer science; Code (set theory); Edit distance; Algorithm; Extension (predicate logic)","score_opus":0.011774200144269744,"score_gpt":0.2671198864667722,"score_spread":0.2553456863225025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111380077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036067078,0.000042606564,0.9862783,0.005457366,0.000053103144,0.00045697312,0.0000023204402,0.00019733034,0.0039053157],"genre_scores_gemma":[0.657282,0.000001552812,0.34067345,0.0013294842,0.00020895127,0.0003716263,0.0000037295595,0.0000061614755,0.00012304535],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995382,0.00001918612,0.00006869045,0.00021180326,0.000056277495,0.00010584381],"domain_scores_gemma":[0.99938846,0.00038134077,0.000019327801,0.00011944886,0.000024951361,0.00006644619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008484881,0.000062557796,0.000075924036,0.000014167153,0.00012384898,0.000040513387,0.00026766062,0.000017020755,0.000005563029],"category_scores_gemma":[0.000055511046,0.000048954676,0.000054126012,0.00016594984,0.000009365558,0.00016292791,0.00008380687,0.00003625129,0.000050092836],"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":[0.000009718967,0.000030956253,0.0006264463,0.000041014162,0.000024297673,0.0000016121397,0.00029608418,0.00027220236,0.0074102567,0.63234985,0.0062764967,0.35266107],"study_design_scores_gemma":[0.00031949065,0.00021373488,0.0019334061,0.0000031905768,0.0000045406496,0.0000031727877,0.000003079947,0.97542226,0.0060027204,0.008150851,0.007840443,0.00010312714],"about_ca_topic_score_codex":0.0000064101914,"about_ca_topic_score_gemma":0.0000011508492,"teacher_disagreement_score":0.97515005,"about_ca_system_score_codex":0.000010167647,"about_ca_system_score_gemma":0.000009072859,"threshold_uncertainty_score":0.19963138},"labels":[],"label_agreement":null},{"id":"W3117642670","doi":"10.1109/iemcon51383.2020.9284951","title":"ECG Knowledge Discovery Based on Ontologies and Rules Learning for the Support of Personalized Medical Decision Making","year":2020,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Knowledge extraction; Knowledge base; Decision support system; Classifier (UML); Process (computing); Knowledge representation and reasoning; Artificial intelligence; Machine learning; Knowledge-based systems; Data mining; Data science","score_opus":0.03388454777414192,"score_gpt":0.313301576427752,"score_spread":0.2794170286536101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117642670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025315287,0.00015842693,0.9856674,0.010614228,0.00003070749,0.00012707782,0.0000025186725,0.000046159028,0.00082196173],"genre_scores_gemma":[0.83771783,0.000032183336,0.16121143,0.00081750104,0.0000420613,0.000035129768,0.0000026867183,0.0000035702424,0.00013758922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993871,0.000024337201,0.00012566379,0.0001897858,0.00018169697,0.00009141521],"domain_scores_gemma":[0.9981243,0.00163988,0.000041730782,0.0001182971,0.000036797854,0.00003900399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022256616,0.000058815145,0.00009320742,0.00001912297,0.00016904096,0.00004485417,0.00034102148,0.000031724943,0.000036370653],"category_scores_gemma":[0.00031267622,0.000035192894,0.000048802136,0.00009673854,0.000087024004,0.00012483988,0.0001328972,0.00007616303,0.0000042346323],"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":[0.00010890289,0.00018949367,0.0025908786,0.000063270934,0.00003052359,0.0000027521824,0.0014690595,0.0021186462,0.00014311551,0.41028434,0.009992007,0.573007],"study_design_scores_gemma":[0.0002806236,0.00012813846,0.0037088115,0.000020291396,0.000004344607,0.0000020118243,0.00011857108,0.98204935,0.000050018858,0.0014261845,0.0121599445,0.000051711046],"about_ca_topic_score_codex":0.0000028583904,"about_ca_topic_score_gemma":0.0000052976193,"teacher_disagreement_score":0.9799307,"about_ca_system_score_codex":0.0000073282413,"about_ca_system_score_gemma":0.00008925162,"threshold_uncertainty_score":0.14351246},"labels":[],"label_agreement":null},{"id":"W3120403074","doi":"10.1109/tse.2021.3101818","title":"Combining Genetic Programming and Model Checking to Generate Environment Assumptions","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Software Engineering","topic":"Evolutionary Algorithms and Applications","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":"University of Ottawa","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; H2020 Excellent Science; Fonds National de la Recherche Luxembourg","keywords":"Component (thermodynamics); Computer science; Spurious relationship; Soundness; Flexibility (engineering); Benchmark (surveying); Genetic programming; Software; State (computer science); Artificial intelligence; Machine learning; Algorithm; Programming language; Mathematics","score_opus":0.01687721823519412,"score_gpt":0.22193729267168105,"score_spread":0.20506007443648694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120403074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037161645,0.00034178642,0.96092534,0.00014384814,0.00043171985,0.00042430693,0.000026373697,0.0005425692,0.0000023833718],"genre_scores_gemma":[0.34159747,0.00013816833,0.6575085,0.000046302306,0.000040879564,0.0005659912,0.0000075146886,0.000034540608,0.000060616676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981495,0.000020608695,0.00034018402,0.00083345553,0.00028813886,0.00036811712],"domain_scores_gemma":[0.9988366,0.00006578551,0.00006491358,0.0007455483,0.000051453426,0.00023574856],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011217147,0.00034999102,0.00027333017,0.00023857094,0.00032219698,0.00033770094,0.0004332411,0.00018158797,0.0000068343575],"category_scores_gemma":[0.0000054393126,0.0004353379,0.00012897074,0.00023938976,0.000019930045,0.0001752437,0.00005714732,0.000663642,0.000011512099],"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":[5.2101757e-7,0.000079640056,0.000008014719,0.000049557242,0.00004515283,0.000006306982,0.00048195728,0.97264224,0.00046320356,0.000099015655,0.000005028653,0.026119381],"study_design_scores_gemma":[0.000115189505,0.000024922263,0.00037024962,0.00017213108,0.000035845664,0.000023135606,0.000016450083,0.9972289,0.001308784,0.000043278153,0.0002162253,0.00044486538],"about_ca_topic_score_codex":0.00001376333,"about_ca_topic_score_gemma":0.0000022993056,"teacher_disagreement_score":0.30443582,"about_ca_system_score_codex":0.00018606456,"about_ca_system_score_gemma":0.00009169854,"threshold_uncertainty_score":0.99980986},"labels":[],"label_agreement":null},{"id":"W3126101431","doi":"","title":"A Fast Fractional Difference Algorithm","year":2013,"lang":"en","type":"preprint","venue":"Research at the University of Copenhagen (University of Copenhagen)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computation; Algorithm; Series (stratigraphy); Order (exchange); Mathematics; Sample (material); Implementation; Arithmetic; Computer science; Applied mathematics; Finance","score_opus":0.041749519855475294,"score_gpt":0.27284255009301783,"score_spread":0.23109303023754255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126101431","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02831446,0.0047474774,0.85904586,0.0047365017,0.0002392759,0.00208144,0.00049786095,0.00014695618,0.10019015],"genre_scores_gemma":[0.18932347,0.0009945884,0.0641521,0.00003348135,0.00007365761,0.0000018217406,0.00017752835,0.000025205547,0.74521816],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957954,0.00077307323,0.00026809276,0.0009856347,0.0015340723,0.00064367545],"domain_scores_gemma":[0.9952417,0.0006459878,0.00049750117,0.0018798532,0.0013905378,0.0003443975],"candidate_categories":["metaepi_narrow","sts","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.0011463938,0.0003293589,0.00059224194,0.0003242409,0.0019744695,0.0000999033,0.006124697,0.00035911342,0.14859863],"category_scores_gemma":[0.000041302086,0.0003645169,0.0003897692,0.0010133832,0.0013352956,0.00067241065,0.009085368,0.0012528761,0.05454019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051727166,0.00029919168,0.0000021333053,0.00010795497,0.0002437626,0.000061383784,0.0021003739,0.00066075136,0.0008708023,0.0033196143,0.96566784,0.026614461],"study_design_scores_gemma":[0.001202593,0.00025333362,0.021920701,0.00024947443,0.00010764206,0.000027737631,0.0042132307,0.12195173,0.00059297465,0.0011259693,0.84765166,0.0007029315],"about_ca_topic_score_codex":0.0034306408,"about_ca_topic_score_gemma":0.00025777737,"teacher_disagreement_score":0.7948938,"about_ca_system_score_codex":0.00052408077,"about_ca_system_score_gemma":0.0011395132,"threshold_uncertainty_score":0.9998807},"labels":[],"label_agreement":null},{"id":"W3128274009","doi":"10.1002/eng2.12369","title":"An investigation of the effects of chaotic maps on the performance of metaheuristics","year":2021,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Metaheuristic; Chaotic; Simulated annealing; Particle swarm optimization; Computer science; Benchmark (surveying); MATLAB; Swarm behaviour; Algorithm; Statistical hypothesis testing; Mathematical optimization; Mathematics; Artificial intelligence; Statistics","score_opus":0.005006273311699667,"score_gpt":0.18356297519858625,"score_spread":0.17855670188688658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128274009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97820574,0.00008734663,0.021245757,0.00013521686,0.00020964677,0.0000815113,6.9744215e-7,0.000014221894,0.000019833757],"genre_scores_gemma":[0.99262875,0.0000073176157,0.007299369,0.000010482644,0.000015810136,0.0000089403675,0.0000013020361,0.0000030641588,0.000024936024],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995007,0.000012361248,0.00018708363,0.00009267306,0.00015616046,0.00005102647],"domain_scores_gemma":[0.99909896,0.000119057055,0.00013800325,0.0005514147,0.0000760793,0.000016500082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015637897,0.000043903518,0.0000741632,0.00001867707,0.00002782338,0.000004211136,0.0001567181,0.00001597021,7.221303e-7],"category_scores_gemma":[0.0001092661,0.000028666658,0.000031111616,0.00027883623,0.000025639329,0.00005379832,0.000043167413,0.000054751057,1.6228529e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.786596e-7,0.00019079784,0.019297985,0.00079898315,0.00007015256,0.000026651653,0.0013464862,0.26592174,0.5534707,0.15699568,0.00025510136,0.0016247996],"study_design_scores_gemma":[0.000025822394,0.000047724257,0.15830387,0.00011562621,0.000010337186,0.00003606346,0.0000039706556,0.20262912,0.63802475,0.00069755444,0.000059470567,0.00004570279],"about_ca_topic_score_codex":0.000002809091,"about_ca_topic_score_gemma":1.2199732e-7,"teacher_disagreement_score":0.15629813,"about_ca_system_score_codex":0.000007128536,"about_ca_system_score_gemma":0.000044320124,"threshold_uncertainty_score":0.116899244},"labels":[],"label_agreement":null},{"id":"W3143185403","doi":"10.1109/cefc-06.2006.1633087","title":"Evolution of Wire Antennas in Three Dimensions using a Novel Growth Process","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Process (computing); Computer science; Antenna (radio); Implementation; Directional antenna; Reconfigurable antenna; Electronic engineering; Space (punctuation); Omnidirectional antenna; Engineering; Telecommunications; Antenna efficiency; Software engineering","score_opus":0.017955038621686736,"score_gpt":0.24947027105430483,"score_spread":0.2315152324326181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143185403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27943093,0.000071261056,0.7193671,0.00023497945,0.00002355327,0.000100910875,0.0000021310946,0.00004106555,0.0007281074],"genre_scores_gemma":[0.86637086,9.3219535e-7,0.13354526,0.000014027238,0.00002238274,0.000010487776,0.0000012559273,0.0000030799424,0.00003170857],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992823,0.0000062350873,0.0002054919,0.0002100909,0.00014970443,0.00014619155],"domain_scores_gemma":[0.99954957,0.000021130829,0.00006395169,0.00020650252,0.00013548038,0.000023371218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000871454,0.0000683276,0.00008867993,0.00011357828,0.000082157836,0.000012677015,0.00026253518,0.000034063727,0.0000035103233],"category_scores_gemma":[0.0000073877322,0.000061632534,0.00002808005,0.0008364187,0.000043699885,0.00034513886,0.00007778842,0.00005338958,0.0000030330013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010764743,0.0003164977,0.020764649,0.0000094799125,0.0000018360181,6.697066e-7,0.000036316767,0.0036885059,0.022402762,0.9526749,0.000019478366,0.00008382978],"study_design_scores_gemma":[0.00019530497,0.000013168201,0.12387173,0.000022863856,0.0000021187852,0.000012829675,0.000023201068,0.7699816,0.0007080886,0.105074115,0.000009232186,0.00008571388],"about_ca_topic_score_codex":0.0016119159,"about_ca_topic_score_gemma":0.00027098556,"teacher_disagreement_score":0.84760076,"about_ca_system_score_codex":0.000060995506,"about_ca_system_score_gemma":0.00009731685,"threshold_uncertainty_score":0.2513302},"labels":[],"label_agreement":null},{"id":"W3175526646","doi":"10.48550/arxiv.2106.14131","title":"SymbolicGPT: A Generative Transformer Model for Symbolic Regression","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Symbolic regression; Computer science; Probabilistic logic; Transformer; Artificial intelligence; Machine learning; Regression; Generative grammar; Exploit; Generative model; Regression analysis; Genetic programming; Mathematics; Statistics; Engineering","score_opus":0.08461084795732692,"score_gpt":0.21562371089393645,"score_spread":0.13101286293660952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175526646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04204868,0.0001819596,0.9554307,0.0005431498,0.00020194026,0.00055897253,0.000069403344,0.00017090191,0.0007942843],"genre_scores_gemma":[0.9354528,0.00029831944,0.060420427,0.00016483641,0.000110218876,0.000027846492,0.00010713534,0.00002045933,0.003397934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981214,0.000056621448,0.00020651723,0.0011771863,0.00009207686,0.00034621157],"domain_scores_gemma":[0.99829745,0.00006172435,0.00015188575,0.0010172579,0.0003017646,0.00016991925],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012917852,0.00031138572,0.0003334736,0.00015205888,0.00036931434,0.00012768399,0.0012270882,0.000289055,0.000010922044],"category_scores_gemma":[0.0000098012115,0.00033039597,0.00033626013,0.00045155175,0.00007652275,0.0004234111,0.0004858556,0.00037066638,0.000011561435],"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":[0.000009080615,0.0001582431,0.000032482993,0.00006267778,0.00006376551,0.000020023355,0.0009610629,0.52866757,0.0003487742,0.46823794,0.0005065442,0.0009318247],"study_design_scores_gemma":[0.00034371906,0.000019017829,0.00008862309,0.00006403915,0.000052342417,0.000004192751,0.00008359096,0.925103,0.0004249867,0.07322198,0.00023885487,0.0003556478],"about_ca_topic_score_codex":0.000034595654,"about_ca_topic_score_gemma":0.000027823946,"teacher_disagreement_score":0.8950103,"about_ca_system_score_codex":0.00016287089,"about_ca_system_score_gemma":0.0004806918,"threshold_uncertainty_score":0.9999148},"labels":[],"label_agreement":null},{"id":"W3177052905","doi":"10.1007/s10710-021-09418-4","title":"Evolving hierarchical memory-prediction machines in multi-task reinforcement learning","year":2021,"lang":"en","type":"preprint","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"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":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Reinforcement learning; Computer science; Task (project management); Variety (cybernetics); Encoding (memory); Artificial intelligence; Action (physics); ENCODE; Identification (biology); Salient; Machine learning; Decomposition; Engineering","score_opus":0.015449851232142396,"score_gpt":0.2573528990363186,"score_spread":0.24190304780417618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177052905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08806127,0.012057834,0.8970006,0.0007345426,0.0006454467,0.000797232,0.0000064424553,0.0004603449,0.00023627939],"genre_scores_gemma":[0.5288731,0.00063793093,0.4690252,0.000049888287,0.00019458105,0.0004773755,0.00016282717,0.000030209858,0.00054892094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968749,0.00019371785,0.0006911196,0.0012060711,0.0004215207,0.0006126405],"domain_scores_gemma":[0.9985392,0.00009825144,0.0002296032,0.0007700265,0.00015949106,0.00020343403],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005437939,0.00045779903,0.00046843328,0.0003069311,0.00049826974,0.00074539427,0.0007580493,0.00028435077,0.000020676089],"category_scores_gemma":[0.00011693379,0.00045474013,0.00014232729,0.00049448456,0.00010819871,0.00024095038,0.002162695,0.0011686921,0.0000058840405],"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":[0.000008461037,0.00036729756,0.049612977,0.00049706193,0.0000967442,0.00006236131,0.002748429,0.30729136,0.0003271035,0.00040867252,0.00011358326,0.63846594],"study_design_scores_gemma":[0.00048173402,0.00007145394,0.04317596,0.0002574459,0.000027130785,0.000056837875,0.00009472567,0.95339733,0.000015008501,0.0010946748,0.0009149443,0.000412721],"about_ca_topic_score_codex":0.0015100935,"about_ca_topic_score_gemma":0.00014287322,"teacher_disagreement_score":0.646106,"about_ca_system_score_codex":0.000114097405,"about_ca_system_score_gemma":0.00020072822,"threshold_uncertainty_score":0.99979043},"labels":[],"label_agreement":null},{"id":"W3182064321","doi":"10.1145/3449726.3463141","title":"House price prediction using clustering and genetic programming along with conducting a comparative study","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; DBSCAN; Artificial intelligence; Regression; Genetic programming; Machine learning; Artificial neural network; Regression analysis; CURE data clustering algorithm; Correlation clustering; Statistics; Mathematics","score_opus":0.05797735410638076,"score_gpt":0.27505070094710155,"score_spread":0.21707334684072077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182064321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6861511,0.00029004554,0.31279373,0.0001566743,0.00006143325,0.00044250005,0.000002461458,0.00007268947,0.000029359448],"genre_scores_gemma":[0.77949655,0.000020460615,0.22038293,0.0000115355015,0.00003802822,0.00002987952,0.0000015188948,0.00000876522,0.000010319521],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985287,0.00004604988,0.00038174677,0.0005089505,0.00032737324,0.00020717041],"domain_scores_gemma":[0.9986349,0.00006416651,0.00030651462,0.00012872956,0.00078209577,0.0000835988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012572401,0.00018487818,0.00022973861,0.000082350285,0.00059692655,0.00018219346,0.00024418038,0.000043320197,0.0000013071505],"category_scores_gemma":[0.000016281014,0.00015583902,0.000028505245,0.00053953583,0.00017411595,0.00044541288,0.00042784645,0.00014495732,4.8154374e-7],"study_design_candidate":"observational","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":[0.00008144474,0.0016846101,0.7694609,0.0005384883,0.00038630964,0.000012286448,0.027046451,0.11581077,0.034811616,0.010930052,0.00011883209,0.039118197],"study_design_scores_gemma":[0.00041363065,0.00013054468,0.40751725,0.00009132099,0.00003459006,0.00037319423,0.0027837995,0.58732104,0.0002018471,0.0009882279,0.000014355012,0.00013016997],"about_ca_topic_score_codex":0.000043255524,"about_ca_topic_score_gemma":0.0000075354,"teacher_disagreement_score":0.4715103,"about_ca_system_score_codex":0.00005346209,"about_ca_system_score_gemma":0.00013777513,"threshold_uncertainty_score":0.6354931},"labels":[],"label_agreement":null},{"id":"W3187199493","doi":"10.1109/cec45853.2021.9504998","title":"Evolving Simple Solutions to the CIFAR-10 Benchmark using Tangled Program Graphs","year":2021,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Benchmark (surveying); Benchmarking; Genetic programming; Class (philosophy); Simplicity; Machine learning; Modularity (biology); Artificial intelligence; Cardinality (data modeling); Graph; Contextual image classification; Theoretical computer science; Data mining; Image (mathematics)","score_opus":0.030184984218675798,"score_gpt":0.2877595361043339,"score_spread":0.25757455188565814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187199493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027679713,0.00031139344,0.9795537,0.007761526,0.00014335198,0.0003883899,0.0000068916447,0.00028755446,0.008779237],"genre_scores_gemma":[0.26652378,0.000009057716,0.7307652,0.0007890759,0.00012303532,0.00020143435,0.000020289945,0.000009596303,0.0015584904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987962,0.00005202735,0.00018904489,0.0003734456,0.00022830535,0.00036092353],"domain_scores_gemma":[0.9987516,0.00008516145,0.000038372596,0.0007414822,0.00026477413,0.000118647375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020121464,0.00010574625,0.000091977294,0.000054377022,0.0009255006,0.0002615622,0.00062145584,0.000035837787,0.00035476926],"category_scores_gemma":[0.000050447048,0.00008144576,0.000091889946,0.0015547669,0.00003689726,0.00030475014,0.0003948368,0.00009663678,0.000096357195],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016088729,0.00080125395,0.00059701997,0.000010906221,0.00007246879,0.000016306365,0.0006038836,0.0045759864,0.008533303,0.76021665,0.12503912,0.09953152],"study_design_scores_gemma":[0.00020261448,0.000068464695,0.008368938,0.00001703458,0.000021701573,0.0000928099,0.00030755586,0.67285067,0.0012373466,0.03187203,0.28456172,0.00039914055],"about_ca_topic_score_codex":0.00008500721,"about_ca_topic_score_gemma":0.00012049442,"teacher_disagreement_score":0.7283446,"about_ca_system_score_codex":0.00005076269,"about_ca_system_score_gemma":0.00016948648,"threshold_uncertainty_score":0.7118296},"labels":[],"label_agreement":null},{"id":"W3188410362","doi":"10.1109/tse.2021.3101818","title":"Combining Genetic Programming and Model Checking to Generate Environment Assumptions","year":2022,"lang":"en","type":"article","venue":"Aisberg (University of Bergamo)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; H2020 Excellent Science; Fonds National de la Recherche Luxembourg","keywords":"Computer science; Component (thermodynamics); Spurious relationship; Soundness; Flexibility (engineering); Benchmark (surveying); Software; Genetic programming; State (computer science); Artificial intelligence; Machine learning; Algorithm; Programming language","score_opus":0.014199649875834691,"score_gpt":0.18986800576013804,"score_spread":0.17566835588430335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188410362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31481218,0.00010010479,0.6829892,0.0017321149,0.000023111528,0.0001317647,0.0000070490114,0.00004142877,0.00016307294],"genre_scores_gemma":[0.6679708,0.00002632671,0.33137432,0.00004990383,0.0000057582124,0.0000037269608,0.000004040294,0.0000036796587,0.0005614493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929345,0.000028137933,0.00007666268,0.00026816243,0.00017997794,0.00015358813],"domain_scores_gemma":[0.99957365,0.000015405882,0.000059724076,0.0002468236,0.0000178879,0.00008649211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010270995,0.0000670772,0.00008713095,0.00008600564,0.00074404705,0.00001770471,0.00038936967,0.000017010283,0.000036279434],"category_scores_gemma":[0.0000010599585,0.00009625847,0.000035288635,0.00022924709,0.000058885307,0.00016562975,0.00084940053,0.00008697904,0.00000853928],"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":[0.0000074396494,0.00038181225,0.0024991937,0.000015913058,0.00006721984,0.000023820736,0.009759793,0.78804225,0.0044641155,0.08793394,0.001023852,0.105780676],"study_design_scores_gemma":[0.0002714666,0.00008671102,0.01297367,0.0000046651844,0.000018068129,0.000016140059,0.0009301927,0.97296363,0.000039590042,0.0013307881,0.0111845955,0.00018045674],"about_ca_topic_score_codex":0.00012899289,"about_ca_topic_score_gemma":0.000012827294,"teacher_disagreement_score":0.35315862,"about_ca_system_score_codex":0.00006328976,"about_ca_system_score_gemma":0.00003152039,"threshold_uncertainty_score":0.57226837},"labels":[],"label_agreement":null},{"id":"W3190647433","doi":"10.1109/cec45853.2021.9504827","title":"Deep Neural Network Guided Evolution of L-System Trees","year":2021,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Rotation formalisms in three dimensions; Computer science; Artificial intelligence; Deep learning; Genetic programming; Tree (set theory); Artificial neural network; Convolutional neural network; Machine learning; Function (biology); Grammatical evolution; Evolutionary algorithm; Tree structure; Theoretical computer science; Algorithm; Binary tree; Mathematics","score_opus":0.015209141321788067,"score_gpt":0.2375788196839892,"score_spread":0.22236967836220112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190647433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037773775,0.0005464062,0.98551816,0.0007282582,0.00019430429,0.00005930229,7.937885e-7,0.00013582918,0.009039577],"genre_scores_gemma":[0.8162171,0.000003231944,0.18303397,0.00004398265,0.000121388744,0.0000126929435,0.00000405427,0.0000027778817,0.00056076096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925184,0.000040217237,0.00021018481,0.00020628926,0.00014527592,0.0001462193],"domain_scores_gemma":[0.99928033,0.000039898237,0.000059856546,0.00041672276,0.00015868028,0.00004451327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009513417,0.000059835216,0.0000965394,0.000021588663,0.00010412344,0.000023943292,0.000300345,0.000030033365,0.000017426933],"category_scores_gemma":[0.000006190336,0.00005491703,0.000057990295,0.00055577094,0.000020715806,0.00018525876,0.00014189034,0.00003856463,0.000022102864],"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":[3.8675432e-7,0.000035133056,0.0012721653,0.000007733222,0.0000074561217,0.0000033688918,0.000027567892,0.028177414,0.0004574639,0.96358794,0.0024103322,0.004013048],"study_design_scores_gemma":[0.00010641411,0.000012699306,0.022341248,0.00000876242,0.0000036954873,0.000050411094,0.00006473583,0.9718549,0.00028622805,0.0038868783,0.0013139871,0.00006999446],"about_ca_topic_score_codex":0.00004414193,"about_ca_topic_score_gemma":0.000041989817,"teacher_disagreement_score":0.95970106,"about_ca_system_score_codex":0.000052426847,"about_ca_system_score_gemma":0.00005648372,"threshold_uncertainty_score":0.22394514},"labels":[],"label_agreement":null},{"id":"W3194234892","doi":"10.1145/3468857","title":"Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and ViZDoom Navigation Tasks","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Evolutionary Learning and Optimization","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"Compute Canada","keywords":"Computer science; Modularity (biology); Representation (politics); Process (computing); Theoretical computer science; Theme (computing); Graph; Artificial intelligence; Task (project management); State (computer science); Programming language; Systems engineering","score_opus":0.02006204693976415,"score_gpt":0.2547973784544936,"score_spread":0.23473533151472942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194234892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032464538,0.0007401174,0.9637584,0.0021772294,0.00014093636,0.0003600673,0.0000076065767,0.00023438656,0.00011670384],"genre_scores_gemma":[0.4017018,0.0010883302,0.59641993,0.000055560355,0.000028394765,0.00024278648,0.00016326134,0.000016649445,0.00028324994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985522,0.00015583582,0.0003055679,0.0005254079,0.000208873,0.00025211298],"domain_scores_gemma":[0.999186,0.00014544412,0.00010435143,0.0002558028,0.00020674962,0.000101670754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018761732,0.0001616309,0.0001469144,0.00015282987,0.00071088277,0.000095349365,0.00014562563,0.00010791907,0.000027220669],"category_scores_gemma":[0.000076528966,0.00018474432,0.00004859773,0.00096971856,0.00006384083,0.00063328835,0.000028188175,0.00031193014,0.0000031544887],"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":[0.000013742058,0.0002987018,0.0014435222,0.000019333287,0.000020025886,0.000010402793,0.00034749875,0.9309284,0.00011293084,0.002779801,0.000025820184,0.0639998],"study_design_scores_gemma":[0.00052667933,0.00017751365,0.0064691426,0.000095309144,0.000019312261,0.000087147,0.0002622335,0.9858091,0.000109471046,0.005199471,0.0010213235,0.0002233303],"about_ca_topic_score_codex":0.000040402905,"about_ca_topic_score_gemma":0.00002995603,"teacher_disagreement_score":0.36923727,"about_ca_system_score_codex":0.00007984966,"about_ca_system_score_gemma":0.00010154207,"threshold_uncertainty_score":0.75336546},"labels":[],"label_agreement":null},{"id":"W3194342044","doi":"10.1162/evco_a_00296","title":"An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming","year":2021,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Aga Khan Foundation","funders":"","keywords":"Computer science; Genetic programming; Intron; Theoretical computer science; Population; Artificial intelligence; Biology; Genetics; Gene","score_opus":0.0064796780933728815,"score_gpt":0.2599060293352423,"score_spread":0.2534263512418694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194342044","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61416507,0.00006457777,0.38545373,0.00010482892,0.00005642641,0.000116506955,0.000007434596,0.00001774577,0.00001365908],"genre_scores_gemma":[0.806205,0.000004822367,0.19371003,0.00001266567,0.000012859684,0.000024502478,0.000022094471,0.0000026233608,0.000005401468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988645,0.0001656096,0.00034560435,0.00026835772,0.00024773547,0.0001082362],"domain_scores_gemma":[0.99889004,0.00010638858,0.00018591945,0.00036646024,0.00042034237,0.00003083685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011506234,0.00007528372,0.00015613549,0.00024718046,0.000118947675,0.0000103184275,0.00031229097,0.00004215636,0.000002224618],"category_scores_gemma":[0.0000377151,0.00007044433,0.00009900972,0.004048141,0.00009734347,0.0003504452,0.00010519532,0.00009007795,0.0000010902361],"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":[0.0000013532992,0.00021589205,0.05543455,0.0000074326176,0.000046763176,8.428051e-7,0.00031728685,0.92557985,0.0013005406,0.012046984,0.0000026344842,0.005045864],"study_design_scores_gemma":[0.00007013643,0.000017795195,0.5207363,0.000008895938,0.000026110105,0.000005263733,0.000035601523,0.47707137,0.000103641556,0.0018794062,0.000009503203,0.00003599194],"about_ca_topic_score_codex":0.0001510741,"about_ca_topic_score_gemma":0.00007321491,"teacher_disagreement_score":0.46530172,"about_ca_system_score_codex":0.00007971967,"about_ca_system_score_gemma":0.00020806913,"threshold_uncertainty_score":0.28726363},"labels":[],"label_agreement":null},{"id":"W3203790825","doi":"10.1007/978-3-030-88113-9_5","title":"Valentino Braitenberg’s Table: Downhill Innovation of Vehicles via Darwinian Evolution","year":2021,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Darwinism; Darwin (ADL); Survivability; Exploit; Context (archaeology); Artificial intelligence; Computer security; Evolutionary biology; Geography; Biology","score_opus":0.024604560431335815,"score_gpt":0.26546234600582497,"score_spread":0.24085778557448914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203790825","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016220927,0.00067781267,0.9121974,0.0013043706,0.00025020563,0.0003692286,0.00004081583,0.00008402218,0.08491391],"genre_scores_gemma":[0.28057227,0.0034937526,0.7088178,0.0012956804,0.00015124644,0.00012080116,0.0007268875,0.000029142882,0.0047924193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769735,0.000035911922,0.0011024454,0.00036341455,0.00056748226,0.0002333925],"domain_scores_gemma":[0.9953879,0.0001559716,0.00068082364,0.002219277,0.0014845956,0.00007139322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010171202,0.0002223776,0.00028649252,0.0013472886,0.0005117532,0.00029309976,0.0025862071,0.00015809067,0.0000140885395],"category_scores_gemma":[0.00005193367,0.00024225899,0.00004802279,0.0020592737,0.000830419,0.006439583,0.002192651,0.00038428893,0.000023400828],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.12766e-7,0.00003167406,0.00007132363,0.000028561515,0.000005874233,1.4887422e-7,0.00035361355,0.00025051535,0.000061448365,0.889196,0.00021843458,0.10978174],"study_design_scores_gemma":[0.0005669766,0.000085569794,0.014610172,0.00046892714,0.000016422553,0.00007367791,0.00004427879,0.75241697,0.00013103714,0.065326616,0.16558425,0.0006750938],"about_ca_topic_score_codex":0.000022981401,"about_ca_topic_score_gemma":0.0000066113307,"teacher_disagreement_score":0.82386935,"about_ca_system_score_codex":0.00018858959,"about_ca_system_score_gemma":0.00063467544,"threshold_uncertainty_score":0.9879035},"labels":[],"label_agreement":null},{"id":"W3210102196","doi":"10.5281/zenodo.3875691","title":"Generation of OPCs and Oligodendrocytes from iPSCs (interactive SOP)","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science","score_opus":0.06533867339507925,"score_gpt":0.25291111197397537,"score_spread":0.1875724385788961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210102196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053024128,0.00030818614,0.92316157,0.0063561015,0.00007277527,0.00037757237,0.0002293706,0.00061382126,0.01585649],"genre_scores_gemma":[0.9841526,0.00009413467,0.014653044,0.0002578085,0.00017571922,5.255736e-8,0.00041540278,0.0001745835,0.000076667726],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991713,0.00009205263,0.00015345743,0.00029729793,0.00017235684,0.000113535665],"domain_scores_gemma":[0.9993267,0.000022974555,0.00007794112,0.0002221254,0.00024176642,0.000108462154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101412734,0.000069508955,0.000084483545,0.00004953363,0.00063268363,0.00027697237,0.00062293134,0.000026771355,0.0006300624],"category_scores_gemma":[0.00011594299,0.000074025425,0.000021916976,0.00029551817,0.00006526451,0.0004068047,0.0007927672,0.00010148167,0.0007120717],"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":[0.000056515866,0.0003840423,0.000021270444,0.000067034445,0.00014604726,0.000013561649,0.014545458,0.00083233014,0.20363556,0.1822709,0.11532994,0.48269734],"study_design_scores_gemma":[0.000582951,0.00030201825,0.0017249538,0.000019073983,0.000013613013,0.000033854474,0.0003098269,0.27976772,0.015864825,0.0015952744,0.6995472,0.00023874718],"about_ca_topic_score_codex":0.000016608335,"about_ca_topic_score_gemma":1.1289397e-7,"teacher_disagreement_score":0.93112844,"about_ca_system_score_codex":0.000026395308,"about_ca_system_score_gemma":0.0000030267424,"threshold_uncertainty_score":0.91524786},"labels":[],"label_agreement":null},{"id":"W3212075545","doi":"10.11606/d.45.2005.tde-20210729-143109","title":"Uma ferramenta interativa para análise de padrões baseada em entropia","year":2005,"lang":"pt","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Banting and Best Diabetes Centre, University of Toronto","keywords":"Humanities; Philosophy","score_opus":0.02318558703029229,"score_gpt":0.30354425542711827,"score_spread":0.280358668396826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212075545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069793314,0.0017433189,0.90678483,0.005297285,0.0014489025,0.0015343158,0.00016570368,0.0004852012,0.012747141],"genre_scores_gemma":[0.6260175,0.0007421065,0.19097354,0.002048988,0.0013315396,0.00076277804,0.00263672,0.000104768624,0.17538205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965142,0.00017023463,0.0007918693,0.0011596679,0.0005936547,0.00077036076],"domain_scores_gemma":[0.9977253,0.00017375607,0.00036874056,0.0010599189,0.000278637,0.00039362736],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002101013,0.00063029566,0.00047026353,0.00023271855,0.0006876859,0.00079287065,0.0015212662,0.00035371658,0.0028649687],"category_scores_gemma":[0.00003319126,0.0006166264,0.00030355935,0.0006156303,0.000070399845,0.00078519207,0.0002331624,0.0005946849,0.00091246836],"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":[0.00015450265,0.004154403,0.002003109,0.00039969268,0.00061556837,0.00011875494,0.034655664,0.0019617109,0.005409003,0.28452763,0.11628284,0.5497171],"study_design_scores_gemma":[0.0011132837,0.00033647253,0.029068578,0.00047609123,0.00013806675,0.00009266296,0.009923468,0.9094817,0.0025198443,0.0020606676,0.043057192,0.0017319603],"about_ca_topic_score_codex":0.0004218813,"about_ca_topic_score_gemma":0.0006501459,"teacher_disagreement_score":0.90752,"about_ca_system_score_codex":0.00041178605,"about_ca_system_score_gemma":0.0005543717,"threshold_uncertainty_score":0.9998654},"labels":[],"label_agreement":null},{"id":"W3517843","doi":"10.22215/etd/2008-08625","title":"Co-evolutionary automatically defined functions in genetic programming","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic programming; Computer science; Artificial intelligence","score_opus":0.012121426303189764,"score_gpt":0.25776849591061735,"score_spread":0.24564706960742758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3517843","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028741257,0.0021861743,0.8594483,0.0013515836,0.0012374613,0.0022510746,0.000033787514,0.0019638368,0.102786504],"genre_scores_gemma":[0.0339277,0.00019248441,0.9180399,0.00014362531,0.00020817321,0.0014460383,0.0017175415,0.00005194701,0.044272576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9979614,0.000046864017,0.00055823947,0.0006334697,0.00041276362,0.0003872412],"domain_scores_gemma":[0.99886686,0.00010367541,0.00014906182,0.00061045174,0.00014940885,0.00012053318],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000671902,0.00027423454,0.00026511823,0.00036315952,0.00034133552,0.00007777794,0.00071635924,0.00025903102,0.000093053946],"category_scores_gemma":[0.000031470212,0.00028084917,0.00012956973,0.0009048193,0.000046742556,0.00026729575,0.000038035956,0.0003375845,0.000498083],"study_design_candidate":"observational","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":[0.000042590873,0.004366721,0.006906251,0.0004937036,0.00024120529,0.0002911337,0.0032337087,0.0029128597,0.0009510298,0.43816364,0.13189594,0.4105012],"study_design_scores_gemma":[0.0008326996,0.00019786687,0.6765813,0.00018555095,0.00003806178,0.00027210405,0.00045954072,0.23759775,0.0000687876,0.012172743,0.07020518,0.0013884128],"about_ca_topic_score_codex":0.00011172842,"about_ca_topic_score_gemma":0.00018452207,"teacher_disagreement_score":0.66967505,"about_ca_system_score_codex":0.00015385596,"about_ca_system_score_gemma":0.0005385169,"threshold_uncertainty_score":0.99996436},"labels":[],"label_agreement":null},{"id":"W4200199124","doi":"10.5540/03.2021.008.01.0411","title":"Evolução diferencial com mutação ordenada em problemas de otimização monobjetivo com restrições de caixa","year":2021,"lang":"pt","type":"article","venue":"Proceeding Series of the Brazilian Society of Computational and Applied Mathematics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PTC (Canada)","funders":"","keywords":"Mathematics; Combinatorics; Computer science","score_opus":0.017802454329084966,"score_gpt":0.24075269294763832,"score_spread":0.22295023861855334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200199124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14028986,0.0004989084,0.84800017,0.0093750525,0.00012750924,0.00082318287,0.00023727366,0.00009380358,0.00055427634],"genre_scores_gemma":[0.50520515,0.0000815226,0.4934552,0.00014946969,0.00008138079,0.00004689467,0.000025120255,0.000028493807,0.0009267808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764615,0.000025461004,0.000813349,0.000445708,0.0006511724,0.00041818252],"domain_scores_gemma":[0.9977643,0.00037058233,0.00072780665,0.0003195143,0.0006643341,0.00015346546],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004101918,0.0003412584,0.0005498367,0.00003664827,0.00069610967,0.00020913065,0.0008000819,0.00017626923,0.000019333298],"category_scores_gemma":[0.000071763134,0.0003137696,0.00027447136,0.00072691013,0.00053201383,0.00030066687,0.00074963516,0.0003472397,0.0000023429932],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043053984,0.0019301325,0.002052448,0.0061845975,0.00073842116,0.0000018424789,0.08665361,0.045757707,0.0049983915,0.83187854,0.011971751,0.0077895154],"study_design_scores_gemma":[0.0006871967,0.00006501376,0.0030896142,0.0005277859,0.00012106011,0.000108695705,0.016581925,0.50131136,0.0040819505,0.47289413,0.00013681361,0.0003944363],"about_ca_topic_score_codex":0.000011266297,"about_ca_topic_score_gemma":0.0000035365981,"teacher_disagreement_score":0.45555365,"about_ca_system_score_codex":0.00009999714,"about_ca_system_score_gemma":0.0009803114,"threshold_uncertainty_score":0.99993145},"labels":[],"label_agreement":null},{"id":"W4205441696","doi":"10.1145/1830761.1830913","title":"Statistical analysis for evolutionary computation","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of Canada; University of Guelph","funders":"","keywords":"Evolutionary computation; Citation; Computer science; Government (linguistics); Library science; Operations research; Artificial intelligence; Mathematics","score_opus":0.013696854163332222,"score_gpt":0.2860462464602212,"score_spread":0.272349392296889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205441696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022784357,0.000005473582,0.99441934,0.001511017,0.00011307925,0.00013302232,0.000024998284,0.00012634271,0.0013883155],"genre_scores_gemma":[0.42457524,3.4840176e-7,0.57503027,0.000071909126,0.000038925213,0.000042171458,0.000053530453,0.0000014832472,0.00018612365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941134,0.000008642783,0.00012622275,0.0002228976,0.000111637026,0.000119259406],"domain_scores_gemma":[0.999385,0.00020062497,0.000029238043,0.00020299974,0.00011811721,0.000064001244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001034893,0.000052228603,0.00007356046,0.00009861418,0.00017029913,0.000043217642,0.00023262501,0.000032287313,0.000057860994],"category_scores_gemma":[0.000021516902,0.000048047095,0.00006004482,0.0005245844,0.000034626166,0.00018318823,0.00004567671,0.00006254606,0.000036622176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.768059e-7,0.00005673839,0.001648105,0.0000011798899,0.000034724988,1.9171746e-7,0.000013311946,0.0009901214,0.0002409096,0.9863066,0.00433149,0.0063759214],"study_design_scores_gemma":[0.00008544062,0.000014942295,0.14161518,1.5429298e-7,0.000022249853,0.0000025479737,0.0000034429147,0.7874592,0.000022695704,0.06538682,0.0053230743,0.00006428397],"about_ca_topic_score_codex":0.000018638593,"about_ca_topic_score_gemma":0.000020360174,"teacher_disagreement_score":0.92091984,"about_ca_system_score_codex":0.000009977175,"about_ca_system_score_gemma":0.000041229876,"threshold_uncertainty_score":0.19593036},"labels":[],"label_agreement":null},{"id":"W4205958788","doi":"10.1109/smc52423.2021.9659280","title":"The Behavioural and Topological Effects of Measurement Noise on Evolutionary Neurocontrollers","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Carleton University","funders":"","keywords":"Noise (video); Computer science; Topology (electrical circuits); Artificial intelligence; Engineering; Electrical engineering","score_opus":0.04878738255001716,"score_gpt":0.26713096991248897,"score_spread":0.2183435873624718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205958788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8004698,0.010926385,0.08349927,0.04107208,0.010729754,0.0029182874,0.00017100584,0.00021761494,0.049995776],"genre_scores_gemma":[0.99711156,0.00079630536,0.00040201194,0.00015408215,0.00011889474,0.000087748675,0.0000058511723,0.000005765699,0.0013177983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830455,0.00015104786,0.00032286838,0.00039725174,0.00064968824,0.00017460235],"domain_scores_gemma":[0.9986417,0.00027479962,0.00014713967,0.00030924933,0.0005359361,0.00009112925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023154344,0.00015698528,0.00018649777,0.00005370053,0.00021664685,0.00019412504,0.0004021207,0.000067231544,0.000011190685],"category_scores_gemma":[0.00007366008,0.00011940057,0.00005611353,0.000097829565,0.00014356569,0.000089067136,0.00012357935,0.00016422404,0.0000125947045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002876475,0.00022301465,0.001225529,0.000026874692,0.00007413574,0.000035027308,0.00010550374,0.000171234,0.0025843557,0.99022305,0.0011133102,0.004189219],"study_design_scores_gemma":[0.0059097903,0.0020510566,0.3744613,0.001288184,0.00016555848,0.00049970316,0.0013287321,0.5342858,0.0063331327,0.03617548,0.036013257,0.0014880331],"about_ca_topic_score_codex":0.000072868024,"about_ca_topic_score_gemma":0.000023318,"teacher_disagreement_score":0.95404756,"about_ca_system_score_codex":0.00006307522,"about_ca_system_score_gemma":0.00010685068,"threshold_uncertainty_score":0.4869014},"labels":[],"label_agreement":null},{"id":"W4211198679","doi":"10.1007/978-981-16-8113-4_1","title":"Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs","year":2022,"lang":"en","type":"book-chapter","venue":"Genetic 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reinforcement learning; Task (project management); Benchmark (surveying); Computer science; Modularity (biology); Artificial intelligence; Population; Benchmarking; Action (physics); Machine learning; Simple (philosophy); Theoretical computer science; Biology; Engineering","score_opus":0.0228751579642623,"score_gpt":0.2598006715739276,"score_spread":0.23692551360966532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211198679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048742097,0.001078304,0.99014276,0.00046646092,0.0001356882,0.0019020254,0.000020365605,0.00046017833,0.0053067817],"genre_scores_gemma":[0.28235838,0.0006043072,0.6636645,0.00032135335,0.00030089795,0.0023301838,0.0022940158,0.0001609298,0.04796541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974509,0.00005406377,0.000507141,0.0008976264,0.00061012094,0.00048014303],"domain_scores_gemma":[0.9988642,0.00008020551,0.0003050504,0.0002922409,0.00022391767,0.00023439623],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00017667213,0.00040654672,0.0002997704,0.00041328924,0.0016068317,0.00013083688,0.00035966423,0.00013753727,0.000082883045],"category_scores_gemma":[0.000008013525,0.0004286183,0.00010232981,0.00034493295,0.00011555841,0.00026118197,0.0005767288,0.0004261254,0.000044368062],"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":[0.000014308973,0.00016548026,0.00013785315,0.00004996784,0.00012556944,0.000012457001,0.00049403997,0.848611,0.000021785334,0.08446577,0.0019352755,0.063966505],"study_design_scores_gemma":[0.0006828904,0.001414434,0.0061708456,0.000079306206,0.00007416327,0.00013557101,0.000055964403,0.8670252,7.0556075e-7,0.022204746,0.10139846,0.00075772445],"about_ca_topic_score_codex":0.000048745576,"about_ca_topic_score_gemma":0.000014543262,"teacher_disagreement_score":0.32647824,"about_ca_system_score_codex":0.00027864057,"about_ca_system_score_gemma":0.00024334807,"threshold_uncertainty_score":0.99981654},"labels":[],"label_agreement":null},{"id":"W42125416","doi":"10.1007/978-1-4419-9685-5_8","title":"Evolutionary Algorithms and Speech Recognition","year":2011,"lang":"en","type":"book-chapter","venue":"Springer briefs in electrical and computer engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 de Moncton","funders":"","keywords":"Robustness (evolution); Computer science; Speech recognition; Front and back ends; TIMIT; Noise (video); Variance (accounting); Limit (mathematics); Algorithm; Artificial intelligence; Hidden Markov model; Mathematics","score_opus":0.013369227126444016,"score_gpt":0.19053204435400098,"score_spread":0.17716281722755697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W42125416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039411924,0.005424021,0.9828729,0.00023025945,0.00033546393,0.00036380283,0.000009478704,0.00035840046,0.010011578],"genre_scores_gemma":[0.0074584195,0.004764768,0.9718821,0.00040927983,0.0016413576,0.00011482889,0.000053571574,0.00013702415,0.013538642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844694,0.000008486812,0.00033290457,0.00068174,0.00019246321,0.00033749247],"domain_scores_gemma":[0.99935675,0.000080571735,0.0000721383,0.0002858789,0.000056156547,0.00014851995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011544452,0.00032488158,0.00030872412,0.0003221277,0.00007936419,0.0000742812,0.00030987378,0.00024339095,0.000012112081],"category_scores_gemma":[0.000004895963,0.00035765825,0.000062621235,0.0001476362,0.000043982152,0.00026002206,0.00037260854,0.00055936043,0.000023200539],"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":[0.000002993044,0.000043928365,0.000034596957,0.000042408694,0.000038636193,0.000065083645,0.000054066317,0.000078207115,0.000014980186,0.254919,0.00030791046,0.74439824],"study_design_scores_gemma":[0.00047471633,0.00020008683,0.0081484215,0.0002930446,0.000026437581,0.00056407356,2.9924615e-7,0.8087255,0.0000538094,0.11298498,0.06741556,0.0011130777],"about_ca_topic_score_codex":0.000026493939,"about_ca_topic_score_gemma":0.0000012563398,"teacher_disagreement_score":0.8086473,"about_ca_system_score_codex":0.000082666535,"about_ca_system_score_gemma":0.0000371739,"threshold_uncertainty_score":0.9998875},"labels":[],"label_agreement":null},{"id":"W4225395200","doi":"10.1109/tevc.2022.3152257","title":"IEEE Transactions on Evolutionary Computation Publication Information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Southern University of Science and Technology; Universität Bielefeld; Xidian University; Guangdong University of Technology; Brock University; Universidad Veracruzana; Zhengzhou University; University of Hong Kong; Swinburne University of Technology; Griffith University; RMIT University; Nanyang Technological University; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional; Shenzhen University; Nanjing University; Dalhousie University; University of Melbourne; University of Sheffield; University of Exeter; South China University of Technology; City University of Hong Kong; Aberystwyth University; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Evolutionary computation; Computer science; Computation; Evolutionary algorithm; Human-based evolutionary computation; Artificial intelligence; Theoretical computer science; Interactive evolutionary computation; Evolutionary programming; Algorithm","score_opus":0.014466550623449472,"score_gpt":0.24034917080551912,"score_spread":0.22588262018206964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225395200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019320218,0.00006794173,0.9856791,0.0052950773,0.002689208,0.0013818621,0.0005218994,0.0012816791,0.0011512233],"genre_scores_gemma":[0.94141585,0.00003771831,0.053916242,0.0015514224,0.0001243882,0.0016309766,0.0005864521,0.000051046358,0.0006859294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99465704,0.0005478765,0.0012336115,0.0010476097,0.0018186892,0.00069516426],"domain_scores_gemma":[0.9969767,0.0004909168,0.0005903397,0.0008006639,0.00082919054,0.00031222525],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005711836,0.0005742985,0.00038481655,0.0016081107,0.0041881283,0.00027027796,0.0010013087,0.00020764025,0.000294898],"category_scores_gemma":[0.0000103026405,0.00070556015,0.00039546075,0.003160414,0.00017017723,0.0040333024,0.00001016491,0.0011151643,0.0008439763],"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":[0.00009671837,0.0013484993,0.0000042512715,0.000020611116,0.00007587196,0.0000032434043,0.00049986254,0.8984676,0.00014116046,0.005093518,0.011243484,0.0830052],"study_design_scores_gemma":[0.0014660946,0.0008004088,0.0015142905,0.00002631271,0.000055225963,0.00021304021,0.000266176,0.9792404,0.0004364726,0.006986518,0.008235879,0.0007591506],"about_ca_topic_score_codex":0.00011038955,"about_ca_topic_score_gemma":0.000006642547,"teacher_disagreement_score":0.9394838,"about_ca_system_score_codex":0.0018580222,"about_ca_system_score_gemma":0.00061846816,"threshold_uncertainty_score":0.99993396},"labels":[],"label_agreement":null},{"id":"W4225566301","doi":"10.5206/mt.v2i1.14421","title":"The art of algorithmic guessing in gfun","year":2022,"lang":"en","type":"preprint","venue":"Maple Transactions","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Computer science","score_opus":0.016563302652947868,"score_gpt":0.2588338502748661,"score_spread":0.24227054762191824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225566301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028147953,0.00049744855,0.9897948,0.0062292884,0.0006639893,0.00033140628,0.000072495015,0.00009017524,0.0020389284],"genre_scores_gemma":[0.58582395,0.0017096319,0.39421237,0.0002162986,0.00028129766,0.0035169725,0.00016936031,0.000074926196,0.013995205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864703,0.00009416992,0.00038153713,0.00037284225,0.0002890143,0.00021542117],"domain_scores_gemma":[0.9987126,0.00016805516,0.00013579914,0.0008927631,0.000050060527,0.000040739695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003489043,0.00013956243,0.00017294043,0.00013943503,0.00051640917,0.00008365186,0.0011753051,0.00007807299,0.00012550612],"category_scores_gemma":[0.0000042438924,0.00012765233,0.00014994678,0.00050785515,0.000084583095,0.00012083549,0.00016606248,0.0007173304,0.000015463696],"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":[0.000018831517,0.001808329,0.00025142456,0.0002512755,0.0002659898,0.00002779083,0.0053230175,0.43310636,0.0009921279,0.14141566,0.008023617,0.4085156],"study_design_scores_gemma":[0.00043336485,0.000049853625,0.006302077,0.00008154043,0.000037290083,0.000024904308,0.00040160152,0.66850966,0.00035776442,0.075465225,0.24781066,0.0005260388],"about_ca_topic_score_codex":0.00019048245,"about_ca_topic_score_gemma":0.00014628278,"teacher_disagreement_score":0.5955824,"about_ca_system_score_codex":0.00012371785,"about_ca_system_score_gemma":0.00023127448,"threshold_uncertainty_score":0.5205511},"labels":[],"label_agreement":null},{"id":"W4230231196","doi":"10.1007/978-3-642-21822-4_7","title":"Basic Object Oriented Genetic Programming","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Carleton University","funders":"","keywords":"Computer science; Genetic programming; Object (grammar); Object-oriented programming; Theoretical computer science; Programming language; Linear programming; Artificial intelligence; Algorithm","score_opus":0.01606424199300017,"score_gpt":0.23396050537295532,"score_spread":0.21789626337995516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230231196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000049751496,0.0005096404,0.99365735,0.0003748868,0.00093643676,0.0005071137,0.0000036188712,0.00025314896,0.0037080476],"genre_scores_gemma":[0.026870875,0.00005235619,0.971335,0.00045311995,0.00044663847,0.000048069982,0.0000063605844,0.000033784494,0.0007538243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996565,0.000022571987,0.00046449035,0.0015477149,0.00073052605,0.0006696895],"domain_scores_gemma":[0.9976085,0.00013857397,0.00024000477,0.0015343111,0.00028090086,0.00019772265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039729843,0.00044989938,0.00035837898,0.00057851226,0.0003794543,0.00029362988,0.0029315099,0.00023991415,0.000041982614],"category_scores_gemma":[0.000027098753,0.00042113202,0.0001314611,0.0008552103,0.0006247827,0.0004463244,0.0011733419,0.00061652844,0.00012015964],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013141762,0.00004768002,0.000042787873,0.00001529746,0.000007981109,0.00004398533,0.0004978405,0.0018152518,0.000031226453,0.064905465,0.000021673652,0.9325695],"study_design_scores_gemma":[0.00038446175,0.000356669,0.0013298454,0.00037608488,0.000020127178,0.00028573492,2.6839845e-7,0.5014369,0.00059331045,0.46225327,0.03152866,0.0014346696],"about_ca_topic_score_codex":0.00003482088,"about_ca_topic_score_gemma":0.000041388408,"teacher_disagreement_score":0.9311348,"about_ca_system_score_codex":0.0002188279,"about_ca_system_score_gemma":0.0005552451,"threshold_uncertainty_score":0.99982405},"labels":[],"label_agreement":null},{"id":"W4230253172","doi":"10.1038/npre.2009.3913.1","title":"Design of a dynamic model of genes with multiple autonomous regulatory modules by evolution in silico","year":2009,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"In silico; Benchmark (surveying); Crossover; Computer science; Exploit; Evolutionary algorithm; Genetic algorithm; Computational biology; Artificial intelligence; Gene; Machine learning; Biology; Genetics","score_opus":0.00811556879099194,"score_gpt":0.22971087267270823,"score_spread":0.2215953038817163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230253172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09105544,0.0038236624,0.9039461,0.00025087822,0.000081859514,0.00063590007,0.000055676934,0.00009696974,0.000053491447],"genre_scores_gemma":[0.64394933,0.00008445195,0.35579374,0.00001287937,0.000011436542,0.00006889887,0.000023356317,0.000011020514,0.000044910936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981964,0.000051849853,0.00044127263,0.0006896171,0.00038494193,0.00023591648],"domain_scores_gemma":[0.99839604,0.00008454208,0.00046831495,0.0007286769,0.0002644829,0.000057945967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033323464,0.0002705281,0.0003995765,0.00025995614,0.00005033771,0.000022812113,0.001054933,0.0006721239,6.9006404e-7],"category_scores_gemma":[0.00002741702,0.00025707565,0.0000794447,0.00036647028,0.00010002066,0.00023404711,0.00037381257,0.0008197821,4.3293863e-7],"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":[0.000044906625,0.0003849787,0.000497744,0.00016593521,0.00004222889,6.5556e-7,0.0005831347,0.88177663,0.092259765,0.0147205535,0.0003661191,0.009157366],"study_design_scores_gemma":[0.00029355442,0.00006259729,0.00529173,0.00019643188,0.000014480306,0.0000037169486,0.000008712147,0.96458197,0.005086405,0.024212353,0.000008871515,0.00023915927],"about_ca_topic_score_codex":0.0000679782,"about_ca_topic_score_gemma":0.00001998662,"teacher_disagreement_score":0.5528939,"about_ca_system_score_codex":0.00028662063,"about_ca_system_score_gemma":0.00030814222,"threshold_uncertainty_score":0.99998814},"labels":[],"label_agreement":null},{"id":"W4230401539","doi":"10.32920/ryerson.14662446.v1","title":"Texture classification using gene expression programming","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Image texture; Pattern recognition (psychology); Computer science; Segmentation; Image segmentation; Computer vision; Pixel; Contextual image classification; Image processing; Digital image; Scale-space segmentation; Feature extraction; Texture (cosmology); Digital image processing; Feature (linguistics); Image (mathematics)","score_opus":0.0541649198913891,"score_gpt":0.3010817169562901,"score_spread":0.246916797064901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230401539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005199032,0.000528606,0.9919282,0.00079114374,0.0003391748,0.00033404265,0.0000031297866,0.00029426056,0.00058236835],"genre_scores_gemma":[0.17985049,0.000038160062,0.81933945,0.00006902418,0.00020286352,0.00011734963,0.000103602004,0.000010862999,0.0002681741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984105,0.000053703832,0.00026984274,0.0007780991,0.00028065793,0.00020717173],"domain_scores_gemma":[0.998368,0.00002372042,0.00018129247,0.0011378564,0.00020257718,0.00008655062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014119035,0.00018613193,0.00016451576,0.00007628681,0.00023811636,0.00044566457,0.00084923004,0.00025115843,0.000024744968],"category_scores_gemma":[0.00001135852,0.00017348284,0.00010996757,0.0002845362,0.000027991151,0.0002794064,0.0014747683,0.00038488215,0.000009680394],"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":[0.0000031017548,0.001150653,0.0008754842,0.00029553435,0.00009950559,0.00004180093,0.0017770781,0.018265147,0.35446754,0.0962653,0.002776417,0.52398247],"study_design_scores_gemma":[0.000075579745,0.000006193287,0.0016134154,0.00011741173,0.000011490115,0.000028676888,0.000107379,0.98330915,0.008129849,0.0024612139,0.0038046108,0.00033501652],"about_ca_topic_score_codex":0.00003887592,"about_ca_topic_score_gemma":0.0000025775964,"teacher_disagreement_score":0.965044,"about_ca_system_score_codex":0.00010300192,"about_ca_system_score_gemma":0.0002741537,"threshold_uncertainty_score":0.7074425},"labels":[],"label_agreement":null},{"id":"W4230496309","doi":"10.1145/3067695.3067712","title":"Introductory statistics for EC","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Citation; Computer science; Library science; Information retrieval; World Wide Web","score_opus":0.025675285711625303,"score_gpt":0.2639686961726163,"score_spread":0.238293410460991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230496309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08894165,0.00016409963,0.9061121,0.0035340218,0.00035475643,0.0004279474,0.000034983215,0.00004249636,0.0003879619],"genre_scores_gemma":[0.7310835,0.00003640455,0.2685975,0.000028784176,0.00009317093,0.000032266078,0.000004532577,0.0000045201627,0.00011926977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990799,0.000007414042,0.00025225524,0.00030662963,0.00019848194,0.0001553156],"domain_scores_gemma":[0.9985583,0.0000620036,0.00037972265,0.00021702629,0.0007258945,0.000057086505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013652357,0.00011495374,0.00014045982,0.000046401175,0.0010012059,0.0001731779,0.0008590219,0.000041827698,0.0000030158237],"category_scores_gemma":[0.00008260672,0.00009742134,0.000042050724,0.00007052996,0.0002593327,0.00036514478,0.0003828075,0.00007274224,0.0000023870616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013636297,0.00012256768,0.01497143,0.00012354675,0.000030687905,7.807064e-8,0.0004033832,0.00093043887,0.002931954,0.9194529,0.015512242,0.045507148],"study_design_scores_gemma":[0.00024570627,0.0000449178,0.4157875,0.000025021305,0.000010972646,0.000011901832,0.00003689828,0.39609012,0.00016284529,0.1863978,0.0010848219,0.00010147609],"about_ca_topic_score_codex":0.00001567095,"about_ca_topic_score_gemma":0.0000010109538,"teacher_disagreement_score":0.73305506,"about_ca_system_score_codex":0.000028350107,"about_ca_system_score_gemma":0.000095372285,"threshold_uncertainty_score":0.7700567},"labels":[],"label_agreement":null},{"id":"W4230649915","doi":"10.32920/ryerson.14646798","title":"Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Algorithm; Estimation of distribution algorithm; Tree (set theory); Computer science; Sampling distribution; Sampling (signal processing); Population; Mathematics; Distribution (mathematics); Probabilistic logic; Mathematical optimization; Statistics; Artificial intelligence; Filter (signal processing)","score_opus":0.022581532623731457,"score_gpt":0.28123412853360324,"score_spread":0.2586525959098718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230649915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032914877,0.0009051548,0.9935096,0.00077520014,0.00035207492,0.0007470946,0.00027885297,0.00006618059,0.0000743874],"genre_scores_gemma":[0.70796275,0.000041520405,0.29108122,0.000007934561,0.000069638474,0.00021137006,0.00055566867,0.0000065173763,0.00006335514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985367,0.000055995948,0.0005047838,0.0004193373,0.0003279333,0.0001552194],"domain_scores_gemma":[0.9978622,0.0003251464,0.0004999847,0.00081149465,0.00046713767,0.00003402571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032608674,0.0001749809,0.00029366466,0.00007073599,0.00015502084,0.000060290913,0.00085692917,0.00018329738,0.000010335552],"category_scores_gemma":[0.000102263555,0.00013155714,0.00022642955,0.00039341417,0.00010085502,0.00017467186,0.00053236214,0.00018279924,5.0869363e-7],"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":[0.000015738708,0.0003455573,0.00014388065,0.00043779117,0.00029505868,4.3602907e-7,0.0006572098,0.16373776,0.0014923213,0.5432686,0.0012731069,0.28833255],"study_design_scores_gemma":[0.00015875636,0.000034666158,0.018500797,0.000042979922,0.00005165075,0.0000033209199,0.00010511957,0.9325318,0.0029377772,0.045410458,0.00009526745,0.0001274148],"about_ca_topic_score_codex":0.00038087374,"about_ca_topic_score_gemma":0.000033136883,"teacher_disagreement_score":0.768794,"about_ca_system_score_codex":0.00017266528,"about_ca_system_score_gemma":0.00041406744,"threshold_uncertainty_score":0.5364744},"labels":[],"label_agreement":null},{"id":"W4231385108","doi":"10.22215/etd/2008-08966","title":"Grammar-based object-oriented genetic programming: an initial implementation","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Computer science; Grammar; Object (grammar); Programming language; Artificial intelligence; Linguistics; Philosophy","score_opus":0.018822490395583445,"score_gpt":0.3257691646648061,"score_spread":0.3069466742692227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231385108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059272457,0.00016502605,0.93316966,0.00023000289,0.0010083908,0.0017097216,0.000032306576,0.0009293824,0.0034830796],"genre_scores_gemma":[0.18427363,0.000057692665,0.7966462,0.0003106129,0.0005483498,0.0016130821,0.013596653,0.00007540799,0.0028783863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785227,0.000067055546,0.00047052978,0.0007296818,0.0005015589,0.00037889613],"domain_scores_gemma":[0.99856144,0.000031901604,0.00024701538,0.00067531684,0.00031092414,0.00017342267],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008669379,0.0003016293,0.00020788902,0.00023588113,0.00044715428,0.00015058133,0.00070325483,0.00018613113,0.00010300387],"category_scores_gemma":[0.000006660019,0.00031048458,0.00012179032,0.00065507746,0.000033841647,0.0003841515,0.000034604018,0.00020866732,0.000058991332],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034518016,0.0012044805,0.00052233995,0.000094280855,0.00007641243,0.000052911346,0.0022858027,0.00023128065,0.0005722797,0.05666097,0.004284173,0.9339805],"study_design_scores_gemma":[0.008323829,0.0044466336,0.21138151,0.00019385964,0.0003526043,0.00023292833,0.007843784,0.36384898,0.030674985,0.011136291,0.3546035,0.006961096],"about_ca_topic_score_codex":0.00038981062,"about_ca_topic_score_gemma":0.00077040197,"teacher_disagreement_score":0.9270195,"about_ca_system_score_codex":0.000087058164,"about_ca_system_score_gemma":0.0006219938,"threshold_uncertainty_score":0.99993473},"labels":[],"label_agreement":null},{"id":"W4231974562","doi":"10.1155/2006/183949","title":"A Game Theoretic Approach to Swarm Robotics","year":2006,"lang":"en","type":"article","venue":"Applied Bionics and Biomechanics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Evolutionary robotics; Robot; Swarm robotics; Set (abstract data type); Personality; Reinforcement learning; Process (computing); Computer science; Swarm behaviour; Robotics; Behavior-based robotics; Machine learning; Human–computer interaction; Psychology; Social psychology","score_opus":0.008189348044613621,"score_gpt":0.2005669144531002,"score_spread":0.19237756640848658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231974562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025529764,0.00015723,0.99040246,0.0012190301,0.000077446006,0.0003253279,0.0000094720635,0.00014832964,0.0051077087],"genre_scores_gemma":[0.7197902,0.000060270166,0.27943218,0.00041093392,0.000082509374,0.000061250495,0.000027488342,0.000011063571,0.00012409942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988249,0.000009357621,0.00020638248,0.0004694372,0.00019784948,0.00029205688],"domain_scores_gemma":[0.9993085,0.000024501698,0.000057030407,0.0004456601,0.000048061884,0.00011621482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018839996,0.00015857062,0.00013533744,0.00011014267,0.00018018663,0.00015013308,0.00048318913,0.00009334435,0.00000108188],"category_scores_gemma":[0.0000017227251,0.00013712632,0.000038094495,0.0007581997,0.00004065744,0.00006245215,0.00032652944,0.00009119213,0.000047253736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013750251,0.00013975885,8.005632e-7,0.000008342012,0.0000042976303,4.2502452e-7,0.000035266694,0.00034681396,0.0038092767,0.98922324,0.0003209299,0.0061094547],"study_design_scores_gemma":[0.0003045931,0.00006681915,0.00004860461,0.000006375073,0.000012383574,0.00002874791,0.000042142616,0.42005357,0.0038498717,0.5565521,0.018659124,0.00037563455],"about_ca_topic_score_codex":0.000014543602,"about_ca_topic_score_gemma":0.0000011821035,"teacher_disagreement_score":0.71723723,"about_ca_system_score_codex":0.00003046662,"about_ca_system_score_gemma":0.000035904442,"threshold_uncertainty_score":0.5591849},"labels":[],"label_agreement":null},{"id":"W4232518276","doi":"10.32920/ryerson.14662446","title":"Texture classification using gene expression programming","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Image texture; Pattern recognition (psychology); Computer science; Segmentation; Image segmentation; Computer vision; Pixel; Digital image; Image processing; Contextual image classification; Scale-space segmentation; Feature extraction; Texture (cosmology); Digital image processing; Image (mathematics); Feature (linguistics)","score_opus":0.0541649198913891,"score_gpt":0.3010817169562901,"score_spread":0.246916797064901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232518276","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005199032,0.000528606,0.9919282,0.00079114374,0.0003391748,0.00033404265,0.0000031297866,0.00029426056,0.00058236835],"genre_scores_gemma":[0.17985049,0.000038160062,0.81933945,0.00006902418,0.00020286352,0.00011734963,0.000103602004,0.000010862999,0.0002681741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984105,0.000053703832,0.00026984274,0.0007780991,0.00028065793,0.00020717173],"domain_scores_gemma":[0.998368,0.00002372042,0.00018129247,0.0011378564,0.00020257718,0.00008655062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014119035,0.00018613193,0.00016451576,0.00007628681,0.00023811636,0.00044566457,0.00084923004,0.00025115843,0.000024744968],"category_scores_gemma":[0.00001135852,0.00017348284,0.00010996757,0.0002845362,0.000027991151,0.0002794064,0.0014747683,0.00038488215,0.000009680394],"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":[0.0000031017548,0.001150653,0.0008754842,0.00029553435,0.00009950559,0.00004180093,0.0017770781,0.018265147,0.35446754,0.0962653,0.002776417,0.52398247],"study_design_scores_gemma":[0.000075579745,0.000006193287,0.0016134154,0.00011741173,0.000011490115,0.000028676888,0.000107379,0.98330915,0.008129849,0.0024612139,0.0038046108,0.00033501652],"about_ca_topic_score_codex":0.00003887592,"about_ca_topic_score_gemma":0.0000025775964,"teacher_disagreement_score":0.965044,"about_ca_system_score_codex":0.00010300192,"about_ca_system_score_gemma":0.0002741537,"threshold_uncertainty_score":0.7074425},"labels":[],"label_agreement":null},{"id":"W4232646195","doi":"10.32920/ryerson.14652885.v1","title":"A novel developmental genetic programming methodology for mathematical modeling and neuroevolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Neuroevolution; Symbolic regression; Genetic programming; Computer science; Artificial intelligence; Artificial neural network; Pairwise comparison; Representation (politics); Genetic algorithm; Set (abstract data type); Interpretation (philosophy); Theoretical computer science; Machine learning; Programming language","score_opus":0.1301289569377599,"score_gpt":0.3282239170783256,"score_spread":0.1980949601405657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232646195","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005276619,0.00019180485,0.99288684,0.0006666706,0.00008728169,0.0006213181,0.000003954469,0.00012431126,0.00014118597],"genre_scores_gemma":[0.011290633,0.000020615023,0.98770446,0.00007010695,0.00005626934,0.00063320756,0.000022237185,0.000011565264,0.00019093677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985941,0.00004349038,0.00031735495,0.00068780396,0.00012646406,0.0002307535],"domain_scores_gemma":[0.9992351,0.00018780126,0.00007124178,0.0003005283,0.00012745705,0.00007789122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003085506,0.00016659168,0.00022855541,0.000067191075,0.00016514596,0.00018242587,0.0003854735,0.00014149038,0.000014115022],"category_scores_gemma":[0.000083271676,0.00016617616,0.000075218806,0.00010634863,0.000034972505,0.00009730227,0.0012805113,0.00018683229,0.0000059153053],"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":[0.000011013024,0.0008705926,0.000039760267,0.0009996268,0.00021205847,0.000009848927,0.0022165056,0.046499446,0.0064525157,0.47780374,0.00017536082,0.46470955],"study_design_scores_gemma":[0.00013002235,0.000014263289,0.00008514819,0.000030217336,0.000015656897,0.00019297024,0.00007608086,0.96886307,0.00005351603,0.030103281,0.00025563312,0.00018013967],"about_ca_topic_score_codex":0.000021511201,"about_ca_topic_score_gemma":0.0000060105253,"teacher_disagreement_score":0.92236364,"about_ca_system_score_codex":0.000053723186,"about_ca_system_score_gemma":0.00022783692,"threshold_uncertainty_score":0.67764676},"labels":[],"label_agreement":null},{"id":"W4233887172","doi":"10.1007/978-1-4939-7131-2_100359","title":"Evolving Graphs","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Calgary","funders":"","keywords":"Computer science","score_opus":0.017480166612028118,"score_gpt":0.23096192419778314,"score_spread":0.21348175758575502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233887172","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.6443188e-7,0.00021734743,0.29535872,0.00033947596,0.00013827544,0.00007300322,0.0000029696403,0.00018577873,0.70368403],"genre_scores_gemma":[0.00010409819,0.00005564023,0.1342433,0.00023981718,0.00018954717,0.000008944563,0.000007859656,0.000012571825,0.86513823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99915105,0.0000025183165,0.00015358882,0.0003738257,0.00018617813,0.0001328455],"domain_scores_gemma":[0.99901396,0.000029186833,0.00007210253,0.0006897367,0.0001272567,0.00006775736],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006499968,0.00014802068,0.00011515394,0.00009393682,0.00014133874,0.00007578987,0.0007585683,0.0001231083,0.0021789777],"category_scores_gemma":[0.0000024558615,0.00013459638,0.0000965334,0.000040200794,0.000067596586,0.00018636066,0.00026454797,0.00012431508,0.002268022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.216971e-8,0.0000037762638,5.24426e-7,0.0000013108022,0.0000080183945,0.000001274839,0.000014922203,2.3560496e-7,0.0000027836884,0.91323197,0.084206626,0.002528515],"study_design_scores_gemma":[0.00002209457,0.000014704063,0.000020532785,0.000013224138,0.0000031152686,0.00000834988,5.447537e-7,0.0029187151,0.0000054184165,0.51972264,0.4771371,0.00013357968],"about_ca_topic_score_codex":0.0000048311226,"about_ca_topic_score_gemma":0.0000038420053,"teacher_disagreement_score":0.39350933,"about_ca_system_score_codex":0.000026240212,"about_ca_system_score_gemma":0.00005550177,"threshold_uncertainty_score":0.99873316},"labels":[],"label_agreement":null},{"id":"W4234407257","doi":"10.32920/ryerson.14645649.v1","title":"On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Evolvability; Representation (politics); Computer science; Genetic programming; Rank (graph theory); Formicoidea; Ant colony; Ant colony optimization algorithms; Artificial intelligence; Theoretical computer science; Mathematical optimization; Mathematics; Ecology; Biology; Evolutionary biology; Aculeata","score_opus":0.06438474259163568,"score_gpt":0.30902303631426026,"score_spread":0.24463829372262458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234407257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07206948,0.00025281444,0.9216178,0.0044878535,0.00021821925,0.000664095,0.000007560145,0.00008534719,0.00059680396],"genre_scores_gemma":[0.36533204,0.000016506208,0.63407576,0.00016431663,0.000037699578,0.00028617308,0.00000785265,0.000005556588,0.00007413742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792945,0.0005283,0.0003803618,0.00068832707,0.00024849808,0.00022508363],"domain_scores_gemma":[0.99677193,0.0010691896,0.00020397406,0.0016728103,0.00022272373,0.000059355967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086158124,0.00017651978,0.00030653813,0.000051348685,0.00013195419,0.000085375235,0.0013063357,0.00008236701,0.00006744878],"category_scores_gemma":[0.00020452491,0.00012440982,0.00018548431,0.00024350229,0.00016167844,0.000039307542,0.0013461726,0.00039356708,0.0000072244193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001016892,0.0014959105,0.0009361982,0.00026643774,0.00025898713,0.000045106695,0.0013425073,0.010277257,0.0009823884,0.8020978,0.0025061904,0.17978106],"study_design_scores_gemma":[0.00024676445,0.00033068523,0.031775683,0.0001441761,0.000069331436,0.00011791143,0.00027495937,0.64895886,0.0063632946,0.30803704,0.0030071316,0.00067415746],"about_ca_topic_score_codex":0.00022615933,"about_ca_topic_score_gemma":0.000024629442,"teacher_disagreement_score":0.6386816,"about_ca_system_score_codex":0.00005965528,"about_ca_system_score_gemma":0.00034773472,"threshold_uncertainty_score":0.5073285},"labels":[],"label_agreement":null},{"id":"W4234443174","doi":"10.1145/1388969.1389071","title":"An introduction to statistical analysis for evolutionary computation","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Citation; Computer science; Evolutionary computation; Library science; Artificial intelligence","score_opus":0.01886113577918248,"score_gpt":0.2895190277285327,"score_spread":0.27065789194935025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234443174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052324357,0.0000060381585,0.98723674,0.0069373823,0.0000850997,0.00022029603,0.00002102835,0.00016392172,0.000097044],"genre_scores_gemma":[0.39409822,8.838732e-7,0.60520786,0.00014239181,0.0002109628,0.000065922795,0.00012482944,0.0000023194723,0.0001465828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991401,0.000025229485,0.00016111549,0.00037831665,0.00015518622,0.00014005775],"domain_scores_gemma":[0.9993174,0.00006806491,0.000027732405,0.0002824867,0.00018492606,0.00011939262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010225881,0.00006586673,0.00009537722,0.00018581391,0.00032245682,0.000026298958,0.00022105701,0.000024675279,0.000028659457],"category_scores_gemma":[0.00001828781,0.00006499592,0.000045636934,0.0009866585,0.000028934057,0.00036497196,0.000031921885,0.000031436495,0.000037016966],"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":[0.000007794715,0.0002835251,0.0019502664,0.0000023034834,0.00006756993,0.0000010446809,0.00018388635,0.12222853,0.00034421726,0.8145377,0.05251452,0.007878646],"study_design_scores_gemma":[0.000087379754,0.00011002049,0.15362456,1.6713274e-7,0.000018740957,0.000012121794,0.000011886803,0.8316583,0.000038246402,0.007786637,0.006557054,0.00009487261],"about_ca_topic_score_codex":0.00002607772,"about_ca_topic_score_gemma":0.0000065420036,"teacher_disagreement_score":0.8067511,"about_ca_system_score_codex":0.000049629787,"about_ca_system_score_gemma":0.000045752753,"threshold_uncertainty_score":0.26504564},"labels":[],"label_agreement":null},{"id":"W4234818670","doi":"10.1002/0470871822.part2","title":"Part Introduction","year":2006,"lang":"en","type":"other","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Library science; Citation; Engineering; Computer science; Art history; History","score_opus":0.0069765364591289035,"score_gpt":0.21423423406044342,"score_spread":0.20725769760131452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234818670","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.2550755e-8,0.000109103144,0.43446136,0.0018182169,0.0002853355,0.000046055833,0.0000020567668,0.00034836706,0.5629295],"genre_scores_gemma":[0.0000010233488,0.000019697787,0.10678075,0.000058126443,0.002792478,0.0000221696,0.000032012547,0.000035890465,0.89025784],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951404,0.0000064909873,0.00006668831,0.00024398185,0.00008634718,0.00008244545],"domain_scores_gemma":[0.99950135,0.000003269828,0.00004323213,0.0004222084,0.00001046628,0.000019447978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025934067,0.00007157641,0.000060712988,0.00006646523,0.000028738126,0.000027234568,0.00027889077,0.00006618227,0.00059739954],"category_scores_gemma":[9.834479e-7,0.000063646956,0.000024974652,0.00014475844,0.000015108047,0.000042612064,0.000053416512,0.000050245642,0.00057975715],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.1333686e-8,0.00001364816,9.238707e-7,0.0000012358499,0.0000020179905,1.6700903e-7,4.6446587e-7,0.000002445119,9.518031e-7,0.20652121,0.7900487,0.0034081882],"study_design_scores_gemma":[0.000022938897,0.0000034103514,0.000021331496,0.000002760906,0.0000013801044,0.0000033080448,2.7673443e-7,0.0010628329,0.0000058385517,0.0019151294,0.9968802,0.00008061368],"about_ca_topic_score_codex":0.0001258136,"about_ca_topic_score_gemma":0.000040200277,"teacher_disagreement_score":0.32768062,"about_ca_system_score_codex":0.000011680305,"about_ca_system_score_gemma":0.000017129387,"threshold_uncertainty_score":0.74517983},"labels":[],"label_agreement":null},{"id":"W4237268483","doi":"10.1109/tevc.2015.2415203","title":"IEEE Transactions on Evolutionary Computation publication information","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Guelph; Dalhousie University","funders":"","keywords":"Computer science; Evolutionary computation; Evolutionary algorithm; Computation; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.024158947341346736,"score_gpt":0.25668197319355296,"score_spread":0.23252302585220622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237268483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016308343,0.00007468508,0.98610044,0.0048648547,0.0026052194,0.0012254639,0.00022056559,0.0013885889,0.0018893239],"genre_scores_gemma":[0.8944251,0.00004161219,0.102637805,0.0011111231,0.00018398734,0.00060362864,0.00038180721,0.000048233895,0.00056671747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950593,0.00037709437,0.0012520831,0.0009902095,0.0016258474,0.00069544866],"domain_scores_gemma":[0.99592704,0.00040917334,0.0005383469,0.00080309814,0.0017532222,0.0005690947],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058617175,0.0006187726,0.00041350984,0.0013984287,0.0014291642,0.0003567859,0.0008255632,0.000371802,0.000054477532],"category_scores_gemma":[0.000023729865,0.0007008987,0.00033197124,0.0026189056,0.00021231639,0.0058016027,0.000005114145,0.0007750444,0.0020641596],"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":[0.00010911451,0.0011706426,0.000006514594,0.000023848683,0.00008041338,0.0000030723859,0.00065801595,0.88271415,0.00008069309,0.0049888953,0.017214859,0.0929498],"study_design_scores_gemma":[0.001919154,0.00069981726,0.0012879728,0.000061731735,0.000058491227,0.00015139651,0.00021745395,0.9788689,0.00071866874,0.010367588,0.004873897,0.00077494263],"about_ca_topic_score_codex":0.00012568834,"about_ca_topic_score_gemma":0.000011548962,"teacher_disagreement_score":0.89279425,"about_ca_system_score_codex":0.0014809017,"about_ca_system_score_gemma":0.00082758913,"threshold_uncertainty_score":0.99987084},"labels":[],"label_agreement":null},{"id":"W4239091323","doi":"10.1002/0470871822.part1","title":"Part Introduction","year":2006,"lang":"en","type":"other","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Library science; Citation; Engineering; Computer science","score_opus":0.0069765364591289035,"score_gpt":0.21423423406044342,"score_spread":0.20725769760131452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239091323","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.2550755e-8,0.000109103144,0.43446136,0.0018182169,0.0002853355,0.000046055833,0.0000020567668,0.00034836706,0.5629295],"genre_scores_gemma":[0.0000010233488,0.000019697787,0.10678075,0.000058126443,0.002792478,0.0000221696,0.000032012547,0.000035890465,0.89025784],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951404,0.0000064909873,0.00006668831,0.00024398185,0.00008634718,0.00008244545],"domain_scores_gemma":[0.99950135,0.000003269828,0.00004323213,0.0004222084,0.00001046628,0.000019447978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025934067,0.00007157641,0.000060712988,0.00006646523,0.000028738126,0.000027234568,0.00027889077,0.00006618227,0.00059739954],"category_scores_gemma":[9.834479e-7,0.000063646956,0.000024974652,0.00014475844,0.000015108047,0.000042612064,0.000053416512,0.000050245642,0.00057975715],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.1333686e-8,0.00001364816,9.238707e-7,0.0000012358499,0.0000020179905,1.6700903e-7,4.6446587e-7,0.000002445119,9.518031e-7,0.20652121,0.7900487,0.0034081882],"study_design_scores_gemma":[0.000022938897,0.0000034103514,0.000021331496,0.000002760906,0.0000013801044,0.0000033080448,2.7673443e-7,0.0010628329,0.0000058385517,0.0019151294,0.9968802,0.00008061368],"about_ca_topic_score_codex":0.0001258136,"about_ca_topic_score_gemma":0.000040200277,"teacher_disagreement_score":0.32768062,"about_ca_system_score_codex":0.000011680305,"about_ca_system_score_gemma":0.000017129387,"threshold_uncertainty_score":0.74517983},"labels":[],"label_agreement":null},{"id":"W4239486805","doi":"10.32920/ryerson.14652885","title":"A novel developmental genetic programming methodology for mathematical modeling and neuroevolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Neuroevolution; Symbolic regression; Genetic programming; Computer science; Artificial intelligence; Artificial neural network; Pairwise comparison; Representation (politics); Set (abstract data type); Genetic algorithm; Interpretation (philosophy); Theoretical computer science; Machine learning; Programming language","score_opus":0.1301289569377599,"score_gpt":0.3282239170783256,"score_spread":0.1980949601405657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239486805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005276619,0.00019180485,0.99288684,0.0006666706,0.00008728169,0.0006213181,0.000003954469,0.00012431126,0.00014118597],"genre_scores_gemma":[0.011290633,0.000020615023,0.98770446,0.00007010695,0.00005626934,0.00063320756,0.000022237185,0.000011565264,0.00019093677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985941,0.00004349038,0.00031735495,0.00068780396,0.00012646406,0.0002307535],"domain_scores_gemma":[0.9992351,0.00018780126,0.00007124178,0.0003005283,0.00012745705,0.00007789122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003085506,0.00016659168,0.00022855541,0.000067191075,0.00016514596,0.00018242587,0.0003854735,0.00014149038,0.000014115022],"category_scores_gemma":[0.000083271676,0.00016617616,0.000075218806,0.00010634863,0.000034972505,0.00009730227,0.0012805113,0.00018683229,0.0000059153053],"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":[0.000011013024,0.0008705926,0.000039760267,0.0009996268,0.00021205847,0.000009848927,0.0022165056,0.046499446,0.0064525157,0.47780374,0.00017536082,0.46470955],"study_design_scores_gemma":[0.00013002235,0.000014263289,0.00008514819,0.000030217336,0.000015656897,0.00019297024,0.00007608086,0.96886307,0.00005351603,0.030103281,0.00025563312,0.00018013967],"about_ca_topic_score_codex":0.000021511201,"about_ca_topic_score_gemma":0.0000060105253,"teacher_disagreement_score":0.92236364,"about_ca_system_score_codex":0.000053723186,"about_ca_system_score_gemma":0.00022783692,"threshold_uncertainty_score":0.67764676},"labels":[],"label_agreement":null},{"id":"W4240179485","doi":"10.1007/978-1-4615-0377-4_6","title":"Genetic Algorithm","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF","funders":"","keywords":"Simulated annealing; Computer science; Genetic algorithm; Algorithm; Theoretical computer science; Machine learning","score_opus":0.013758157240657971,"score_gpt":0.21743094720136755,"score_spread":0.2036727899607096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240179485","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.9214205e-8,0.0003846892,0.5198232,0.00038691994,0.00011057776,0.00008190577,0.000004646564,0.00009435833,0.47911367],"genre_scores_gemma":[6.7785385e-7,0.00012532725,0.46838284,0.00049115915,0.000085694555,0.000010815771,0.0000039443007,0.000011317057,0.5308882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989657,0.000004934969,0.00020122349,0.00044389482,0.00021888189,0.00016537395],"domain_scores_gemma":[0.998979,0.000022649596,0.00007501868,0.0007539618,0.00007467521,0.00009470393],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000044042936,0.00019570164,0.00015118459,0.00007604016,0.00010828039,0.00005977104,0.000658511,0.00015774083,0.0005269798],"category_scores_gemma":[0.0000011456706,0.0001858468,0.00010097199,0.000039681738,0.00004186026,0.00008631728,0.00014125562,0.00016855577,0.001288677],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.2796202e-8,0.0000073839756,1.5359336e-7,0.0000014767866,0.000009827837,0.000006572201,0.0000038710245,0.0000053485055,5.919515e-7,0.8463107,0.030598082,0.12305602],"study_design_scores_gemma":[0.000043213513,0.000014445176,0.000018152243,0.000006287185,0.0000053814097,0.000046836973,2.7197447e-7,0.010502934,0.0000034398745,0.23598336,0.75317055,0.00020512116],"about_ca_topic_score_codex":0.0000038734197,"about_ca_topic_score_gemma":6.9462396e-7,"teacher_disagreement_score":0.7225725,"about_ca_system_score_codex":0.00003997467,"about_ca_system_score_gemma":0.00006515715,"threshold_uncertainty_score":0.99948895},"labels":[],"label_agreement":null},{"id":"W4240996730","doi":"10.32920/ryerson.14646798.v1","title":"Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Estimation of distribution algorithm; Algorithm; Tree (set theory); Computer science; Sampling distribution; Population; Sampling (signal processing); Mathematics; Distribution (mathematics); Probabilistic logic; Statistics; Machine learning; Filter (signal processing)","score_opus":0.022581532623731457,"score_gpt":0.28123412853360324,"score_spread":0.2586525959098718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240996730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032914877,0.0009051548,0.9935096,0.00077520014,0.00035207492,0.0007470946,0.00027885297,0.00006618059,0.0000743874],"genre_scores_gemma":[0.70796275,0.000041520405,0.29108122,0.000007934561,0.000069638474,0.00021137006,0.00055566867,0.0000065173763,0.00006335514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985367,0.000055995948,0.0005047838,0.0004193373,0.0003279333,0.0001552194],"domain_scores_gemma":[0.9978622,0.0003251464,0.0004999847,0.00081149465,0.00046713767,0.00003402571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032608674,0.0001749809,0.00029366466,0.00007073599,0.00015502084,0.000060290913,0.00085692917,0.00018329738,0.000010335552],"category_scores_gemma":[0.000102263555,0.00013155714,0.00022642955,0.00039341417,0.00010085502,0.00017467186,0.00053236214,0.00018279924,5.0869363e-7],"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":[0.000015738708,0.0003455573,0.00014388065,0.00043779117,0.00029505868,4.3602907e-7,0.0006572098,0.16373776,0.0014923213,0.5432686,0.0012731069,0.28833255],"study_design_scores_gemma":[0.00015875636,0.000034666158,0.018500797,0.000042979922,0.00005165075,0.0000033209199,0.00010511957,0.9325318,0.0029377772,0.045410458,0.00009526745,0.0001274148],"about_ca_topic_score_codex":0.00038087374,"about_ca_topic_score_gemma":0.000033136883,"teacher_disagreement_score":0.768794,"about_ca_system_score_codex":0.00017266528,"about_ca_system_score_gemma":0.00041406744,"threshold_uncertainty_score":0.5364744},"labels":[],"label_agreement":null},{"id":"W4241234484","doi":"10.1109/tevc.2015.2432412","title":"IEEE Transactions on Evolutionary Computation publication information","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Guelph; Dalhousie University","funders":"","keywords":"Computer science; Evolutionary computation; Computation; Evolutionary algorithm; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.024158947341346736,"score_gpt":0.25668197319355296,"score_spread":0.23252302585220622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241234484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016308343,0.00007468508,0.98610044,0.0048648547,0.0026052194,0.0012254639,0.00022056559,0.0013885889,0.0018893239],"genre_scores_gemma":[0.8944251,0.00004161219,0.102637805,0.0011111231,0.00018398734,0.00060362864,0.00038180721,0.000048233895,0.00056671747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950593,0.00037709437,0.0012520831,0.0009902095,0.0016258474,0.00069544866],"domain_scores_gemma":[0.99592704,0.00040917334,0.0005383469,0.00080309814,0.0017532222,0.0005690947],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058617175,0.0006187726,0.00041350984,0.0013984287,0.0014291642,0.0003567859,0.0008255632,0.000371802,0.000054477532],"category_scores_gemma":[0.000023729865,0.0007008987,0.00033197124,0.0026189056,0.00021231639,0.0058016027,0.000005114145,0.0007750444,0.0020641596],"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":[0.00010911451,0.0011706426,0.000006514594,0.000023848683,0.00008041338,0.0000030723859,0.00065801595,0.88271415,0.00008069309,0.0049888953,0.017214859,0.0929498],"study_design_scores_gemma":[0.001919154,0.00069981726,0.0012879728,0.000061731735,0.000058491227,0.00015139651,0.00021745395,0.9788689,0.00071866874,0.010367588,0.004873897,0.00077494263],"about_ca_topic_score_codex":0.00012568834,"about_ca_topic_score_gemma":0.000011548962,"teacher_disagreement_score":0.89279425,"about_ca_system_score_codex":0.0014809017,"about_ca_system_score_gemma":0.00082758913,"threshold_uncertainty_score":0.99987084},"labels":[],"label_agreement":null},{"id":"W4241360849","doi":"10.3765/salt.v0i0.2478","title":"Classifiers and Number Marking","year":2015,"lang":"en","type":"article","venue":"Proceedings from Semantics and Linguistic Theory","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Natural language processing; Mathematics","score_opus":0.01844661278574656,"score_gpt":0.24456928804325934,"score_spread":0.22612267525751278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241360849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33925465,0.0019454042,0.5784163,0.0024286348,0.0009873001,0.00033518276,0.000016843494,0.00042143284,0.07619424],"genre_scores_gemma":[0.9395058,0.000064516535,0.059731685,0.00014427313,0.00029941468,0.000007976906,0.0000020057623,0.000008301287,0.0002360292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99926555,0.000007401793,0.00013459568,0.00030237244,0.00013086596,0.00015923598],"domain_scores_gemma":[0.9994019,0.00011204054,0.00006475369,0.00009830637,0.00015462918,0.00016834012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000368815,0.0001049867,0.000113018956,0.000031348125,0.00015114462,0.00019432999,0.00019666759,0.000048943017,0.0000043238715],"category_scores_gemma":[0.0002915103,0.000096846656,0.000015165219,0.00011298024,0.000100108526,0.0001038575,0.000209881,0.00010647095,0.000010585988],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036561373,0.000017310003,0.004440276,0.00000928491,0.000010987612,0.0000024465337,0.0017353117,2.2592454e-7,0.000060985847,0.98993754,0.00034934148,0.0034326075],"study_design_scores_gemma":[0.00023979202,0.0000193748,0.0036492175,0.00003413154,0.000020663472,0.000026629787,0.0004758736,0.040105533,0.000047643738,0.9394848,0.015723474,0.00017290123],"about_ca_topic_score_codex":0.000020923266,"about_ca_topic_score_gemma":4.2783518e-7,"teacher_disagreement_score":0.60025114,"about_ca_system_score_codex":0.000013468705,"about_ca_system_score_gemma":0.000028025652,"threshold_uncertainty_score":0.3949292},"labels":[],"label_agreement":null},{"id":"W4241477819","doi":"10.1109/tevc.2021.3079536","title":"IEEE Transactions on Evolutionary Computation Society Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Southern University of Science and Technology; Universidad de Granada; Inyuvesi Yakwazulu-Natali; Swinburne University of Technology; University of Warwick; Universidad del Atlántico; Università degli Studi di Milano-Bicocca; Università degli Studi di Milano; St. Francis Xavier University; University of Essex; Florida Atlantic University","keywords":"Evolutionary computation; Computer science; Computation; Evolutionary algorithm; Human-based evolutionary computation; Interactive evolutionary computation; Artificial intelligence; Theoretical computer science; Evolutionary programming; Algorithm","score_opus":0.014148062668026515,"score_gpt":0.24466855154596032,"score_spread":0.2305204888779338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241477819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002301826,0.00013612793,0.988905,0.0034153603,0.0023801874,0.0007302609,0.000278281,0.0010150118,0.00083790295],"genre_scores_gemma":[0.8167209,0.0001636517,0.18024245,0.0013920157,0.00016531002,0.00033989648,0.00031183645,0.00004423742,0.00061972573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99561185,0.00031612715,0.0010813545,0.0010287992,0.0012877441,0.0006741491],"domain_scores_gemma":[0.99694043,0.0005369766,0.00037386199,0.00072970294,0.0010976678,0.00032138478],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030835805,0.0005836613,0.00041781526,0.0005288767,0.0021009569,0.0002713338,0.0005664876,0.00036948975,0.00011243488],"category_scores_gemma":[0.000008258598,0.00068844703,0.0006267128,0.0023530337,0.00020218878,0.0034715438,0.0000053333174,0.0008532559,0.00092421274],"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":[0.000045398556,0.0012369144,0.0000043277087,0.00004389381,0.00014253032,0.0000085350875,0.0006104744,0.9085526,0.0004420712,0.0029738918,0.007982183,0.077957205],"study_design_scores_gemma":[0.0014807337,0.00033253068,0.0017348942,0.00009305991,0.00007574249,0.00024654163,0.00028095892,0.98192936,0.0028482303,0.0071943407,0.003028721,0.00075487694],"about_ca_topic_score_codex":0.000054476273,"about_ca_topic_score_gemma":0.000012070337,"teacher_disagreement_score":0.81441903,"about_ca_system_score_codex":0.0010161529,"about_ca_system_score_gemma":0.00082166435,"threshold_uncertainty_score":0.9998537},"labels":[],"label_agreement":null},{"id":"W4244080053","doi":"10.1007/978-1-4939-7131-2_100361","title":"Evolving Networks or Graphs","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Calgary","funders":"","keywords":"Computer science","score_opus":0.02050179703952979,"score_gpt":0.23709873838588766,"score_spread":0.21659694134635787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244080053","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.4073272e-7,0.0002507299,0.51179725,0.00019884466,0.00019367742,0.00009547216,0.000001908938,0.00017980133,0.4872822],"genre_scores_gemma":[0.00012121726,0.00018060555,0.11912711,0.00038049938,0.0004393779,0.000016412889,0.000012144024,0.000019595369,0.87970304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989201,0.000004562906,0.00021519234,0.00046531358,0.00019321842,0.00020164734],"domain_scores_gemma":[0.99880314,0.000067753055,0.000103572325,0.00078772876,0.00014300251,0.0000948041],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000092634604,0.00020227538,0.00016542923,0.00009136118,0.00020172882,0.000111633526,0.0009091083,0.00019941616,0.003168574],"category_scores_gemma":[0.000003750379,0.00015593978,0.00010755968,0.00007609936,0.00008540791,0.00021341162,0.00034319772,0.00019685793,0.00068984623],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.6937066e-7,0.00000600168,7.418668e-7,0.0000020780576,0.000014693609,0.0000040290793,0.000012310512,0.000013492737,2.824798e-7,0.87416035,0.11951411,0.0062714475],"study_design_scores_gemma":[0.000063211664,0.000053860313,0.000031709977,0.000044969107,0.000009463734,0.000029693902,0.0000013574123,0.14519861,0.0000010207549,0.28517625,0.56908286,0.00030700682],"about_ca_topic_score_codex":0.000006502961,"about_ca_topic_score_gemma":0.000016736267,"teacher_disagreement_score":0.58898413,"about_ca_system_score_codex":0.000032911827,"about_ca_system_score_gemma":0.00007988887,"threshold_uncertainty_score":0.99774265},"labels":[],"label_agreement":null},{"id":"W4246177449","doi":"10.1109/ijcnn.2006.1716100","title":"Opposition-Based Q(λ) Algorithm","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Opposition (politics); Reinforcement learning; Computer science; Lambda; Reinforcement; Artificial intelligence; Algorithm; Theoretical computer science; Engineering; Political science; Law; Physics; Structural engineering","score_opus":0.030114229558463834,"score_gpt":0.25399593957540195,"score_spread":0.22388171001693813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246177449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016128954,0.000110206085,0.82693756,0.09186126,0.002702842,0.00089921313,0.00006160646,0.0008904346,0.0604079],"genre_scores_gemma":[0.945357,0.000019429628,0.04764436,0.0029681197,0.0021182883,0.00018657108,0.000040621682,0.00002042599,0.0016451284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812967,0.000020289473,0.00038175672,0.00047466386,0.0006047906,0.00038883855],"domain_scores_gemma":[0.99888015,0.000083394196,0.00022578714,0.0002449518,0.000488562,0.00007715015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002605692,0.0002372481,0.00015793247,0.00009542852,0.00040776999,0.00044395664,0.0013308675,0.00006838709,0.00006667419],"category_scores_gemma":[0.0000072511352,0.00018062322,0.00012165419,0.00040451254,0.00012618116,0.00044106515,0.00010539498,0.0003055568,0.00015120834],"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":[0.000013786578,0.00022982623,0.00015199388,0.0000046343653,0.00002530801,0.000008065711,0.000037001933,0.019406777,0.0019235893,0.84808016,0.09378171,0.036337163],"study_design_scores_gemma":[0.000275898,0.0000759263,0.0019178353,0.00004586487,0.0000066780344,0.00003559818,0.0000079704205,0.93878967,0.0011542828,0.050954703,0.006502491,0.00023306714],"about_ca_topic_score_codex":0.00007423906,"about_ca_topic_score_gemma":0.0000048301545,"teacher_disagreement_score":0.9292281,"about_ca_system_score_codex":0.00009183737,"about_ca_system_score_gemma":0.000069917565,"threshold_uncertainty_score":0.7365601},"labels":[],"label_agreement":null},{"id":"W4246584857","doi":"10.1109/ijcnn.2006.1716072","title":"Aggregation of Reinforcement Learning Algorithms","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Learning classifier system; Instance-based learning; Machine learning; Robustness (evolution); Robot learning; Algorithm; Online machine learning; Unsupervised learning; Computational learning theory; Proactive learning; Robot; Mobile robot","score_opus":0.031508803972247715,"score_gpt":0.25756897615265123,"score_spread":0.22606017218040353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246584857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17516537,0.0001640201,0.66645783,0.03264513,0.0028313263,0.0013757247,0.000012233576,0.00071696367,0.12063142],"genre_scores_gemma":[0.99107695,0.00004104628,0.005392569,0.00024739062,0.00074491964,0.00006174168,0.000015398295,0.000009640066,0.0024103487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983744,0.000017244769,0.00045506866,0.00031749165,0.0005681744,0.0002676395],"domain_scores_gemma":[0.9988362,0.000057094905,0.00039680462,0.0001633344,0.000504512,0.000042022482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030334716,0.00017209638,0.00015279082,0.000086970205,0.00024420803,0.00015710978,0.00086636026,0.000051461888,0.000038684317],"category_scores_gemma":[0.000017698543,0.00013176308,0.000091130474,0.00034712572,0.00009887029,0.0004268622,0.00012784656,0.00027104196,0.000038085018],"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":[0.0000152442235,0.000072006405,0.0003722174,0.0000064543246,0.000022680551,0.0000013255088,0.00010432679,0.18468256,0.0023641612,0.7891507,0.0100264065,0.013181923],"study_design_scores_gemma":[0.00024512742,0.00012290511,0.0029315527,0.00007349474,0.0000065120407,0.000022358894,0.000025230735,0.96156293,0.0030950438,0.028723914,0.0030232312,0.00016772134],"about_ca_topic_score_codex":0.00008951294,"about_ca_topic_score_gemma":0.0000030506374,"teacher_disagreement_score":0.8159116,"about_ca_system_score_codex":0.00006412333,"about_ca_system_score_gemma":0.00003651695,"threshold_uncertainty_score":0.5373143},"labels":[],"label_agreement":null},{"id":"W4247167517","doi":"10.1109/ahs.2015.7231148","title":"Preface","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.03523017411762691,"score_gpt":0.26367665345040087,"score_spread":0.22844647933277396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247167517","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006478419,0.000031301213,0.91275746,0.0029643073,0.00004531763,0.000025528245,1.228938e-7,0.0001256641,0.08340247],"genre_scores_gemma":[0.30274752,0.0000016231533,0.6851567,0.00039087218,0.000047994203,0.0000145978875,6.7962463e-7,0.000001539473,0.011638474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997747,0.00000398778,0.000031591375,0.00007574866,0.00006406431,0.00004990906],"domain_scores_gemma":[0.9997241,0.00000572458,0.0000061734377,0.00017769587,0.000030331083,0.00005598818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049107446,0.000018567582,0.000016726473,0.000008864905,0.000022351915,0.000019746214,0.00022810056,0.0000074642267,0.000007524713],"category_scores_gemma":[0.0000038516323,0.000014989186,0.000006944233,0.00010556463,0.000006198091,0.00016247667,0.000066455876,0.0000152203975,0.0003668203],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.1682585e-8,0.000018201243,0.00008552355,1.5393485e-7,6.9535287e-7,2.919084e-7,0.00006434282,0.00007356843,0.000027434548,0.9577706,0.035497457,0.006461698],"study_design_scores_gemma":[0.00020457219,0.00003639562,0.0033748576,0.0000010020724,7.1367606e-7,0.000017776236,0.000041622097,0.29650038,0.00068671186,0.13023336,0.56878537,0.00011726227],"about_ca_topic_score_codex":0.000009494251,"about_ca_topic_score_gemma":7.206232e-7,"teacher_disagreement_score":0.8275372,"about_ca_system_score_codex":0.000008201978,"about_ca_system_score_gemma":0.000028896498,"threshold_uncertainty_score":0.47148556},"labels":[],"label_agreement":null},{"id":"W4247884300","doi":"10.1109/tevc.2015.2455356","title":"IEEE Transactions on Evolutionary Computation publication information","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Guelph; Dalhousie University","funders":"","keywords":"Evolutionary computation; Computer science; Computation; Evolutionary algorithm; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.024158947341346736,"score_gpt":0.25668197319355296,"score_spread":0.23252302585220622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247884300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016308343,0.00007468508,0.98610044,0.0048648547,0.0026052194,0.0012254639,0.00022056559,0.0013885889,0.0018893239],"genre_scores_gemma":[0.8944251,0.00004161219,0.102637805,0.0011111231,0.00018398734,0.00060362864,0.00038180721,0.000048233895,0.00056671747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950593,0.00037709437,0.0012520831,0.0009902095,0.0016258474,0.00069544866],"domain_scores_gemma":[0.99592704,0.00040917334,0.0005383469,0.00080309814,0.0017532222,0.0005690947],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058617175,0.0006187726,0.00041350984,0.0013984287,0.0014291642,0.0003567859,0.0008255632,0.000371802,0.000054477532],"category_scores_gemma":[0.000023729865,0.0007008987,0.00033197124,0.0026189056,0.00021231639,0.0058016027,0.000005114145,0.0007750444,0.0020641596],"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":[0.00010911451,0.0011706426,0.000006514594,0.000023848683,0.00008041338,0.0000030723859,0.00065801595,0.88271415,0.00008069309,0.0049888953,0.017214859,0.0929498],"study_design_scores_gemma":[0.001919154,0.00069981726,0.0012879728,0.000061731735,0.000058491227,0.00015139651,0.00021745395,0.9788689,0.00071866874,0.010367588,0.004873897,0.00077494263],"about_ca_topic_score_codex":0.00012568834,"about_ca_topic_score_gemma":0.000011548962,"teacher_disagreement_score":0.89279425,"about_ca_system_score_codex":0.0014809017,"about_ca_system_score_gemma":0.00082758913,"threshold_uncertainty_score":0.99987084},"labels":[],"label_agreement":null},{"id":"W4248338987","doi":"10.1093/obo/9780199941728-0122","title":"Evolutionary Computation","year":2019,"lang":"en","type":"reference-entry","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Evolutionary computation; Benchmark (surveying); Evolutionary algorithm; Heuristic; Selection (genetic algorithm); Computation; Interactive evolutionary computation; Evolution strategy; Evolutionary programming; Artificial intelligence; Machine learning; Theoretical computer science; Algorithm","score_opus":0.022525941804213825,"score_gpt":0.266930267438085,"score_spread":0.24440432563387118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248338987","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000022231027,0.0017616801,0.57015735,0.00094883254,0.0010114147,0.00022732081,0.000033919532,0.00025134726,0.4256059],"genre_scores_gemma":[0.00073574216,0.009319535,0.36080587,0.0005723393,0.00096269243,0.00012302333,0.0014009542,0.000035457204,0.6260444],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983053,0.000044704415,0.00030164194,0.0006308742,0.0004473366,0.00027014542],"domain_scores_gemma":[0.99879265,0.00012160222,0.00016327655,0.0006528379,0.00018150585,0.00008814134],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008076399,0.00023974766,0.00025496667,0.00017669276,0.00016549372,0.000107365246,0.0009921732,0.00027071984,0.0001302153],"category_scores_gemma":[0.000007753429,0.00022089524,0.00013091486,0.00041276426,0.000034508485,0.0003681732,0.00039046985,0.00034767637,0.0025508802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.6341132e-7,0.000059436123,0.000012814447,0.00003570251,0.000017631934,0.0000014236302,0.000012965621,0.0001669641,1.6075626e-7,0.10647608,0.82956934,0.063647084],"study_design_scores_gemma":[0.0001473977,0.000035780202,0.0009971285,0.00007605137,0.000009877403,0.00002742283,0.0000062690597,0.14484042,0.0000010708487,0.019242983,0.8342787,0.00033691843],"about_ca_topic_score_codex":0.00003825801,"about_ca_topic_score_gemma":0.0000015792633,"teacher_disagreement_score":0.2093515,"about_ca_system_score_codex":0.00015807987,"about_ca_system_score_gemma":0.00050072675,"threshold_uncertainty_score":0.99822575},"labels":[],"label_agreement":null},{"id":"W4248503900","doi":"10.1007/978-0-387-39940-9_2601","title":"Evolutionary Computation","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Evolutionary Algorithms and Applications","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 Waterloo","funders":"","keywords":"Computer science","score_opus":0.015446435704062695,"score_gpt":0.24117460025809984,"score_spread":0.22572816455403713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248503900","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001921943,0.00410524,0.31859112,0.0002410094,0.00078283343,0.0005418642,0.0006241865,0.0001769418,0.67493486],"genre_scores_gemma":[0.0013543355,0.004343213,0.16076557,0.00009885517,0.0019690155,0.00011178464,0.004300057,0.00008704743,0.8269701],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977699,0.000035799145,0.0007299369,0.0006199659,0.0006222086,0.00022220226],"domain_scores_gemma":[0.9978831,0.00013261315,0.00056467945,0.0010279766,0.0002597488,0.00013187199],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022490247,0.0003218952,0.00043428704,0.0002529129,0.00013509105,0.000035086592,0.00084643636,0.0001995233,0.000029009267],"category_scores_gemma":[0.000015154289,0.0003334983,0.00013984491,0.000119359516,0.000075977216,0.00050101115,0.00024937032,0.0002773607,0.00027797924],"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":[0.0000019026935,0.00005951776,0.000004085589,0.00010283904,0.000036311016,0.000017229433,0.000040645107,0.00044675116,0.000010302332,0.9135952,0.070814684,0.01487056],"study_design_scores_gemma":[0.00020262027,0.00007610399,0.00012319177,0.00038285856,0.000033383916,0.00006938352,0.000007002449,0.027294954,0.0000016860254,0.015470693,0.9558996,0.00043852933],"about_ca_topic_score_codex":0.00007484382,"about_ca_topic_score_gemma":0.0000027732954,"teacher_disagreement_score":0.89812446,"about_ca_system_score_codex":0.0001001423,"about_ca_system_score_gemma":0.00027256523,"threshold_uncertainty_score":0.9999117},"labels":[],"label_agreement":null},{"id":"W4249884558","doi":"10.32920/ryerson.14645649","title":"On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Evolvability; Representation (politics); Computer science; Rank (graph theory); Genetic programming; Formicoidea; Ant colony; Artificial intelligence; Theoretical computer science; Ant colony optimization algorithms; Mathematical optimization; Mathematics; Biology; Ecology; Evolutionary biology; Aculeata","score_opus":0.06438474259163568,"score_gpt":0.30902303631426026,"score_spread":0.24463829372262458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249884558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07206948,0.00025281444,0.9216178,0.0044878535,0.00021821925,0.000664095,0.000007560145,0.00008534719,0.00059680396],"genre_scores_gemma":[0.36533204,0.000016506208,0.63407576,0.00016431663,0.000037699578,0.00028617308,0.00000785265,0.000005556588,0.00007413742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792945,0.0005283,0.0003803618,0.00068832707,0.00024849808,0.00022508363],"domain_scores_gemma":[0.99677193,0.0010691896,0.00020397406,0.0016728103,0.00022272373,0.000059355967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086158124,0.00017651978,0.00030653813,0.000051348685,0.00013195419,0.000085375235,0.0013063357,0.00008236701,0.00006744878],"category_scores_gemma":[0.00020452491,0.00012440982,0.00018548431,0.00024350229,0.00016167844,0.000039307542,0.0013461726,0.00039356708,0.0000072244193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001016892,0.0014959105,0.0009361982,0.00026643774,0.00025898713,0.000045106695,0.0013425073,0.010277257,0.0009823884,0.8020978,0.0025061904,0.17978106],"study_design_scores_gemma":[0.00024676445,0.00033068523,0.031775683,0.0001441761,0.000069331436,0.00011791143,0.00027495937,0.64895886,0.0063632946,0.30803704,0.0030071316,0.00067415746],"about_ca_topic_score_codex":0.00022615933,"about_ca_topic_score_gemma":0.000024629442,"teacher_disagreement_score":0.6386816,"about_ca_system_score_codex":0.00005965528,"about_ca_system_score_gemma":0.00034773472,"threshold_uncertainty_score":0.5073285},"labels":[],"label_agreement":null},{"id":"W4251062538","doi":"10.1109/tevc.2015.2480234","title":"IEEE Transactions on Evolutionary Computation publication information","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Guelph; Dalhousie University","funders":"","keywords":"Computer science; Evolutionary computation; Computation; Evolutionary algorithm; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.024158947341346736,"score_gpt":0.25668197319355296,"score_spread":0.23252302585220622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251062538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016308343,0.00007468508,0.98610044,0.0048648547,0.0026052194,0.0012254639,0.00022056559,0.0013885889,0.0018893239],"genre_scores_gemma":[0.8944251,0.00004161219,0.102637805,0.0011111231,0.00018398734,0.00060362864,0.00038180721,0.000048233895,0.00056671747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950593,0.00037709437,0.0012520831,0.0009902095,0.0016258474,0.00069544866],"domain_scores_gemma":[0.99592704,0.00040917334,0.0005383469,0.00080309814,0.0017532222,0.0005690947],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058617175,0.0006187726,0.00041350984,0.0013984287,0.0014291642,0.0003567859,0.0008255632,0.000371802,0.000054477532],"category_scores_gemma":[0.000023729865,0.0007008987,0.00033197124,0.0026189056,0.00021231639,0.0058016027,0.000005114145,0.0007750444,0.0020641596],"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":[0.00010911451,0.0011706426,0.000006514594,0.000023848683,0.00008041338,0.0000030723859,0.00065801595,0.88271415,0.00008069309,0.0049888953,0.017214859,0.0929498],"study_design_scores_gemma":[0.001919154,0.00069981726,0.0012879728,0.000061731735,0.000058491227,0.00015139651,0.00021745395,0.9788689,0.00071866874,0.010367588,0.004873897,0.00077494263],"about_ca_topic_score_codex":0.00012568834,"about_ca_topic_score_gemma":0.000011548962,"teacher_disagreement_score":0.89279425,"about_ca_system_score_codex":0.0014809017,"about_ca_system_score_gemma":0.00082758913,"threshold_uncertainty_score":0.99987084},"labels":[],"label_agreement":null},{"id":"W4251507379","doi":"10.32920/ryerson.14643771.v1","title":"Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Evolvability; Computer science; Genetic programming; Population; Transposition (logic); Symbolic regression; Tree (set theory); Representation (politics); Encoding (memory); Algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Biology; Genetics","score_opus":0.036910055541007535,"score_gpt":0.29822233896101363,"score_spread":0.2613122834200061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251507379","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012749007,0.00037642597,0.9851392,0.00030493128,0.000109790475,0.0011072847,0.000016650269,0.00009801469,0.00009871791],"genre_scores_gemma":[0.24389707,0.000021853131,0.7551605,0.000015490657,0.000039891227,0.00066145574,0.00010556326,0.000008825219,0.00008932547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836165,0.00006709139,0.0004185139,0.000775957,0.00020040783,0.00017639522],"domain_scores_gemma":[0.99816954,0.00021746555,0.00030888402,0.0007714983,0.00046065095,0.000071955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024567102,0.00017031902,0.00024209231,0.00008771732,0.00017606693,0.000175503,0.00040958828,0.00015120395,0.000006168176],"category_scores_gemma":[0.00014276759,0.00016407267,0.000114224094,0.00018081981,0.00007474413,0.00033180002,0.0010460434,0.00015536846,2.385383e-7],"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":[0.0000629628,0.0022753077,0.0061644204,0.0024643869,0.00036408607,0.000007673654,0.012550114,0.03699247,0.10518309,0.13693528,0.0008896663,0.69611055],"study_design_scores_gemma":[0.00025687434,0.00007369613,0.0033263727,0.000096697586,0.00003346907,0.000005455045,0.00037913243,0.9652965,0.014966781,0.015213042,0.00008191396,0.0002700455],"about_ca_topic_score_codex":0.00020199559,"about_ca_topic_score_gemma":0.000016395126,"teacher_disagreement_score":0.9283041,"about_ca_system_score_codex":0.00003816716,"about_ca_system_score_gemma":0.00019144011,"threshold_uncertainty_score":0.66906893},"labels":[],"label_agreement":null},{"id":"W4251697033","doi":"10.1080/17513472.2011.574870","title":"Cover","year":2011,"lang":"en","type":"article","venue":"Journal of Mathematics and the Arts","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cover (algebra); Variation (astronomy); Painting; Computer science; Cluster analysis; Pheromone; Art history; Artificial intelligence; Art; Ecology; Engineering; Biology","score_opus":0.029029071602069204,"score_gpt":0.23520470107581576,"score_spread":0.20617562947374657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251697033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019639691,0.0002970587,0.963245,0.0018921177,0.000080836995,0.00004852283,2.1668423e-7,0.000005606113,0.014790962],"genre_scores_gemma":[0.60500616,0.00011398174,0.3944084,0.00018118283,0.000057179364,0.0000015490558,1.8685803e-8,0.0000023583018,0.0002291734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997002,0.0000065910835,0.00014160115,0.00002638472,0.00008465541,0.000040584244],"domain_scores_gemma":[0.99960434,0.000066764514,0.00013695615,0.0001182749,0.000047866764,0.000025802039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035727292,0.000027102236,0.0000674263,0.000016649788,0.000057747948,0.000022998332,0.0002130942,0.000007958845,0.000012588056],"category_scores_gemma":[0.00001496703,0.000013717824,0.000032812113,0.000041749874,0.000041176423,0.000120690805,0.000049005135,0.0000437715,0.000012389224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000124943,0.000056121415,0.0000062362565,0.0000027557808,0.000010048403,0.0000020288235,0.0017918433,0.000002043274,0.000014708905,0.9959044,0.0010361444,0.0011724278],"study_design_scores_gemma":[0.00050625,0.000038944807,0.00064332027,0.0000188151,0.000012532482,0.00043086312,0.00011029123,0.029527202,0.00016270405,0.96334255,0.0051653236,0.000041218358],"about_ca_topic_score_codex":9.0698825e-7,"about_ca_topic_score_gemma":8.147623e-8,"teacher_disagreement_score":0.5853665,"about_ca_system_score_codex":0.000002229562,"about_ca_system_score_gemma":0.000010412401,"threshold_uncertainty_score":0.055939663},"labels":[],"label_agreement":null},{"id":"W4252262041","doi":"10.1007/978-3-662-44185-5_100372","title":"Evolutionary Tree","year":2015,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Astrobiology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"","keywords":"Tree (set theory); Computer science; Mathematics; Combinatorics","score_opus":0.016715210855324823,"score_gpt":0.235683127992004,"score_spread":0.21896791713667918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252262041","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008180943,0.0019595693,0.056073017,0.0006362933,0.0005767923,0.00020664088,0.000098042074,0.000106767184,0.9403347],"genre_scores_gemma":[0.00039275037,0.0007933203,0.15886027,0.00006201673,0.0007212179,0.000041286752,0.00030732068,0.000034146917,0.8387877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863,0.000024135741,0.00043442316,0.00049550063,0.00017336142,0.00024257995],"domain_scores_gemma":[0.9984924,0.000092482376,0.0003160134,0.00074335025,0.00023943452,0.00011628398],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013563673,0.00025993268,0.00039207112,0.00016436985,0.00005915225,0.0000054627135,0.0010090201,0.00033648452,0.00019434608],"category_scores_gemma":[0.000017140763,0.0002523448,0.00014665493,0.000058718386,0.0002243811,0.00012798539,0.00041453072,0.00029417552,0.0003864216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030469087,0.000031651285,0.00002591932,0.000006486588,0.0000359569,0.0000046395335,0.00003106697,0.00003356952,0.000014192303,0.884492,0.09792826,0.017393256],"study_design_scores_gemma":[0.00016129692,0.00016918665,0.00033816136,0.000019604719,0.000014936555,0.00004059759,0.0000023768177,0.00025884848,0.0000017729876,0.1280311,0.87075025,0.00021189767],"about_ca_topic_score_codex":0.000007132605,"about_ca_topic_score_gemma":0.000005071727,"teacher_disagreement_score":0.77282196,"about_ca_system_score_codex":0.00007804051,"about_ca_system_score_gemma":0.00038836294,"threshold_uncertainty_score":0.9999929},"labels":[],"label_agreement":null},{"id":"W4252545596","doi":"10.32920/ryerson.14643771","title":"Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene expression programming; Evolvability; Computer science; Genetic programming; Population; Transposition (logic); Symbolic regression; Representation (politics); Tree (set theory); Encoding (memory); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Biology; Genetics","score_opus":0.036910055541007535,"score_gpt":0.29822233896101363,"score_spread":0.2613122834200061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252545596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012749007,0.00037642597,0.9851392,0.00030493128,0.000109790475,0.0011072847,0.000016650269,0.00009801469,0.00009871791],"genre_scores_gemma":[0.24389707,0.000021853131,0.7551605,0.000015490657,0.000039891227,0.00066145574,0.00010556326,0.000008825219,0.00008932547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836165,0.00006709139,0.0004185139,0.000775957,0.00020040783,0.00017639522],"domain_scores_gemma":[0.99816954,0.00021746555,0.00030888402,0.0007714983,0.00046065095,0.000071955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024567102,0.00017031902,0.00024209231,0.00008771732,0.00017606693,0.000175503,0.00040958828,0.00015120395,0.000006168176],"category_scores_gemma":[0.00014276759,0.00016407267,0.000114224094,0.00018081981,0.00007474413,0.00033180002,0.0010460434,0.00015536846,2.385383e-7],"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":[0.0000629628,0.0022753077,0.0061644204,0.0024643869,0.00036408607,0.000007673654,0.012550114,0.03699247,0.10518309,0.13693528,0.0008896663,0.69611055],"study_design_scores_gemma":[0.00025687434,0.00007369613,0.0033263727,0.000096697586,0.00003346907,0.000005455045,0.00037913243,0.9652965,0.014966781,0.015213042,0.00008191396,0.0002700455],"about_ca_topic_score_codex":0.00020199559,"about_ca_topic_score_gemma":0.000016395126,"teacher_disagreement_score":0.9283041,"about_ca_system_score_codex":0.00003816716,"about_ca_system_score_gemma":0.00019144011,"threshold_uncertainty_score":0.66906893},"labels":[],"label_agreement":null},{"id":"W4253061157","doi":"10.4018/978-1-4666-2476-4.ch018","title":"Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Position (finance); Trajectory; Computer science; Dimension (graph theory); Particle (ecology); Mathematical optimization; Control theory (sociology); Algorithm; Mathematics; Artificial intelligence; Control (management); Physics","score_opus":0.016899147478968896,"score_gpt":0.25724579025161404,"score_spread":0.24034664277264514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253061157","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012955765,0.0070431153,0.27454036,0.00033005446,0.0002937868,0.001646497,0.005441053,0.0004824396,0.69726694],"genre_scores_gemma":[0.9368136,0.000021288362,0.034960106,0.00018726617,0.0003061888,0.00014155544,0.00006218918,0.000038069644,0.027469723],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99915034,0.0000080772,0.00023514658,0.00029125175,0.0001469013,0.00016827854],"domain_scores_gemma":[0.9992417,0.00010173654,0.00015842715,0.00024456697,0.00016334264,0.00009022215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007916809,0.00017096142,0.00032018885,0.00005195679,0.00009384498,0.000028501148,0.00022426787,0.00010504692,0.000008969483],"category_scores_gemma":[0.000009374986,0.00016046596,0.00016841952,0.000063199994,0.00014109132,0.00008571114,0.00008767726,0.000054890046,0.00001268262],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036390309,0.000014517568,0.00010999265,0.000008736177,0.00041446765,5.8792216e-7,0.00009757141,0.0000039407028,0.00003302242,0.9907171,0.000042814638,0.008553591],"study_design_scores_gemma":[0.0005831506,0.00022426558,0.0052494695,0.00008762344,0.0029716797,0.000016751119,0.000014924403,0.038830373,0.00036601676,0.94925004,0.0016480507,0.0007576766],"about_ca_topic_score_codex":0.000063498504,"about_ca_topic_score_gemma":0.000046778605,"teacher_disagreement_score":0.92385787,"about_ca_system_score_codex":0.000031167692,"about_ca_system_score_gemma":0.00005665217,"threshold_uncertainty_score":0.65436125},"labels":[],"label_agreement":null},{"id":"W4253480471","doi":"10.22215/etd/2009-09227","title":"Learning the neuron functions within neural networks based on genetic programming","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"Universitat Politècnica de Catalunya","keywords":"Genetic programming; Artificial neural network; Computer science; Artificial intelligence; Humanities; Art","score_opus":0.009179212263049779,"score_gpt":0.23533304844879593,"score_spread":0.22615383618574617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253480471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005244618,0.00020408652,0.98236537,0.0020507704,0.0012335201,0.00092206756,9.693998e-7,0.00070995087,0.0072686514],"genre_scores_gemma":[0.8957379,0.000022820914,0.06605961,0.0018430123,0.0008929375,0.0006582636,0.0007106713,0.00007050007,0.034004275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984694,0.00010622547,0.00028481975,0.000529145,0.0003229803,0.0002874264],"domain_scores_gemma":[0.99890745,0.00013300634,0.00019989497,0.00058811787,0.000097806274,0.00007372574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014779139,0.00024610508,0.00013943177,0.000100255966,0.0008671147,0.00029980173,0.000733753,0.00013789184,0.000015395093],"category_scores_gemma":[0.000021166175,0.00017849199,0.0001243813,0.000608349,0.00002321338,0.000114343646,0.000027487751,0.00078788254,0.000030570987],"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":[0.000005483662,0.000077124016,0.000044941524,0.0000050069016,0.0000058902815,0.0000029987918,0.00009581797,0.7444712,0.0000050929266,0.002763449,0.0006942132,0.2518288],"study_design_scores_gemma":[0.00009057672,0.00023920993,0.018305019,0.000018068553,0.000017981958,0.0000050927797,0.000097264034,0.9761477,0.000003915306,0.00014485378,0.0047328775,0.00019744293],"about_ca_topic_score_codex":0.000039962066,"about_ca_topic_score_gemma":0.00009323254,"teacher_disagreement_score":0.9163058,"about_ca_system_score_codex":0.000033274173,"about_ca_system_score_gemma":0.000085783366,"threshold_uncertainty_score":0.7278692},"labels":[],"label_agreement":null},{"id":"W4253844258","doi":"10.1007/978-1-4614-6170-8_100652","title":"Evolving Graphs","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Calgary","funders":"","keywords":"Computer science","score_opus":0.013973330836175158,"score_gpt":0.21919849711277614,"score_spread":0.20522516627660098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253844258","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.946163e-8,0.00015359046,0.46216175,0.00040839202,0.0000830047,0.0000515736,0.0000012345168,0.00014272801,0.5369977],"genre_scores_gemma":[0.00038185797,0.000052832173,0.10801621,0.00035340607,0.00012352908,0.0000104367655,0.000008649907,0.000013461532,0.8910396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991684,0.0000034913132,0.00015932064,0.00035583094,0.00018508467,0.000127893],"domain_scores_gemma":[0.9990368,0.000046802033,0.00007674307,0.0006881346,0.00008132676,0.0000701963],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000078246754,0.00015012921,0.00013446534,0.00009192856,0.00012226387,0.00007043924,0.00072941685,0.00012048613,0.00042312863],"category_scores_gemma":[0.0000028849129,0.00013702898,0.000106303334,0.00003173515,0.00003620764,0.00009883086,0.00021392936,0.00015292142,0.0010175895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.670558e-8,0.000002376054,7.182614e-7,0.000001989753,0.0000066108087,8.4599947e-7,0.0000050745116,0.000002201624,0.0000025136994,0.9589395,0.030558532,0.010479605],"study_design_scores_gemma":[0.000025669435,0.000009906673,0.000026287918,0.000012896545,0.0000032062776,0.000007328747,1.8622066e-7,0.007909347,0.000002660829,0.43497443,0.55688655,0.00014154025],"about_ca_topic_score_codex":0.0000059291788,"about_ca_topic_score_gemma":0.000002826893,"teacher_disagreement_score":0.526328,"about_ca_system_score_codex":0.000022148497,"about_ca_system_score_gemma":0.00003879575,"threshold_uncertainty_score":0.9997602},"labels":[],"label_agreement":null},{"id":"W4254113190","doi":"10.1007/978-1-4939-7131-2_100360","title":"Evolving Networks","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.015515252700059667,"score_gpt":0.22466145872537727,"score_spread":0.2091462060253176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254113190","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.3636973e-8,0.0002599775,0.49809694,0.00020186635,0.0001372458,0.000053054293,8.515725e-7,0.0001304613,0.50111955],"genre_scores_gemma":[0.00011007703,0.000075509124,0.111641005,0.00030001358,0.00069803296,0.000008423545,0.000009752935,0.000014299886,0.8871429],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99917275,0.0000027982946,0.00015826388,0.00036487577,0.0001502763,0.0001510203],"domain_scores_gemma":[0.99902815,0.000036633744,0.00007639574,0.0006733164,0.00011597066,0.00006951012],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007291022,0.00014955593,0.000119440316,0.000052176838,0.00014251613,0.00008748508,0.00074158295,0.00015950596,0.0020094907],"category_scores_gemma":[0.0000020426887,0.00013712737,0.00007884548,0.000033117096,0.000055861845,0.00016767296,0.0003206118,0.00016099206,0.0012681996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.549821e-8,0.0000031504744,6.014205e-7,0.0000010065398,0.000007926918,0.0000013820725,0.000008125364,0.000018082037,2.7429198e-7,0.8742267,0.120141566,0.0055911154],"study_design_scores_gemma":[0.000029371045,0.00001849997,0.000021799098,0.000022565571,0.0000044791996,0.00001131585,4.7591251e-7,0.17346786,8.2984286e-7,0.1817307,0.6444972,0.00019489098],"about_ca_topic_score_codex":0.0000033580607,"about_ca_topic_score_gemma":0.000003504983,"teacher_disagreement_score":0.692496,"about_ca_system_score_codex":0.00003570241,"about_ca_system_score_gemma":0.000045719473,"threshold_uncertainty_score":0.99950945},"labels":[],"label_agreement":null},{"id":"W4281627470","doi":"10.5281/zenodo.6633719","title":"The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Benchmark (surveying); CHAOS (operating system); Cart; Pendulum; Computer science; Control (management); Inverted pendulum; Artificial intelligence; Control theory (sociology); Physics; Engineering; Mechanical engineering; Cartography; Geography; Nonlinear system; Operating system","score_opus":0.0195745055684784,"score_gpt":0.24370860537004582,"score_spread":0.22413409980156743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281627470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030207558,0.0033518854,0.87165076,0.013773734,0.0012147706,0.0079997955,0.0011174984,0.0047737295,0.06591025],"genre_scores_gemma":[0.99741465,0.000019093484,0.001007848,0.00006943577,0.00007431155,0.0000030471142,0.00015697647,0.00022961377,0.0010250035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986918,0.00027897675,0.00015178445,0.0003546845,0.00027145594,0.00025129388],"domain_scores_gemma":[0.99930155,0.00008479404,0.000081426806,0.00029242385,0.00014474257,0.00009504083],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006150839,0.00009185102,0.00008255806,0.00006936366,0.00840311,0.0005248699,0.0009028361,0.000017679287,0.00033225826],"category_scores_gemma":[0.00010287068,0.00008334768,0.000039333012,0.0002488257,0.00007981834,0.00012042144,0.0009930857,0.00020246566,0.00021885728],"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":[0.00018709792,0.001274268,0.000013524483,0.000085728294,0.00014338593,0.000019088566,0.0073126554,0.001663624,0.015215773,0.7330562,0.14107327,0.099955395],"study_design_scores_gemma":[0.0007994071,0.00053700124,0.000099248005,0.000005135231,0.000003709141,0.0000858545,0.0017003979,0.075504825,0.0001896906,0.000044641616,0.9209344,0.00009571623],"about_ca_topic_score_codex":0.000007968041,"about_ca_topic_score_gemma":9.224091e-8,"teacher_disagreement_score":0.96720713,"about_ca_system_score_codex":0.00016590307,"about_ca_system_score_gemma":0.0000047946264,"threshold_uncertainty_score":0.9928878},"labels":[],"label_agreement":null},{"id":"W4281745141","doi":"10.5281/zenodo.6632782","title":"The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Benchmark (surveying); Cart; CHAOS (operating system); Control theory (sociology); Pendulum; Computer science; Inverted pendulum; Double pendulum; Control (management); Artificial intelligence; Physics; Nonlinear system; Engineering; Geography","score_opus":0.0195745055684784,"score_gpt":0.24370860537004582,"score_spread":0.22413409980156743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281745141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030207558,0.0033518854,0.87165076,0.013773734,0.0012147706,0.0079997955,0.0011174984,0.0047737295,0.06591025],"genre_scores_gemma":[0.99741465,0.000019093484,0.001007848,0.00006943577,0.00007431155,0.0000030471142,0.00015697647,0.00022961377,0.0010250035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986918,0.00027897675,0.00015178445,0.0003546845,0.00027145594,0.00025129388],"domain_scores_gemma":[0.99930155,0.00008479404,0.000081426806,0.00029242385,0.00014474257,0.00009504083],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006150839,0.00009185102,0.00008255806,0.00006936366,0.00840311,0.0005248699,0.0009028361,0.000017679287,0.00033225826],"category_scores_gemma":[0.00010287068,0.00008334768,0.000039333012,0.0002488257,0.00007981834,0.00012042144,0.0009930857,0.00020246566,0.00021885728],"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":[0.00018709792,0.001274268,0.000013524483,0.000085728294,0.00014338593,0.000019088566,0.0073126554,0.001663624,0.015215773,0.7330562,0.14107327,0.099955395],"study_design_scores_gemma":[0.0007994071,0.00053700124,0.000099248005,0.000005135231,0.000003709141,0.0000858545,0.0017003979,0.075504825,0.0001896906,0.000044641616,0.9209344,0.00009571623],"about_ca_topic_score_codex":0.000007968041,"about_ca_topic_score_gemma":9.224091e-8,"teacher_disagreement_score":0.96720713,"about_ca_system_score_codex":0.00016590307,"about_ca_system_score_gemma":0.0000047946264,"threshold_uncertainty_score":0.9928878},"labels":[],"label_agreement":null},{"id":"W4281920676","doi":"10.1109/tevc.2022.3174737","title":"IEEE Transactions on Evolutionary Computation Publication Information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Southern University of Science and Technology; Universität Bielefeld; Xidian University; Guangdong University of Technology; Brock University; Universidad Veracruzana; Zhengzhou University; University of Hong Kong; Swinburne University of Technology; Griffith University; RMIT University; Nanyang Technological University; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional; Shenzhen University; Nanjing University; Dalhousie University; University of Melbourne; University of Sheffield; University of Exeter; South China University of Technology; City University of Hong Kong; Aberystwyth University; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Evolutionary computation; Computer science; Computation; Evolutionary algorithm; Theoretical computer science; Artificial intelligence; Algorithm","score_opus":0.014466550623449472,"score_gpt":0.24034917080551912,"score_spread":0.22588262018206964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281920676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019320218,0.00006794173,0.9856791,0.0052950773,0.002689208,0.0013818621,0.0005218994,0.0012816791,0.0011512233],"genre_scores_gemma":[0.94141585,0.00003771831,0.053916242,0.0015514224,0.0001243882,0.0016309766,0.0005864521,0.000051046358,0.0006859294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99465704,0.0005478765,0.0012336115,0.0010476097,0.0018186892,0.00069516426],"domain_scores_gemma":[0.9969767,0.0004909168,0.0005903397,0.0008006639,0.00082919054,0.00031222525],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005711836,0.0005742985,0.00038481655,0.0016081107,0.0041881283,0.00027027796,0.0010013087,0.00020764025,0.000294898],"category_scores_gemma":[0.0000103026405,0.00070556015,0.00039546075,0.003160414,0.00017017723,0.0040333024,0.00001016491,0.0011151643,0.0008439763],"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":[0.00009671837,0.0013484993,0.0000042512715,0.000020611116,0.00007587196,0.0000032434043,0.00049986254,0.8984676,0.00014116046,0.005093518,0.011243484,0.0830052],"study_design_scores_gemma":[0.0014660946,0.0008004088,0.0015142905,0.00002631271,0.000055225963,0.00021304021,0.000266176,0.9792404,0.0004364726,0.006986518,0.008235879,0.0007591506],"about_ca_topic_score_codex":0.00011038955,"about_ca_topic_score_gemma":0.000006642547,"teacher_disagreement_score":0.9394838,"about_ca_system_score_codex":0.0018580222,"about_ca_system_score_gemma":0.00061846816,"threshold_uncertainty_score":0.99993396},"labels":[],"label_agreement":null},{"id":"W4285101299","doi":"10.1109/aiiot54504.2022.9817370","title":"An Approach for Automatic Discovery of Rules Based on ECG Data Using Learning Classifier Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Classifier (UML); Artificial intelligence; Component (thermodynamics); Personalized medicine; Decision support system; Association rule learning; Data mining; Bioinformatics","score_opus":0.05134884789720748,"score_gpt":0.3044739133264933,"score_spread":0.2531250654292858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285101299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018099915,0.00022993972,0.97806907,0.00039878493,0.0012199816,0.00087519083,0.0005165018,0.00019899741,0.00039163567],"genre_scores_gemma":[0.8748832,0.0000024395156,0.12119634,0.00024124136,0.0002604284,0.00051285845,0.00061256206,0.000046006262,0.0022449305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974476,0.00032829557,0.0004558775,0.0008063299,0.00061483314,0.0003470537],"domain_scores_gemma":[0.9974745,0.00033858846,0.00035059117,0.0016659354,0.00008097169,0.00008940496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008350111,0.00021428513,0.0003348986,0.00031428307,0.0008769973,0.0002721927,0.0021276705,0.000037416903,0.000026359801],"category_scores_gemma":[0.000027941538,0.00021567584,0.00009272652,0.00076044066,0.000091236805,0.0006260668,0.0004564913,0.00039291932,0.000002472954],"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":[0.00002495067,0.0007372818,0.0009942099,0.00018698958,0.000058388378,0.0000057523675,0.00011663056,0.95772433,0.0024353405,0.027528016,0.0053429794,0.0048451493],"study_design_scores_gemma":[0.00043787947,0.00010996801,0.000366105,0.00004240035,0.000028100605,0.000007858667,0.00015706015,0.9919536,0.00012810665,0.00014058634,0.0063702078,0.0002581128],"about_ca_topic_score_codex":0.00008414604,"about_ca_topic_score_gemma":0.0000064591895,"teacher_disagreement_score":0.85687274,"about_ca_system_score_codex":0.00016520667,"about_ca_system_score_gemma":0.0002612029,"threshold_uncertainty_score":0.87950057},"labels":[],"label_agreement":null},{"id":"W4285217523","doi":"10.1007/978-3-031-79167-3","title":"Applying Reinforcement Learning on Real-World Data with Practical Examples in Python","year":2022,"lang":"en","type":"book","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Canadian Institute for Advanced Research","funders":"","keywords":"Python (programming language); Computer science; Reinforcement learning; Artificial intelligence; Machine learning; Programming language","score_opus":0.08124021256867234,"score_gpt":0.32675245957882326,"score_spread":0.24551224701015092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285217523","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000019342178,0.000017421056,0.30988836,0.0011382652,0.00005294175,0.0007333633,0.0000070731767,0.00021376905,0.68794686],"genre_scores_gemma":[0.00021561241,0.00016094014,0.16547297,0.00035150937,0.00016081397,0.00072736386,0.0010509033,0.00003635631,0.8318235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977723,0.00007100001,0.00032598688,0.0009033238,0.00064450287,0.00028293798],"domain_scores_gemma":[0.9975673,0.00047473735,0.000245806,0.0016034204,0.000031610758,0.00007711014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056452875,0.00022734943,0.00024739502,0.00027947177,0.0002903654,0.000121505036,0.0013115386,0.000048425052,0.00026359208],"category_scores_gemma":[0.000024206503,0.00019947794,0.00002683043,0.0004145055,0.00005437602,0.0004231311,0.0016328696,0.0009554287,0.000066600915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011176312,0.00006666385,0.000052520885,0.000016657603,0.000021107751,0.000047113896,0.000056769226,0.014031993,0.0000010636164,0.9276341,0.043899506,0.014161331],"study_design_scores_gemma":[0.00014778951,0.00015466021,0.00014901959,0.000053040956,0.000008119809,0.000013987646,0.00003404404,0.13108715,9.996108e-7,0.0015014072,0.8665898,0.00025997934],"about_ca_topic_score_codex":0.00044372675,"about_ca_topic_score_gemma":0.0006065067,"teacher_disagreement_score":0.9261327,"about_ca_system_score_codex":0.0003910666,"about_ca_system_score_gemma":0.0006671841,"threshold_uncertainty_score":0.8134475},"labels":[],"label_agreement":null},{"id":"W4285464742","doi":"10.32920/ryerson.14651808","title":"A Study on Financial Time Series Forecasting and Symbolic Regression by means of a Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Symbolic regression; Probabilistic logic; Genetic programming; Computer science; Hidden Markov model; Markov chain; Representation (politics); Stock market; Regression; Statistical model; Econometrics; Artificial intelligence; Machine learning; Mathematics; Statistics; Geography","score_opus":0.060633634333013824,"score_gpt":0.30307290876519427,"score_spread":0.24243927443218044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285464742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32514697,0.00023025094,0.67334557,0.00019260359,0.000057311467,0.0008759903,0.000012420505,0.000085623666,0.000053238404],"genre_scores_gemma":[0.3784974,0.000010239178,0.62100405,0.000015584095,0.00003645658,0.00033081364,0.000012960914,0.000013583034,0.00007888397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975979,0.00030531053,0.0005064168,0.0009942482,0.0002839461,0.00031221122],"domain_scores_gemma":[0.99847645,0.00023041957,0.00029427398,0.00071992533,0.00018325796,0.00009564836],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005491437,0.00030691086,0.0005463546,0.00012373913,0.00023381537,0.00013006643,0.00057827483,0.000106839005,0.0000024329681],"category_scores_gemma":[0.00022453189,0.00027366105,0.00009122567,0.00024342876,0.00010004322,0.00012955608,0.0013819014,0.00035863067,4.909261e-7],"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":[0.00011440998,0.0063671716,0.003667734,0.0020970097,0.00055394956,0.00041500365,0.02875811,0.3539592,0.0144825345,0.07344043,0.0013506737,0.51479375],"study_design_scores_gemma":[0.00021628491,0.0003408185,0.0007209846,0.00027899773,0.000057469486,0.00012018519,0.00018053723,0.9831994,0.0010418907,0.013478559,0.000018568688,0.00034627793],"about_ca_topic_score_codex":0.00006736723,"about_ca_topic_score_gemma":0.000015771915,"teacher_disagreement_score":0.6292402,"about_ca_system_score_codex":0.000056980578,"about_ca_system_score_gemma":0.0002919675,"threshold_uncertainty_score":0.99997157},"labels":[],"label_agreement":null},{"id":"W4285734686","doi":"10.1145/3512290.3528694","title":"Evolving transferable neural pruning functions","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Pruning; Computer science; Artificial intelligence; Machine learning; Inference; Artificial neural network; Deep learning; Process (computing); Function (biology); Genetic programming","score_opus":0.016475071863052915,"score_gpt":0.21564160838079635,"score_spread":0.19916653651774344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285734686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42655474,0.0010133655,0.5623542,0.0060923113,0.0004062774,0.0004973616,0.000022267586,0.00015597383,0.0029034733],"genre_scores_gemma":[0.96117985,0.00001429607,0.038187392,0.000087353816,0.00003092839,0.000088067834,0.0000032441205,0.0000055870382,0.00040326905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989334,0.000021231413,0.00024704039,0.0003021725,0.00032455224,0.00017163043],"domain_scores_gemma":[0.9993772,0.000049314436,0.00012916441,0.00010143851,0.00028967095,0.000053235824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014042688,0.00010545527,0.00010690701,0.00008351279,0.0011909914,0.000065648965,0.00054980896,0.000021303342,0.000037736292],"category_scores_gemma":[0.0000149027865,0.00009747002,0.000053321095,0.0005621782,0.00010406124,0.00033645704,0.00044733688,0.0001750312,0.0000017512903],"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":[0.000047001857,0.00079668785,0.06338337,0.00023063748,0.00013999113,0.0000016557842,0.006993034,0.2935445,0.023152316,0.50010836,0.025408909,0.08619355],"study_design_scores_gemma":[0.00019126698,0.00007565017,0.12654829,0.000010195449,0.000011579871,0.00009167972,0.00040778014,0.8441268,0.000048071364,0.027454358,0.0009128249,0.00012147925],"about_ca_topic_score_codex":0.000022312905,"about_ca_topic_score_gemma":4.5233614e-7,"teacher_disagreement_score":0.5505823,"about_ca_system_score_codex":0.000050111506,"about_ca_system_score_gemma":0.000106454456,"threshold_uncertainty_score":0.9160263},"labels":[],"label_agreement":null},{"id":"W4285805245","doi":"10.1145/3520304.3528883","title":"Genetic programming with external memory in sequence recall tasks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Genetic programming; Benchmark (surveying); Recall; Task (project management); Machine learning; Scalability; Artificial intelligence; Sequence (biology); Set (abstract data type); Theoretical computer science; Programming language; Cognitive psychology; Psychology","score_opus":0.02325489766358696,"score_gpt":0.2404040909826295,"score_spread":0.21714919331904253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285805245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7894471,0.0004638885,0.20801446,0.0011414865,0.00010791672,0.00052770047,0.0000058568944,0.00006090574,0.00023064089],"genre_scores_gemma":[0.824897,0.000024050858,0.17480439,0.00005362479,0.000025681402,0.00013538483,0.000002383066,0.0000067715305,0.000050750717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848723,0.00004020085,0.00034749944,0.00042778408,0.00046487682,0.00023240193],"domain_scores_gemma":[0.9992306,0.000044676086,0.00026449037,0.00013614104,0.00025854184,0.00006554905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001901264,0.0001510633,0.00016346447,0.00013324514,0.00047260194,0.00006784735,0.00071685365,0.000029741765,0.000010692104],"category_scores_gemma":[0.000009488111,0.0001293796,0.00003696819,0.00069209334,0.00017337776,0.0002541092,0.0005667233,0.0002248154,0.0000012855011],"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":[0.00013486951,0.0011881383,0.27203828,0.0002971871,0.000066768334,0.0000116994315,0.0060863784,0.27074632,0.015019929,0.12687057,0.001118679,0.3064212],"study_design_scores_gemma":[0.00038716802,0.0001586562,0.46794152,0.000057748395,0.00000821126,0.00025742382,0.0003567046,0.5092947,0.000057810823,0.02105689,0.00024998953,0.00017319722],"about_ca_topic_score_codex":0.00007692717,"about_ca_topic_score_gemma":0.0000033146623,"teacher_disagreement_score":0.306248,"about_ca_system_score_codex":0.000109343775,"about_ca_system_score_gemma":0.00016973603,"threshold_uncertainty_score":0.5275947},"labels":[],"label_agreement":null},{"id":"W4285805253","doi":"10.1145/3520304.3529043","title":"Regulatory genotype-to-phenotype mappings improve evolvability in genetic programming","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Evolvability; Robustness (evolution); Phenotype; Genetic programming; Fitness landscape; Novelty; Computer science; Genotype; Biology; Grammatical evolution; Neutral network; Computational biology; Genetics; Gene; Artificial intelligence; Population","score_opus":0.013898914795487155,"score_gpt":0.22730685067141557,"score_spread":0.2134079358759284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285805253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87244225,0.00040263764,0.12379142,0.0019226129,0.00026130734,0.00090794405,0.000009265097,0.00008185715,0.00018071198],"genre_scores_gemma":[0.86869603,0.000009178129,0.13091347,0.00009504311,0.0000379105,0.00018258096,0.0000035890448,0.000008891547,0.000053320127],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981256,0.000046381167,0.00048242358,0.0005741898,0.00048365002,0.0002877418],"domain_scores_gemma":[0.99899846,0.0000522711,0.0002552267,0.00021590885,0.0003773986,0.00010072002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031941454,0.00017602034,0.00020767945,0.00018408893,0.0005451031,0.00006551853,0.0008329256,0.000043368043,0.000014208382],"category_scores_gemma":[0.000031023515,0.00017151663,0.00005998429,0.0009590406,0.00013331018,0.0002159992,0.001073847,0.00022362935,0.000004020366],"study_design_candidate":"observational","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":[0.00019169117,0.0017452921,0.24100278,0.0005188464,0.00009623437,0.0000025814618,0.0104018655,0.172799,0.036533568,0.20849198,0.0039188853,0.32429728],"study_design_scores_gemma":[0.00023772924,0.00011170312,0.66735,0.000020297402,0.0000067283695,0.000020735082,0.0002453931,0.29699787,0.00005137588,0.033982527,0.00081077934,0.00016485713],"about_ca_topic_score_codex":0.00007860429,"about_ca_topic_score_gemma":0.0000028248219,"teacher_disagreement_score":0.4263472,"about_ca_system_score_codex":0.00019668197,"about_ca_system_score_gemma":0.0001886999,"threshold_uncertainty_score":0.6994245},"labels":[],"label_agreement":null},{"id":"W4285805506","doi":"10.1145/3520304.3528765","title":"On the interaction between lexicase selection, modularity and data subsets","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Modularity (biology); Selection (genetic algorithm); Computer science; Representation (politics); Population; Benchmarking; Range (aeronautics); Artificial intelligence; Machine learning; Theoretical computer science; Biology; Engineering","score_opus":0.05248454930940616,"score_gpt":0.28094247180006954,"score_spread":0.22845792249066338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285805506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83592635,0.00015028231,0.15404294,0.008946331,0.00015721253,0.00041856698,0.000044628774,0.00006164832,0.0002520374],"genre_scores_gemma":[0.99071914,0.000023972832,0.008986804,0.00011986056,0.000050338924,0.00003964947,0.000021854112,0.000005114858,0.000033293072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878967,0.000058729347,0.0002530497,0.00042076808,0.0003441311,0.00013363108],"domain_scores_gemma":[0.9991153,0.0001785225,0.00022469272,0.0002072354,0.00022478291,0.000049494356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033818375,0.00011902284,0.000119759534,0.00007272235,0.00121449,0.00009327514,0.00082877575,0.000027168624,0.0000143506895],"category_scores_gemma":[0.000040497394,0.00009021714,0.00002560373,0.00042216195,0.00013635673,0.00033792286,0.0012472904,0.00024640636,0.0000017847428],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047116253,0.00044507242,0.0715349,0.00008119068,0.00010855255,3.8268107e-7,0.0015112686,0.019822937,0.0014198535,0.8316244,0.025010424,0.04839389],"study_design_scores_gemma":[0.00013858402,0.00007171088,0.2288584,0.000013475047,0.000013723758,0.000049866663,0.00021367057,0.67753184,0.000038299717,0.092201546,0.00077376387,0.00009515502],"about_ca_topic_score_codex":0.000043761098,"about_ca_topic_score_gemma":0.0000010040178,"teacher_disagreement_score":0.73942286,"about_ca_system_score_codex":0.000052433323,"about_ca_system_score_gemma":0.00006972833,"threshold_uncertainty_score":0.9340998},"labels":[],"label_agreement":null},{"id":"W4285805521","doi":"10.1145/3520304.3528766","title":"Benchmarking genetic programming in a multi-action reinforcement learning locomotion task","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Benchmarking; Reinforcement learning; Benchmark (surveying); Computer science; Task (project management); Scalability; Artificial intelligence; Action (physics); Genetic programming; Machine learning; Engineering","score_opus":0.032416746657698725,"score_gpt":0.2598119153093554,"score_spread":0.22739516865165665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285805521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36599535,0.00022814976,0.6323615,0.0005626159,0.0001678621,0.0005329078,0.0000011731971,0.000065815446,0.00008465435],"genre_scores_gemma":[0.89527756,0.00005092099,0.104374625,0.000025791067,0.000032354157,0.00016253901,0.000010052919,0.000007200264,0.0000589472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998436,0.00005768625,0.0004468008,0.0004055122,0.0004183715,0.00023563243],"domain_scores_gemma":[0.9992055,0.000038694892,0.00036553774,0.00009935133,0.00023676487,0.00005414539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026515598,0.00015185939,0.0001587464,0.00019263008,0.0007693314,0.00007529609,0.0004462726,0.0000372582,0.00001194007],"category_scores_gemma":[0.000016421469,0.00015136825,0.000054331227,0.00072369457,0.00008607671,0.0002972213,0.0006218235,0.00027882314,0.0000014554614],"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":[0.000016839565,0.0003250414,0.068660356,0.00010042062,0.000020347627,6.9309334e-7,0.0020792603,0.8136453,0.0041734795,0.013188087,0.0002344075,0.09755576],"study_design_scores_gemma":[0.00031726944,0.00010376437,0.30215666,0.0000311975,0.000006096285,0.0000450222,0.0003825364,0.6933457,0.000031273674,0.002999573,0.00046329785,0.000117604075],"about_ca_topic_score_codex":0.0000773464,"about_ca_topic_score_gemma":0.0000030195995,"teacher_disagreement_score":0.5292822,"about_ca_system_score_codex":0.00018529462,"about_ca_system_score_gemma":0.00009581992,"threshold_uncertainty_score":0.61726177},"labels":[],"label_agreement":null},{"id":"W4288720071","doi":"10.3390/s22155659","title":"Plant Tissue Modelling Using Power-Law Filters","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Academy of Scientific Research and Technology","keywords":"Electrical impedance; Filter (signal processing); Power (physics); Dielectric spectroscopy; Equivalent circuit; Heuristic; Electronic engineering; Biological system; Computer science; Engineering; Electrical engineering; Voltage; Chemistry; Electrode; Electrochemistry; Physics; Artificial intelligence; Biology","score_opus":0.030473856898595057,"score_gpt":0.24876777781564605,"score_spread":0.218293920917051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288720071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1999023,0.00009972121,0.7953451,0.000941606,0.00034799724,0.00015339388,0.00005346528,0.00017822119,0.0029781796],"genre_scores_gemma":[0.8624903,0.0000015948541,0.13680482,0.00029210103,0.00003986458,0.000013249898,0.000009707781,0.0000079847605,0.00034036802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924284,0.000035324985,0.0001117223,0.00023428927,0.00020001784,0.00017580122],"domain_scores_gemma":[0.9995651,0.000029453695,0.000039967545,0.00029873554,0.00001645875,0.000050254734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000961805,0.000067690846,0.00006547934,0.000042652126,0.00055811513,0.000035884506,0.00035898836,0.000013676162,0.00004604085],"category_scores_gemma":[0.0000010206084,0.00007457415,0.00002882003,0.00022802157,0.00002371017,0.00010512435,0.00021535988,0.000112485766,0.000021810352],"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":[8.262525e-7,0.000025504085,0.0000035679898,9.774552e-7,0.0000038839376,0.00001328882,0.0003542427,0.8397541,0.0006912141,0.15860458,0.00024314963,0.00030466804],"study_design_scores_gemma":[0.000057469686,0.000028756169,0.000007966904,0.0000016948497,0.0000018411068,0.000076196695,0.000096088814,0.9557554,0.00044390245,0.003505865,0.0399231,0.000101725724],"about_ca_topic_score_codex":0.00016882483,"about_ca_topic_score_gemma":0.0000012787734,"teacher_disagreement_score":0.662588,"about_ca_system_score_codex":0.000059732763,"about_ca_system_score_gemma":0.000026195881,"threshold_uncertainty_score":0.4292627},"labels":[],"label_agreement":null},{"id":"W4292072693","doi":"10.1109/newcas52662.2022.9842014","title":"An FPGA Implementation of A Portable DNA Sequencing Device Based on RISC-V","year":2022,"lang":"en","type":"article","venue":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Field-programmable gate array; Computer science; x86; Application-specific integrated circuit; Reduced instruction set computing; Embedded system; Efficient energy use; Mobile device; Computer architecture; ARM architecture; Instruction set; Computer hardware; Operating system; Software; Engineering","score_opus":0.06194817081840881,"score_gpt":0.3270940042653912,"score_spread":0.2651458334469824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292072693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22675519,0.000082591025,0.7591565,0.006733856,0.0012505635,0.0011002724,0.00038852316,0.00035804094,0.004174438],"genre_scores_gemma":[0.988137,0.0000071607574,0.009209911,0.0014553452,0.00012156597,0.00039170144,0.00019183094,0.000024176788,0.00046128742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967822,0.00025551248,0.0006554345,0.0008434131,0.0009915689,0.00047185874],"domain_scores_gemma":[0.9979137,0.00016627621,0.00045402488,0.00096540875,0.00029164331,0.0002089051],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056438276,0.00029935088,0.00032424406,0.00034754732,0.0005728013,0.00011933852,0.0015981362,0.00005874902,0.0017209471],"category_scores_gemma":[0.000013307517,0.00032512817,0.00016516625,0.00086216675,0.00009465815,0.00070448837,0.00026460428,0.00041807324,0.000051222072],"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":[0.00039475758,0.003674115,0.0069579664,0.00019997744,0.00039957653,0.0004157956,0.008223673,0.15731934,0.077749744,0.5113009,0.08496168,0.14840245],"study_design_scores_gemma":[0.0014619724,0.0015328898,0.0044553387,0.00006998826,0.000042522013,0.00017542577,0.0040776567,0.94287,0.012770469,0.007890802,0.023830371,0.0008225391],"about_ca_topic_score_codex":0.0011823141,"about_ca_topic_score_gemma":0.00030406,"teacher_disagreement_score":0.7855507,"about_ca_system_score_codex":0.00039196186,"about_ca_system_score_gemma":0.0012587105,"threshold_uncertainty_score":0.99992007},"labels":[],"label_agreement":null},{"id":"W4292415121","doi":"10.1007/s10710-022-09437-9","title":"Editorial Introduction","year":2022,"lang":"en","type":"editorial","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.004277830987765302,"score_gpt":0.22872496680388982,"score_spread":0.22444713581612452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292415121","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011869714,0.0027350797,0.035434872,0.0006458491,0.96043557,0.00029065413,0.00003284385,0.0003248065,0.00008845107],"genre_scores_gemma":[0.000010296538,0.00036513002,0.09620491,0.000008715526,0.90122694,0.0004116014,0.00028951818,0.000027776217,0.0014551043],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974144,0.000085086744,0.00032767258,0.0009284832,0.00083427917,0.000410071],"domain_scores_gemma":[0.998597,0.00016881463,0.00018238943,0.0007318008,0.00019552438,0.0001244465],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004234477,0.00031080443,0.0002920798,0.00014459691,0.0007672864,0.00042095166,0.0008665505,0.00030799967,0.00005566591],"category_scores_gemma":[0.00014416796,0.00030723328,0.00008532374,0.0004347262,0.00007604248,0.00018071689,0.0007480496,0.0007568605,0.000018101924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003748676,0.000060106293,0.000012277514,0.00003946019,0.000021520198,0.0000013944706,0.00006106857,0.00011355116,0.0000049623886,0.00032870215,0.89974505,0.09960814],"study_design_scores_gemma":[0.00022581988,0.0001499828,0.000019501178,0.000007286742,0.000030545067,0.000009806626,0.000011143831,0.008597892,9.968006e-7,0.002395086,0.9882212,0.00033070458],"about_ca_topic_score_codex":0.00031758047,"about_ca_topic_score_gemma":0.000008832857,"teacher_disagreement_score":0.09927743,"about_ca_system_score_codex":0.0000859526,"about_ca_system_score_gemma":0.00024638893,"threshold_uncertainty_score":0.99993795},"labels":[],"label_agreement":null},{"id":"W4293519282","doi":"10.1109/cibcb55180.2022.9863054","title":"EvoDNN - Evolving Weights, Biases, and Activation Functions in a Deep Neural Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Activation function; Computer science; Differentiable function; Artificial neural network; Artificial intelligence; Deep neural networks; Set (abstract data type); Flexibility (engineering); Feature (linguistics); Coding (social sciences); Function (biology); Code (set theory); Theoretical computer science; Mathematics; Biology","score_opus":0.01799613487483072,"score_gpt":0.23022305169533416,"score_spread":0.21222691682050343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293519282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080947146,0.00054947403,0.908055,0.006293109,0.00028121835,0.00031288486,0.0000033775755,0.00023230474,0.0033254824],"genre_scores_gemma":[0.9642604,0.0000056365193,0.034515433,0.00039083188,0.000093744915,0.00017975539,0.000016312833,0.000004616221,0.0005332452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992629,0.00005237096,0.00012979141,0.00025009742,0.0001391295,0.00016570897],"domain_scores_gemma":[0.99955255,0.00013523431,0.000036422567,0.00021256562,0.000021425612,0.000041796568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013944336,0.00006376656,0.00006047412,0.00008462418,0.00055596547,0.000056798923,0.00020749605,0.000014776505,0.00014026389],"category_scores_gemma":[0.000011882278,0.00006382179,0.000019793646,0.0007819637,0.000016713211,0.00045243363,0.00029607653,0.00014093629,0.0000051706656],"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":[0.000009808533,0.0004300367,0.027613878,0.0000072246476,0.000020859088,0.000009309915,0.00076715107,0.21621737,0.0003705081,0.6373267,0.021774866,0.09545228],"study_design_scores_gemma":[0.00013032484,0.000031536358,0.071636565,0.0000022481747,0.0000013922535,0.000017397962,0.00008641288,0.9137461,0.000004400278,0.007257449,0.0070006945,0.000085473235],"about_ca_topic_score_codex":0.00014165255,"about_ca_topic_score_gemma":0.000044839086,"teacher_disagreement_score":0.8833133,"about_ca_system_score_codex":0.00006946276,"about_ca_system_score_gemma":0.000026495833,"threshold_uncertainty_score":0.4276093},"labels":[],"label_agreement":null},{"id":"W4294723731","doi":"10.1016/j.inffus.2022.09.001","title":"Exploring diversity in data complexity and classifier decision spaces for pool generation","year":2022,"lang":"en","type":"article","venue":"Information Fusion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Classifier (UML); Computer science; Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology)","score_opus":0.32454960079118994,"score_gpt":0.30275272053261065,"score_spread":0.021796880258579288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294723731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2965105,0.000016139651,0.70152074,0.0012767356,0.00019038098,0.00026956646,0.00007112262,0.000039252238,0.0001056034],"genre_scores_gemma":[0.91930586,0.00004760884,0.07972261,0.00022923276,0.000041757394,0.00011015622,0.0005250715,0.0000018045778,0.000015870235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993392,0.000021984422,0.00017980789,0.00014664687,0.00022097985,0.00009138189],"domain_scores_gemma":[0.9994671,0.000055581833,0.00007905464,0.0003232993,0.000044342076,0.000030677013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041287005,0.00005170465,0.000055808276,0.00013143805,0.000985247,0.00009169918,0.00044563288,0.00001405545,0.000014117418],"category_scores_gemma":[0.000025762994,0.00005438547,0.000011394084,0.00025169383,0.000015308467,0.004018477,0.0023988308,0.000069306596,0.00000520027],"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":[0.000058100053,0.00018184728,0.0037057188,0.000033026416,0.000008369303,9.991755e-7,0.008316635,0.019408088,0.00060499186,0.24370638,0.018422449,0.7055534],"study_design_scores_gemma":[0.00035192998,0.00002481961,0.021471445,0.0000030203596,0.0000013601026,0.000003804531,0.00028687887,0.93099385,0.000028727627,0.0027750877,0.043985296,0.00007379438],"about_ca_topic_score_codex":0.00007585627,"about_ca_topic_score_gemma":0.00003538535,"teacher_disagreement_score":0.91158575,"about_ca_system_score_codex":0.00006293049,"about_ca_system_score_gemma":0.000023996039,"threshold_uncertainty_score":0.7577823},"labels":[],"label_agreement":null},{"id":"W4294811494","doi":"10.1109/cec55065.2022.9870271","title":"Mixed Media in Evolutionary Art","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Computer graphics (images); Bitmap; Variety (cybernetics); Stylized fact; Digital art; Genetic programming; Pixel; Image (mathematics); Object (grammar); Computer vision; Artificial intelligence; Simple (philosophy); Media arts; Digital media; Art; Visual arts; World Wide Web","score_opus":0.023231085893861393,"score_gpt":0.25065400120207504,"score_spread":0.22742291530821365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294811494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14436276,0.004879518,0.74105716,0.038227208,0.03968345,0.0049970425,0.0016620883,0.003630647,0.0215001],"genre_scores_gemma":[0.9599504,0.000069890666,0.032764215,0.001121797,0.00040352897,0.0012759996,0.0010196215,0.00005903856,0.0033355278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99553734,0.0005688734,0.0007839483,0.0011136781,0.0013832361,0.0006129174],"domain_scores_gemma":[0.9978164,0.0006782915,0.0003166144,0.00074126525,0.00023156172,0.00021588214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005678854,0.00036861558,0.00035494874,0.0008601235,0.0011666054,0.000078124605,0.0013749991,0.00010203599,0.00046520523],"category_scores_gemma":[0.000069611866,0.0004487303,0.00016503794,0.0025651632,0.0001703525,0.00076306134,0.00059145666,0.00074670033,0.0005070251],"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":[0.00006456763,0.0013107476,0.0012172314,0.00001471268,0.000041377956,0.000113910624,0.00033303734,0.4226462,0.0002544331,0.1465976,0.42028412,0.0071220496],"study_design_scores_gemma":[0.0014759172,0.00025346698,0.06207018,0.00002576973,0.000010726196,0.00018500282,0.00026563034,0.822736,0.00004006132,0.0317847,0.08048709,0.0006654496],"about_ca_topic_score_codex":0.00003536395,"about_ca_topic_score_gemma":0.000021951206,"teacher_disagreement_score":0.81558764,"about_ca_system_score_codex":0.00096571754,"about_ca_system_score_gemma":0.00047206957,"threshold_uncertainty_score":0.99979645},"labels":[],"label_agreement":null},{"id":"W4294811616","doi":"10.1109/cec55065.2022.9870353","title":"Managing Diversity and Many Objectives in Evolutionary Design","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"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; Evolutionary algorithm; Image (mathematics); Diversity (politics); Quality (philosophy); Genetic programming; Artificial intelligence; Road map; Evolutionary computation; Mathematical optimization; Theoretical computer science; Mathematics; Geography; Law","score_opus":0.023409568731471677,"score_gpt":0.24729910156788587,"score_spread":0.2238895328364142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294811616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05520519,0.0016499619,0.932904,0.004738273,0.002327078,0.0012324809,0.000101130565,0.0005162005,0.001325705],"genre_scores_gemma":[0.9702165,0.000107781125,0.028020225,0.00040684364,0.000090191206,0.00029699737,0.00006089396,0.000021192132,0.0007793793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969141,0.0005550337,0.00040263237,0.00091841235,0.000793562,0.00041627296],"domain_scores_gemma":[0.9986589,0.00046651304,0.00020633708,0.00040006734,0.00013105017,0.00013714141],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00052408397,0.00027009984,0.00025031008,0.00065379235,0.003367674,0.000057561872,0.00080574054,0.00006151427,0.00007533272],"category_scores_gemma":[0.000024699624,0.00033519865,0.00007796232,0.001390169,0.00016479791,0.00078984495,0.0019120179,0.0004824238,0.000029829123],"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":[0.00012345181,0.0008817704,0.008364993,0.00002645807,0.00006712397,0.00013455778,0.0013887718,0.853444,0.0001615381,0.0969844,0.027748162,0.010674816],"study_design_scores_gemma":[0.00081975316,0.00022079896,0.1321366,0.000017299586,0.000009118984,0.000106164865,0.0003321295,0.81391937,0.000013866899,0.0505895,0.0014736635,0.00036177543],"about_ca_topic_score_codex":0.000115205665,"about_ca_topic_score_gemma":0.0000040103205,"teacher_disagreement_score":0.9150113,"about_ca_system_score_codex":0.0006843449,"about_ca_system_score_gemma":0.00016062257,"threshold_uncertainty_score":0.99991},"labels":[],"label_agreement":null},{"id":"W4299812029","doi":"10.48550/arxiv.1802.03318","title":"Nature vs. Nurture: The Role of Environmental Resources in Evolutionary\\n Deep Intelligence","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nature versus nurture; Modern evolutionary synthesis; MNIST database; Artificial neural network; Computer science; Artificial intelligence; Process (computing); Evolutionary algorithm; Deep neural networks; Biology; Machine learning; Evolutionary biology; Genetics","score_opus":0.01606193551316163,"score_gpt":0.16903891216838832,"score_spread":0.1529769766552267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299812029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64203274,0.0067679905,0.34315607,0.0010179415,0.0008909962,0.0015316823,0.00022772477,0.000107993554,0.004266866],"genre_scores_gemma":[0.99492514,0.0015021672,0.0025451595,0.00009997272,0.00022140727,0.0000055343153,0.000024858555,0.0000209213,0.000654836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656403,0.00033426948,0.0005628081,0.0016834165,0.0002906033,0.000564851],"domain_scores_gemma":[0.9967414,0.00031177406,0.00060911215,0.0020258727,0.0001260241,0.00018578995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044396863,0.0005048741,0.0004322944,0.00031577155,0.00057017023,0.00006867787,0.004341709,0.0007554785,0.00031694834],"category_scores_gemma":[0.000033910892,0.0004940124,0.00033797225,0.0014201957,0.0015546614,0.00048635507,0.0030836207,0.0017288146,0.0001923423],"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":[0.00027443847,0.0021766974,0.03506445,0.000094156065,0.0002583803,0.00010208065,0.007141342,0.33060232,0.00034884427,0.6118843,0.0002943197,0.011758661],"study_design_scores_gemma":[0.00019214982,0.00012115779,0.06398968,0.00009945263,0.0000655524,0.000019337662,0.0013449497,0.7836102,0.00028726098,0.14085093,0.008922717,0.0004965769],"about_ca_topic_score_codex":0.00020030403,"about_ca_topic_score_gemma":0.00006340164,"teacher_disagreement_score":0.47103336,"about_ca_system_score_codex":0.000494247,"about_ca_system_score_gemma":0.00019942518,"threshold_uncertainty_score":0.99975115},"labels":[],"label_agreement":null},{"id":"W4300019589","doi":"10.48550/arxiv.1802.02423","title":"On the Generalizability of Linear and Non-Linear Region of\\n Interest-Based Multivariate Regression Models for fMRI Data","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Multivariate statistics; Univariate; Overfitting; General linear model; Bayesian multivariate linear regression; Linear regression; Generalizability theory; Functional magnetic resonance imaging; Linear model; Artificial intelligence; Computer science; Proper linear model; Regression; Statistics; Pattern recognition (psychology); Machine learning; Mathematics; Psychology; Artificial neural network","score_opus":0.26806971921984746,"score_gpt":0.26782813762062274,"score_spread":0.00024158159922471611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300019589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20996155,0.000055843255,0.7877888,0.00060915883,0.00019816258,0.0010161739,0.00030109388,0.00002464197,0.00004459508],"genre_scores_gemma":[0.9654448,0.00020410815,0.033783007,0.000104162,0.00010791722,0.0000055905957,0.00012268416,0.000020596932,0.00020712632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968532,0.00032281972,0.00057018315,0.0018161996,0.00012416551,0.00031343245],"domain_scores_gemma":[0.9928931,0.00088419294,0.0009837911,0.0043283035,0.000756953,0.00015363161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009859364,0.00040886435,0.0005201241,0.00018352672,0.00047922644,0.000029748131,0.0032745046,0.00033318208,0.0000146007715],"category_scores_gemma":[0.0001532799,0.0003356387,0.00022182577,0.00058192486,0.0008819496,0.00045180423,0.003208114,0.00039718137,0.0000041914755],"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":[0.0006685046,0.0011399837,0.00039498816,0.0005073581,0.00014985935,0.000010026541,0.00038293144,0.71337783,0.00031863604,0.28046834,0.001795341,0.00078616315],"study_design_scores_gemma":[0.0009223203,0.00029094185,0.0002860747,0.00041264747,0.00010961967,0.0000015350266,0.00004617515,0.93016315,0.000768116,0.066514544,0.00019148113,0.00029341198],"about_ca_topic_score_codex":0.0003589272,"about_ca_topic_score_gemma":0.000016418264,"teacher_disagreement_score":0.75548327,"about_ca_system_score_codex":0.00008776961,"about_ca_system_score_gemma":0.00036165438,"threshold_uncertainty_score":0.9999096},"labels":[],"label_agreement":null},{"id":"W4302343786","doi":"10.1007/978-3-642-17508-4","title":"Learning Classifier Systems","year":2010,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Classifier (UML); Artificial intelligence; Computer science","score_opus":0.012436757185495206,"score_gpt":0.24047724926240355,"score_spread":0.22804049207690835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302343786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003661544,0.00038596138,0.9896198,0.0006597397,0.0027113957,0.00034092367,0.000002569485,0.00026213247,0.005980896],"genre_scores_gemma":[0.046404846,0.00007128942,0.9324038,0.0008513027,0.0031306902,0.00011935975,0.000029449453,0.00006674188,0.0169225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966454,0.00005489682,0.00042530926,0.0013761704,0.00086427445,0.00063394365],"domain_scores_gemma":[0.9975468,0.00043512124,0.0002584803,0.00131575,0.00025927756,0.0001845897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00083476066,0.00037537742,0.000377483,0.0005616661,0.000524626,0.00077866676,0.0036592192,0.00043524458,0.000009188427],"category_scores_gemma":[0.00007636716,0.00034529128,0.0000978562,0.0011720258,0.0005724902,0.0005810661,0.0011281279,0.0019871956,0.00012489781],"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":[0.0000015042237,0.00009163483,0.00012689369,0.00007022374,0.000013435542,0.00006391426,0.0008027335,0.19418833,0.0004644153,0.06282383,0.0010544535,0.7402986],"study_design_scores_gemma":[0.000111958405,0.000074958174,0.0001662566,0.00012859177,0.000003408523,0.00009289195,1.7614414e-7,0.9155546,0.00007635889,0.037000623,0.046339963,0.00045022706],"about_ca_topic_score_codex":0.000023974973,"about_ca_topic_score_gemma":0.000019060977,"teacher_disagreement_score":0.7398484,"about_ca_system_score_codex":0.00033281557,"about_ca_system_score_gemma":0.0014345814,"threshold_uncertainty_score":0.9998999},"labels":[],"label_agreement":null},{"id":"W4308272241","doi":"10.21203/rs.3.rs-2208846/v1","title":"Evolutionary design of swing-up controllers for stabilization task of underactuated inverted pendulums","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital","funders":"","keywords":"Underactuation; Inverted pendulum; Genetic programming; Control theory (sociology); Control engineering; Computer science; Nonlinear system; Set (abstract data type); Nonlinear control; Controller (irrigation); Fitness function; Process (computing); Genetic algorithm; Artificial intelligence; Engineering; Control (management); Machine learning","score_opus":0.11388763413710282,"score_gpt":0.38418929037160443,"score_spread":0.27030165623450164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308272241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011988028,0.0007845757,0.99253386,0.0010632375,0.00023149724,0.0034716679,0.00048117043,0.0000854721,0.0001497008],"genre_scores_gemma":[0.94468933,0.00029342345,0.05101378,0.000020194288,0.0000715682,0.0021934283,0.0011394585,0.000035273944,0.0005435394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99609727,0.00079774065,0.000643946,0.0007121207,0.0013133384,0.00043559668],"domain_scores_gemma":[0.9947605,0.0016727606,0.000364652,0.0011035017,0.001978799,0.00011975412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002077158,0.00019856525,0.00042126531,0.0006104106,0.00043078794,0.000050071005,0.0015289665,0.00019229682,0.00020035116],"category_scores_gemma":[0.0006226503,0.00021032349,0.00020425573,0.0011916023,0.00015690178,0.00022498301,0.0015347709,0.00065591914,0.00000911787],"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":[0.0004077069,0.0015943792,0.0006563652,0.0021702936,0.00039628573,0.000004903209,0.0025724722,0.76835024,0.007780431,0.17194529,0.039228063,0.0048935944],"study_design_scores_gemma":[0.0008495149,0.00035782816,0.001594062,0.00012747933,0.000013893013,0.0000015695845,0.000414158,0.9478366,0.00033618155,0.046534527,0.0017436817,0.0001905101],"about_ca_topic_score_codex":0.00023624234,"about_ca_topic_score_gemma":0.0000057382767,"teacher_disagreement_score":0.9434905,"about_ca_system_score_codex":0.0004640994,"about_ca_system_score_gemma":0.0019525667,"threshold_uncertainty_score":0.8576743},"labels":[],"label_agreement":null},{"id":"W4308594125","doi":"10.31234/osf.io/5tzph","title":"How a Generation Was Misled About Natural Selection (Natural Selection: How it Works, How it Applies to Culture)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Natural selection; Natural (archaeology); Selection (genetic algorithm); Cultural transmission in animals; Process (computing); Darwinism; Adaptation (eye); Protocell; Computer science; Biology; Evolutionary biology; Artificial intelligence; Paleontology; Genetics","score_opus":0.017775267760908958,"score_gpt":0.25450726524025963,"score_spread":0.23673199747935067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308594125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002297701,0.0014729628,0.8397068,0.1496182,0.0033445472,0.0018222431,0.00002512836,0.0009454199,0.00076703075],"genre_scores_gemma":[0.5311678,0.0003549392,0.328885,0.0023910268,0.004010038,0.002704151,0.0007916155,0.000060613875,0.12963481],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99589556,0.00018764619,0.0003533196,0.0018763266,0.0010137473,0.0006733931],"domain_scores_gemma":[0.9977706,0.00006917549,0.0003611663,0.0008904937,0.00067961984,0.00022894815],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00034239743,0.00069265295,0.0005064659,0.00036294642,0.0014703053,0.003666886,0.0016125075,0.00041409617,0.00010904107],"category_scores_gemma":[0.000067346555,0.0006478451,0.00033632363,0.0017877539,0.000060014976,0.0012341689,0.001470626,0.0019083925,0.000028435868],"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":[0.000032990854,0.00032104456,0.00009120487,0.0001174423,0.00034711597,0.0000074778327,0.0029716513,0.022197016,0.019412955,0.044693325,0.8752813,0.034526486],"study_design_scores_gemma":[0.00039937394,0.0001166682,0.00061984267,0.00005914272,0.000058309088,0.00011161622,0.0006539329,0.6223666,0.002467572,0.0013039266,0.37061706,0.0012259329],"about_ca_topic_score_codex":0.00009036658,"about_ca_topic_score_gemma":0.000604686,"teacher_disagreement_score":0.6001696,"about_ca_system_score_codex":0.00090400415,"about_ca_system_score_gemma":0.00038385091,"threshold_uncertainty_score":0.99982965},"labels":[],"label_agreement":null},{"id":"W4310375416","doi":"10.1007/s10710-022-09446-8","title":"Benchmarking ensemble genetic programming with a linked list external memory on scalable partially observable tasks","year":2022,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Benchmarking; Computer science; Observable; Scalability; Operator (biology); State (computer science); Artificial intelligence; Machine learning; Set (abstract data type); Genetic programming; Identification (biology); Algorithm; Programming language","score_opus":0.013273359654309161,"score_gpt":0.2288545776728534,"score_spread":0.21558121801854424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310375416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49030095,0.005882849,0.49930233,0.0011061176,0.00047922498,0.0015904543,0.000013264723,0.0006922923,0.00063250813],"genre_scores_gemma":[0.47795147,0.00004404868,0.5196529,0.00019662699,0.00022382566,0.0010197375,0.000017493998,0.000034490185,0.0008594118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692917,0.00014314115,0.00042986035,0.00098861,0.00065770413,0.0008514999],"domain_scores_gemma":[0.99855334,0.000099549165,0.00020693554,0.0007718738,0.000106863605,0.00026145205],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00046198565,0.00037051685,0.00031849477,0.00015681707,0.0018844226,0.00057340675,0.000848807,0.00006448429,0.000057328747],"category_scores_gemma":[0.000014335688,0.0003361623,0.00008759641,0.0008304466,0.00012793067,0.00021342158,0.0006337926,0.00040230664,0.0000106930365],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048350288,0.00052507216,0.018549714,0.00008175883,0.000055684555,0.00008989003,0.0006261377,0.040393904,0.00069414295,0.0012161592,0.00034388778,0.9373753],"study_design_scores_gemma":[0.0015212992,0.0023506186,0.03743979,0.00014784305,0.00009265461,0.0006914349,0.00020463314,0.8825511,0.00021303068,0.0032119246,0.07045731,0.0011184126],"about_ca_topic_score_codex":0.0009445468,"about_ca_topic_score_gemma":0.00011944562,"teacher_disagreement_score":0.9362569,"about_ca_system_score_codex":0.000090145004,"about_ca_system_score_gemma":0.00017851924,"threshold_uncertainty_score":0.99990904},"labels":[],"label_agreement":null},{"id":"W4312472887","doi":"10.1109/access.2022.3225435","title":"Facing Up Fare War: Generating Competitive Price Models With Gene Expression Programming","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"European Regional Development Fund; Ministerio de Ciencia e Innovación","keywords":"Gene expression programming; Computer science; Genetic programming; Artificial intelligence","score_opus":0.03221900081480514,"score_gpt":0.2765023299847848,"score_spread":0.24428332916997966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312472887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05885143,0.00014524253,0.939547,0.00024617137,0.00019209988,0.00032502224,0.000013855449,0.0001954338,0.0004837597],"genre_scores_gemma":[0.75029194,0.000005926623,0.24876945,0.00016353336,0.00009512394,0.0005041961,0.00001765673,0.000012025301,0.00014017102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863625,0.000063290674,0.00017744802,0.00046324634,0.00038622468,0.0002735211],"domain_scores_gemma":[0.99925643,0.000044258166,0.00012966958,0.00039256964,0.00010383951,0.00007322883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014802364,0.00012922466,0.0001162924,0.00006872375,0.0011917826,0.000207333,0.0010875663,0.00002087375,0.0000163043],"category_scores_gemma":[0.0000026358628,0.00011606662,0.000033038606,0.0005388727,0.000026079717,0.0011798616,0.00061656506,0.00020135596,0.0000030850024],"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":[0.000013090463,0.0002652394,0.0005148,0.000030099298,0.00003248751,0.000038514543,0.004259847,0.8884812,0.031641513,0.03578559,0.0007831404,0.03815446],"study_design_scores_gemma":[0.0003495917,0.00007396142,0.00012853756,0.000026560718,0.0000057180946,0.00006299704,0.000495913,0.96914655,0.024211172,0.0012313694,0.0039506922,0.000316948],"about_ca_topic_score_codex":0.00005187782,"about_ca_topic_score_gemma":0.0000046934224,"teacher_disagreement_score":0.6914405,"about_ca_system_score_codex":0.000086670356,"about_ca_system_score_gemma":0.000083615305,"threshold_uncertainty_score":0.9166349},"labels":[],"label_agreement":null},{"id":"W4320855027","doi":"10.48550/arxiv.2302.06548","title":"Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Alberta; Alberta Machine Intelligence Institute; Compute Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institute for Advanced Research","keywords":"Reinforcement learning; Computer science; Noise (video); Focus (optics); Task (project management); Artificial intelligence; Margin (machine learning); Robot; Code (set theory); Machine learning; Deep learning; Pattern recognition (psychology); Set (abstract data type)","score_opus":0.0645443691250533,"score_gpt":0.19874993150394174,"score_spread":0.13420556237888842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320855027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18891795,0.0000121967805,0.8096683,0.000102152386,0.00010837736,0.0002635707,0.0000010939749,0.000413665,0.00051266426],"genre_scores_gemma":[0.98628396,0.000051433588,0.012222685,0.000017947708,0.000019625857,0.00000994115,0.000027261725,0.000020772188,0.0013463959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984494,0.00006109721,0.00021943593,0.00080023875,0.0001045865,0.00036527443],"domain_scores_gemma":[0.9988749,0.00008515231,0.00020005033,0.0006827402,0.00005147434,0.00010570952],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021745177,0.00023977371,0.00024729606,0.00034731787,0.0001898543,0.00008666673,0.0009976518,0.00011870009,0.000019936233],"category_scores_gemma":[0.000014708278,0.00027339364,0.000079510835,0.00083102105,0.00006008626,0.0003076021,0.0011208977,0.0005837571,0.00008462328],"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":[0.000003086731,0.00001916028,0.0004770942,0.000051325667,0.000023282222,0.00018499781,0.0010638278,0.9788766,0.0000141172595,0.017690944,0.0000036005697,0.0015919687],"study_design_scores_gemma":[0.00030110587,0.000041566618,0.004829536,0.00019476519,0.000018065823,0.0000067389415,0.0004208559,0.98687595,0.0000041396474,0.0069519365,0.000041727424,0.0003136364],"about_ca_topic_score_codex":0.00014413056,"about_ca_topic_score_gemma":0.00020168681,"teacher_disagreement_score":0.79744565,"about_ca_system_score_codex":0.000315158,"about_ca_system_score_gemma":0.00020383297,"threshold_uncertainty_score":0.9999718},"labels":[],"label_agreement":null},{"id":"W4323967467","doi":"10.1007/978-981-19-8460-0_4","title":"Genetic Programming for Interpretable and Explainable Machine Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Queen's University","funders":"","keywords":"Interpretability; Genetic programming; Artificial intelligence; Machine learning; Computer science; Field (mathematics); Mathematics","score_opus":0.015279925989371296,"score_gpt":0.2302078721210929,"score_spread":0.2149279461317216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323967467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002320743,0.0065762126,0.98874104,0.00040126254,0.00019851598,0.00078780483,0.000028619948,0.00030048503,0.0027339743],"genre_scores_gemma":[0.01583604,0.0023265665,0.8384455,0.00008728564,0.00037133964,0.00038611933,0.00036449963,0.00009877856,0.14208388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846965,0.00002203439,0.0003482263,0.0006683008,0.00020484821,0.0002869308],"domain_scores_gemma":[0.9991757,0.00018192227,0.00018185718,0.00018837469,0.00015593244,0.0001161812],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001308875,0.000272397,0.00024449785,0.0001966924,0.00059932785,0.00012705727,0.00020090687,0.00016557689,0.000006223763],"category_scores_gemma":[0.000015478601,0.00030172948,0.00006919202,0.00009119044,0.00010300232,0.00016275083,0.00029360753,0.00019642463,0.000026939762],"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":[0.00002358803,0.00006104503,0.00041484978,0.00039957114,0.00019191255,0.00002050769,0.0006412345,0.043036904,0.000027976947,0.24478136,0.003935791,0.70646524],"study_design_scores_gemma":[0.00027683473,0.00019734853,0.003912561,0.00007336962,0.00004244309,0.00009987203,0.000017084929,0.77010584,5.010848e-7,0.16001083,0.06493355,0.00032973482],"about_ca_topic_score_codex":0.000024198376,"about_ca_topic_score_gemma":0.0000042069128,"teacher_disagreement_score":0.72706896,"about_ca_system_score_codex":0.00006259739,"about_ca_system_score_gemma":0.0000885478,"threshold_uncertainty_score":0.9999435},"labels":[],"label_agreement":null},{"id":"W4360798737","doi":"10.1016/j.cam.2023.115225","title":"A new deterministic PSO algorithm for real-time systems implemented on low-power devices","year":2023,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Evolutionary Algorithms and Applications","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":"","keywords":"Particle swarm optimization; Algorithm; Realization (probability); Flexibility (engineering); Block (permutation group theory); Emulation; Function (biology); Computer science; Mathematical optimization; Mathematics","score_opus":0.0171070896693709,"score_gpt":0.2817058613901625,"score_spread":0.2645987717207916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360798737","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007072586,0.000020798989,0.9915858,0.00044481936,0.000106030115,0.00028700713,0.000023104452,0.000057908597,0.00040193158],"genre_scores_gemma":[0.03385478,0.00001694251,0.9654637,0.00008335979,0.00022145532,0.000032806216,0.00001680741,0.00001526036,0.00029487564],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987787,0.000008783574,0.00053307874,0.00014955972,0.0003626664,0.00016719358],"domain_scores_gemma":[0.9984717,0.00069575204,0.00040947623,0.00011803997,0.00017792282,0.00012710557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003431327,0.0001268924,0.00024628526,0.00015612046,0.00016657355,0.0001337285,0.0002932559,0.000036209723,0.000005881681],"category_scores_gemma":[0.000011859339,0.00010342479,0.000067765126,0.00028093197,0.000020602965,0.00011411463,0.00006448873,0.00007090782,0.000037145066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014702016,0.0003124345,0.0000025075874,0.00019454575,0.00015310038,0.000012567914,0.0010626204,0.0395821,0.0005614987,0.8702624,0.02135907,0.06648245],"study_design_scores_gemma":[0.00067867007,0.00017294782,0.0003425435,0.000084154846,0.000020518602,0.00009800688,0.00013561676,0.8558307,0.0000355556,0.14082298,0.0016432774,0.00013498456],"about_ca_topic_score_codex":8.127242e-7,"about_ca_topic_score_gemma":6.691087e-8,"teacher_disagreement_score":0.81624866,"about_ca_system_score_codex":0.000025274589,"about_ca_system_score_gemma":0.00013228749,"threshold_uncertainty_score":0.42175406},"labels":[],"label_agreement":null},{"id":"W4360897297","doi":"10.2139/ssrn.4321420","title":"An Individual Evolutionary Learning Model Meets Cournot","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cournot competition; Computer science; Economics; Artificial intelligence; Mathematical economics","score_opus":0.015018501455154392,"score_gpt":0.26498052890551677,"score_spread":0.24996202745036236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360897297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048921004,0.00090271584,0.94443655,0.004634672,0.00012896948,0.00009577256,0.000004071008,0.00045595746,0.00042025896],"genre_scores_gemma":[0.98242646,0.001395398,0.0141966,0.000092935086,0.00028378196,0.000019336423,0.000024857476,0.000018017372,0.0015426369],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971571,0.000093951465,0.00023854576,0.00030557864,0.00048514968,0.0017196502],"domain_scores_gemma":[0.9992996,0.000043612305,0.000117807984,0.0002814221,0.0001118033,0.00014577154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015492577,0.00013374438,0.0001131417,0.00022083409,0.00091331365,0.00013866744,0.0011010406,0.0000694325,0.0000064060764],"category_scores_gemma":[0.000022429183,0.00013085765,0.00008227758,0.0007300156,0.000039870614,0.00092264696,0.00014536489,0.001538351,0.00014848185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000288319,0.000082062,0.0004370962,0.0000011698279,0.000046856516,0.0000045055176,0.0002644061,0.1247418,0.00030578187,0.84827906,0.00094857905,0.02488582],"study_design_scores_gemma":[0.0001795629,0.00012369586,0.0019012543,0.000003777858,0.00000666822,0.0002882897,0.00023362436,0.6263453,0.0000074365967,0.36942995,0.0013550132,0.00012543818],"about_ca_topic_score_codex":0.0000102482645,"about_ca_topic_score_gemma":0.000018417777,"teacher_disagreement_score":0.9335054,"about_ca_system_score_codex":0.00037690563,"about_ca_system_score_gemma":0.0020447157,"threshold_uncertainty_score":0.70245624},"labels":[],"label_agreement":null},{"id":"W4361767974","doi":"10.1007/978-3-031-29573-7_4","title":"Phenotype Search Trajectory Networks for Linear Genetic Programming","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Phenotype; Genetic programming; Trajectory; Graph; Genotype-phenotype distinction; Theoretical computer science; Artificial intelligence; Biology; Gene; Genetics","score_opus":0.029315099092381978,"score_gpt":0.27389622426426324,"score_spread":0.24458112517188127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361767974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014200924,0.00039158395,0.99631745,0.0006425515,0.0011288541,0.000904209,0.000007081398,0.00032270094,0.00027134936],"genre_scores_gemma":[0.007447601,0.00006534301,0.9896384,0.00032508932,0.001382234,0.00010662405,0.000016905708,0.00005552496,0.00096230424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678564,0.00001986669,0.00041264234,0.001380228,0.0006405579,0.0007610675],"domain_scores_gemma":[0.9978156,0.0005069935,0.00013297927,0.0010504439,0.0003220557,0.00017191589],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00068312144,0.00036882216,0.00033817897,0.00047090737,0.00046032353,0.00031532397,0.0026124048,0.00027369655,0.0000059429562],"category_scores_gemma":[0.00003079374,0.00035901298,0.00014999264,0.00085904193,0.00046042612,0.0002480169,0.00081647147,0.00061714154,0.00004359783],"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":[0.000001916325,0.000020324635,0.000007355496,0.00002555633,0.000007684505,0.000010895881,0.00018457085,0.28541014,0.000012381847,0.019742908,0.000027335123,0.6945489],"study_design_scores_gemma":[0.00014632125,0.0001344109,0.0001738847,0.000109278946,0.000006168044,0.000014490544,1.5232939e-7,0.94381493,0.000028728375,0.05287381,0.0023078667,0.00038995693],"about_ca_topic_score_codex":0.000014512957,"about_ca_topic_score_gemma":0.000041885865,"teacher_disagreement_score":0.694159,"about_ca_system_score_codex":0.0001812429,"about_ca_system_score_gemma":0.0005260726,"threshold_uncertainty_score":0.99988616},"labels":[],"label_agreement":null},{"id":"W4361770952","doi":"10.1007/978-3-031-29573-7_9","title":"A Boosting Approach to Constructing an Ensemble Stack","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Boosting (machine learning); Computer science; Residual; Interpretability; Genetic programming; Ensemble learning; Benchmarking; Machine learning; Artificial intelligence; Classifier (UML); Data mining; Algorithm","score_opus":0.03509581858218008,"score_gpt":0.2648130078073741,"score_spread":0.22971718922519402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361770952","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009580288,0.000047621437,0.985752,0.0005183551,0.0007235903,0.00046567724,0.000007857153,0.00042294402,0.011966143],"genre_scores_gemma":[0.017663082,0.000005943646,0.98030376,0.0007535575,0.000516765,0.000030730436,0.00001023495,0.000038809965,0.00067713263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962468,0.000026583128,0.00046451096,0.0017705043,0.0008022056,0.000689363],"domain_scores_gemma":[0.99749935,0.00036063313,0.00019557022,0.0014021238,0.00024722583,0.00029509852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008370516,0.0004015408,0.00037978883,0.0006893595,0.000485051,0.00058015686,0.0030076518,0.00020182646,0.0000033615177],"category_scores_gemma":[0.00008876488,0.0004018765,0.00008138093,0.0012056357,0.00035108207,0.00063933514,0.0015574573,0.00062903337,0.00010156592],"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":[0.0000014269142,0.000042024225,0.000029702054,0.000026862715,0.000007558895,0.00002306504,0.0012026271,0.07679608,0.00028630553,0.24866953,0.00004272751,0.67287207],"study_design_scores_gemma":[0.00011683918,0.00009884441,0.00008342915,0.00015788732,0.0000041411117,0.000119219345,0.0000017021649,0.8270217,0.00026188424,0.17058031,0.000941275,0.0006127361],"about_ca_topic_score_codex":0.000027981863,"about_ca_topic_score_gemma":0.000024823652,"teacher_disagreement_score":0.75022566,"about_ca_system_score_codex":0.00020587324,"about_ca_system_score_gemma":0.00048739905,"threshold_uncertainty_score":0.9998433},"labels":[],"label_agreement":null},{"id":"W4379801796","doi":"10.1002/cjs.11775","title":"Objective model selection with parallel genetic algorithms using an eradication strategy","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Model selection; Machine learning; Genetic algorithm; Artificial intelligence; Population; Algorithm; Feature (linguistics)","score_opus":0.03842926554301982,"score_gpt":0.2630375572372838,"score_spread":0.22460829169426397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379801796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0152874235,0.000040344545,0.9842581,0.00013979031,0.000061712264,0.00007936451,0.000071547736,0.00001816447,0.000043514377],"genre_scores_gemma":[0.29355946,0.000016364549,0.706227,0.00003448249,0.000087975124,0.0000033239742,0.000011177854,0.000009307283,0.00005091792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991853,0.00003357993,0.00022452687,0.00014684295,0.0001806984,0.00022902939],"domain_scores_gemma":[0.99884164,0.00002926885,0.00017596106,0.00013754556,0.00042414595,0.00039141427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013482047,0.000086770044,0.00010026979,0.0002672047,0.00029271294,0.00011289437,0.00028680122,0.000037519258,0.0000058095247],"category_scores_gemma":[0.00001334127,0.000083018,0.00001656595,0.000626815,0.00005842396,0.00038561586,0.00000702552,0.00014430523,0.000005787978],"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":[0.0000018430067,0.000016737536,0.00042114814,0.0000036670258,0.000020764972,0.000052842755,0.00045000564,0.95170003,0.00008063653,0.033342883,0.0008869841,0.0130224265],"study_design_scores_gemma":[0.00015095039,0.00016827924,0.01639108,0.000009456115,0.000014580759,0.00024648051,0.000083783976,0.95564586,0.000011130163,0.02708669,0.00009022001,0.000101484315],"about_ca_topic_score_codex":0.0013905863,"about_ca_topic_score_gemma":0.0039493525,"teacher_disagreement_score":0.27827203,"about_ca_system_score_codex":0.00019356461,"about_ca_system_score_gemma":0.0024114118,"threshold_uncertainty_score":0.4277741},"labels":[],"label_agreement":null},{"id":"W4384663720","doi":"10.1162/artl_a_00407","title":"Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?","year":2023,"lang":"en","type":"article","venue":"Artificial Life","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Field (mathematics); Artificial intelligence; Artificial life; Ubiquitous computing; Human–computer interaction; Data science; Mathematics","score_opus":0.011618171340337848,"score_gpt":0.2894958695313705,"score_spread":0.2778776981910327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384663720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31260645,0.00021833702,0.37404862,0.30776742,0.0027415922,0.0008884247,0.000063033076,0.0008059943,0.0008601098],"genre_scores_gemma":[0.9906172,0.000005628883,0.003979124,0.004687026,0.00054560584,0.000016694783,0.000006388093,0.0000062475337,0.000136088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989073,0.000052071013,0.00026426217,0.00026519794,0.00025542814,0.0002556938],"domain_scores_gemma":[0.99848396,0.000745617,0.00009100275,0.00047309243,0.00010490305,0.00010145688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042101784,0.000092398455,0.00013524696,0.000077156066,0.0004008491,0.00008830213,0.00075560773,0.000078884135,0.0000040144873],"category_scores_gemma":[0.0005583965,0.000054320764,0.00007315477,0.0010067596,0.000031071246,0.000110794936,0.00036646912,0.00022597563,0.000070729155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026287778,0.00013524675,0.0025133134,0.00002555668,0.00006671981,0.000010014648,0.0020427743,0.0044291085,0.009869662,0.8023792,0.09612015,0.08238197],"study_design_scores_gemma":[0.0005741255,0.00039822352,0.3604061,0.0002114116,0.000049176164,0.000011006846,0.0009286097,0.3111945,0.045241408,0.047661733,0.23223947,0.00108424],"about_ca_topic_score_codex":0.000053805223,"about_ca_topic_score_gemma":0.00003547324,"teacher_disagreement_score":0.75471747,"about_ca_system_score_codex":0.000010896877,"about_ca_system_score_gemma":0.000060243878,"threshold_uncertainty_score":0.3083048},"labels":[],"label_agreement":null},{"id":"W4385216284","doi":"10.1145/3583133.3596356","title":"Gaggle: Genetic Algorithms on the GPU using PyTorch","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Bank of Canada","keywords":"Computer science; Scalability; Usability; Interface (matter); Artificial intelligence; Artificial neural network; Deep learning; Neuroevolution; Parallel computing; Machine learning; Computer architecture; Human–computer interaction; Operating system","score_opus":0.053250387202081854,"score_gpt":0.2889604164032335,"score_spread":0.23571002920115164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385216284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031275816,0.000041139312,0.9533717,0.009609399,0.00026152324,0.00021401607,0.0000037397301,0.00047478214,0.0047479016],"genre_scores_gemma":[0.46790174,0.00010495573,0.51580524,0.0032394973,0.0008210971,0.00016788178,0.000010340224,0.000036958532,0.011912328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916023,0.000031240885,0.00010985661,0.000247114,0.00021561314,0.00023596222],"domain_scores_gemma":[0.9992237,0.00014362803,0.000025020141,0.0005235562,0.00003753942,0.00004655736],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018560168,0.00007461787,0.00005229733,0.000058211786,0.000379586,0.00008080811,0.00064220733,0.00002612107,0.000055598055],"category_scores_gemma":[0.00001054522,0.000048869035,0.00003819824,0.00090209674,0.000036026206,0.00008841991,0.00020827036,0.00008347584,0.00080133555],"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":[8.477033e-7,0.000103831866,0.00017925169,0.0000035906562,0.000021867569,0.000017842909,0.0003357164,0.010406227,0.0022219892,0.84796447,0.057586346,0.08115802],"study_design_scores_gemma":[0.00005352122,0.000020459482,0.010771135,0.0000042001093,0.0000016160654,0.000011197227,0.000030714044,0.96716255,0.00039546643,0.012533337,0.008922634,0.000093199356],"about_ca_topic_score_codex":0.000040745348,"about_ca_topic_score_gemma":0.0000014098612,"teacher_disagreement_score":0.9567563,"about_ca_system_score_codex":0.000024271183,"about_ca_system_score_gemma":0.000043022494,"threshold_uncertainty_score":0.99997663},"labels":[],"label_agreement":null},{"id":"W4385282513","doi":"10.1109/icde55515.2023.00364","title":"MMCo-Clus – An Evolutionary Co-clustering Algorithm for Gene Selection (Extended abstract)","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Cluster analysis; Selection (genetic algorithm); Gene selection; Selection algorithm; Gene; Algorithm; Artificial intelligence; Biology; Genetics; Gene expression; Microarray analysis techniques","score_opus":0.02534786924191622,"score_gpt":0.29872157663116744,"score_spread":0.2733737073892512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385282513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019158386,0.00003253311,0.9940438,0.00092734105,0.00027099103,0.00043724992,0.000043120603,0.0012066672,0.0011224254],"genre_scores_gemma":[0.040628824,0.000037287027,0.9524303,0.0002631414,0.00052964303,0.00049495714,0.00029372438,0.00002836832,0.0052937563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984687,0.000020534186,0.00027469976,0.0005585977,0.0002620926,0.0004153763],"domain_scores_gemma":[0.99911284,0.00009288706,0.00007463293,0.00039966498,0.00016769924,0.00015227913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028976623,0.00016001229,0.00013244919,0.00019486906,0.0006398869,0.00014874506,0.0005466085,0.00008952765,0.000039705497],"category_scores_gemma":[0.000010561368,0.00016422977,0.00008686012,0.0007865486,0.00003649969,0.0012499585,0.00012865721,0.00010460588,0.00020361788],"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":[0.000013711629,0.00066414155,0.00011938023,0.00003823233,0.00008815474,0.000015180114,0.00043791422,0.011050825,0.019200882,0.07099028,0.0500321,0.84734917],"study_design_scores_gemma":[0.00031319904,0.00010554406,0.018028531,0.0000033655922,0.000004714986,0.000054099553,0.00004077186,0.9585294,0.001389113,0.0089960545,0.01230869,0.00022652706],"about_ca_topic_score_codex":0.00006808258,"about_ca_topic_score_gemma":0.000013697483,"teacher_disagreement_score":0.9474786,"about_ca_system_score_codex":0.00011274338,"about_ca_system_score_gemma":0.00011699804,"threshold_uncertainty_score":0.66970956},"labels":[],"label_agreement":null},{"id":"W4385573703","doi":"10.18653/v1/2022.findings-emnlp.114","title":"Text Editing as Imitation Game","year":2022,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Security token; Decoding methods; Artificial intelligence; Sequence (biology); Robustness (evolution); Reinforcement learning; Sequence learning; Encoding (memory); Natural language processing; Theoretical computer science; Algorithm","score_opus":0.013058678216677172,"score_gpt":0.24589753300885053,"score_spread":0.23283885479217337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385573703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066241203,0.000037469566,0.93097246,0.00996735,0.00024424423,0.00009399681,0.0000019666634,0.0002194687,0.051838934],"genre_scores_gemma":[0.9142362,0.0000016132399,0.07963309,0.0009794849,0.00012349483,0.00012078775,0.000007898016,0.0000031127563,0.004894344],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951446,0.000019017236,0.00007428913,0.00014484217,0.00016019818,0.000087220185],"domain_scores_gemma":[0.9997164,0.000036889673,0.00002755752,0.00016913965,0.000023494647,0.000026524924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000105657295,0.000031497315,0.0000272751,0.000033048735,0.00027983368,0.000032933804,0.00030412534,0.0000059016425,0.00032339705],"category_scores_gemma":[0.000008482435,0.000032607895,0.00001963258,0.00027672053,0.000007950909,0.00019734602,0.00021668134,0.00006301981,0.00016271177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0293787e-7,0.00003882541,0.00004366241,5.6740765e-7,0.0000018353446,0.000001402628,0.00024551398,0.0013455958,0.00031811083,0.95764655,0.010861547,0.029496187],"study_design_scores_gemma":[0.00016830837,0.00007977401,0.004260012,0.0000010460724,0.0000017937713,0.00006283534,0.0005772803,0.61201644,0.00031377195,0.10270944,0.27966312,0.00014615926],"about_ca_topic_score_codex":0.000027417127,"about_ca_topic_score_gemma":8.8854495e-7,"teacher_disagreement_score":0.9076121,"about_ca_system_score_codex":0.000031062078,"about_ca_system_score_gemma":0.000030541327,"threshold_uncertainty_score":0.3540971},"labels":[],"label_agreement":null},{"id":"W4386902952","doi":"10.1162/isal_a_00664","title":"Finding Sparse Initialisations using Neuroevolutionary Ticket Search (NeTS)","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"UK Research and Innovation","keywords":"Ticket; Computer science; Computer network","score_opus":0.1289051437679396,"score_gpt":0.33602778849513965,"score_spread":0.20712264472720004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386902952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07566216,0.000025035471,0.9080993,0.00618014,0.00030376023,0.00022506801,0.000016689273,0.0008553954,0.008632468],"genre_scores_gemma":[0.83708316,0.00003218025,0.15974647,0.00056332024,0.00021981577,0.0000470302,0.0000544835,0.000016137617,0.0022374056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885124,0.000056537243,0.00018356089,0.0003228778,0.00027430255,0.00031149658],"domain_scores_gemma":[0.9992289,0.00014732909,0.000028894892,0.00041508395,0.000080723774,0.00009909342],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023276082,0.000090958056,0.00007769921,0.00023902507,0.0005499175,0.00009188024,0.0004897203,0.00003702843,0.000073096875],"category_scores_gemma":[0.000023784542,0.00009384907,0.000049230588,0.0016188145,0.000058288366,0.0005291021,0.00035191013,0.00011601194,0.0007961206],"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":[0.0000013202831,0.00011163254,0.0017806843,0.000008709851,0.000017043787,0.00003700959,0.00051221467,0.036466632,0.003075965,0.9243003,0.0300113,0.0036771977],"study_design_scores_gemma":[0.00010056594,0.0000143416855,0.018930983,0.000005805014,0.0000028033462,0.000041957792,0.00007251464,0.9656892,0.00013702664,0.009319558,0.005557283,0.00012799424],"about_ca_topic_score_codex":0.00007237909,"about_ca_topic_score_gemma":0.0000037741681,"teacher_disagreement_score":0.9292225,"about_ca_system_score_codex":0.00005448645,"about_ca_system_score_gemma":0.00014770104,"threshold_uncertainty_score":0.9999819},"labels":[],"label_agreement":null},{"id":"W4386919946","doi":"10.1109/icaiss58487.2023.10250749","title":"Artificial Intelligence for Development of Variable Power Biomedical Electronics Gadgets Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Electronics; Power electronics; Variable (mathematics); Computer science; Power (physics); Electrical engineering; Engineering; Voltage","score_opus":0.02995225608399267,"score_gpt":0.29740776866959845,"score_spread":0.2674555125856058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386919946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029772185,0.00003253586,0.997425,0.0009839734,0.00005299714,0.00034481718,0.0000064805918,0.0001757903,0.00068065734],"genre_scores_gemma":[0.047146816,0.000010193853,0.9515458,0.00005955634,0.000038672006,0.00074552005,0.000042828353,0.0000060459824,0.00040462273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99903053,0.0000065345143,0.0003158393,0.00024541686,0.00016686288,0.00023479467],"domain_scores_gemma":[0.9993621,0.00012903882,0.00005144082,0.00028045403,0.00011339277,0.0000635737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003189049,0.000065886445,0.00008364963,0.00010228705,0.00018850579,0.00001945849,0.0005023232,0.00004332652,0.000028330907],"category_scores_gemma":[0.000016767806,0.00006131701,0.00003078767,0.0010955435,0.000043791446,0.000087486434,0.00012990145,0.00004718475,0.000119745426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.7949284e-7,0.00010745623,0.0000017390978,0.0000065373724,0.000008002773,5.1807447e-8,0.00011723258,0.000074855976,0.002483638,0.926604,0.001039634,0.06955608],"study_design_scores_gemma":[0.00003992038,0.0000671801,0.0001518712,0.0000064160545,0.00000393311,0.000002625594,0.00007102775,0.23303784,0.019548858,0.3594746,0.38741583,0.00017988801],"about_ca_topic_score_codex":0.0000016228945,"about_ca_topic_score_gemma":0.0000018500497,"teacher_disagreement_score":0.5671294,"about_ca_system_score_codex":0.000032460717,"about_ca_system_score_gemma":0.00031885703,"threshold_uncertainty_score":0.2500435},"labels":[],"label_agreement":null},{"id":"W4387005942","doi":"10.1109/cec53210.2023.10254093","title":"Increasing Features in MAP-Elites Using an Age-Layered Population Structure","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Feature (linguistics); Population; Computer science; Grid; Set (abstract data type); Genetic algorithm; Layer (electronics); Feature vector; Space (punctuation); Artificial intelligence; Algorithm; Theoretical computer science; Data mining; Mathematics; Machine learning; Materials science","score_opus":0.02554115033438046,"score_gpt":0.28984046312766387,"score_spread":0.2642993127932834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387005942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9468048,0.000040004805,0.05196362,0.0005259882,0.00010024908,0.0001235793,0.0000058516657,0.0003045562,0.00013133117],"genre_scores_gemma":[0.83329225,0.0000018342188,0.16639507,0.00007236202,0.00006865614,0.0000034352224,0.00006354726,0.0000049892483,0.00009788901],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992374,0.00005700627,0.00013429987,0.00025863116,0.00013979836,0.00017283995],"domain_scores_gemma":[0.99956554,0.00004405265,0.000034026987,0.00028336825,0.000025013032,0.000047976155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014418957,0.00007796794,0.00007684877,0.00016822376,0.00015247666,0.00012060809,0.00028133267,0.000053707565,0.000012517627],"category_scores_gemma":[0.000015610876,0.000071875744,0.000019354018,0.0008146032,0.00001204703,0.0005698662,0.000105765816,0.00008486449,0.000009108052],"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":[0.000013820437,0.00022913603,0.11557411,0.000049521524,0.000023018354,0.000084678395,0.0023799194,0.21757701,0.103284895,0.49173033,0.0018601589,0.0671934],"study_design_scores_gemma":[0.000083093524,0.000006870968,0.49496338,0.000008775937,9.4335644e-7,0.000012343645,0.000044045988,0.47569656,0.00014183648,0.028815212,0.00013108451,0.00009584414],"about_ca_topic_score_codex":0.0016367546,"about_ca_topic_score_gemma":0.00034517856,"teacher_disagreement_score":0.46291512,"about_ca_system_score_codex":0.00004252366,"about_ca_system_score_gemma":0.00001875795,"threshold_uncertainty_score":0.29310077},"labels":[],"label_agreement":null},{"id":"W4387143486","doi":"10.48550/arxiv.2309.14307","title":"A post-selection algorithm for improving dynamic ensemble selection methods","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Selection (genetic algorithm); Classifier (UML); Machine learning; Ensemble learning; Data mining; Metric (unit); Artificial intelligence; Algorithm","score_opus":0.05432604121401534,"score_gpt":0.24761642378344834,"score_spread":0.19329038256943298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387143486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032516213,0.00003866519,0.99397767,0.00022453417,0.00071943784,0.000764829,0.000052448035,0.00088367634,0.00008710858],"genre_scores_gemma":[0.19470066,0.000045230445,0.7982468,0.000054823016,0.00016933974,0.000042315092,0.00011754158,0.000044696786,0.006578571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772555,0.00015324417,0.00024388371,0.0013571762,0.00008583555,0.0004343149],"domain_scores_gemma":[0.9982569,0.00023287647,0.00029051793,0.0005938308,0.00049473095,0.00013113221],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054320524,0.0003025438,0.00027638918,0.00042096985,0.00055550574,0.00014705877,0.0009887295,0.00033691974,0.000006146784],"category_scores_gemma":[0.000046982288,0.00038185754,0.0002742656,0.0011638008,0.00004823728,0.00044549172,0.00086422666,0.00050002226,0.000049499446],"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":[0.000036152473,0.00042732057,0.0001784235,0.0003367558,0.00031153994,0.000028711553,0.0003517728,0.27771205,0.010959136,0.1925243,0.00056424906,0.5165696],"study_design_scores_gemma":[0.00026369834,0.00009946251,0.0005452817,0.000016387725,0.000073194904,0.000011516235,0.000041196035,0.9260908,0.00046466477,0.07163085,0.00039188622,0.00037106618],"about_ca_topic_score_codex":0.00051850046,"about_ca_topic_score_gemma":0.00015120498,"teacher_disagreement_score":0.64837873,"about_ca_system_score_codex":0.00053676905,"about_ca_system_score_gemma":0.0003748086,"threshold_uncertainty_score":0.9998633},"labels":[],"label_agreement":null},{"id":"W4387265244","doi":"10.3390/biomimetics8060470","title":"Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Benchmark (surveying); Metaheuristic; Algorithm; Test suite; Computer science; Suite; Mathematical optimization; Engineering optimization; Test functions for optimization; Imperialist competitive algorithm; Optimization algorithm; Parallel metaheuristic; Evolutionary algorithm; Optimization problem; Test case; Artificial intelligence; Machine learning; Mathematics; Meta-optimization; Multi-swarm optimization","score_opus":0.027581158289016614,"score_gpt":0.25943459655944207,"score_spread":0.23185343827042545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387265244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012010652,0.0002510133,0.9954329,0.0016964141,0.0005839007,0.00097569975,0.00006780036,0.0009053088,0.00007491638],"genre_scores_gemma":[0.0003048585,0.00023452949,0.9971145,0.00011531657,0.00035351372,0.0002490379,0.0003921965,0.000045096385,0.0011909723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791,0.00003654321,0.0005112661,0.000672123,0.0003562806,0.0005137591],"domain_scores_gemma":[0.99843,0.00018466669,0.00022615274,0.0006333567,0.00030509438,0.00022072121],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003739616,0.0002672985,0.0002634789,0.0003882443,0.00047297686,0.00029073146,0.00076950586,0.00015246464,0.000020220343],"category_scores_gemma":[0.00008784413,0.0002770173,0.00014522631,0.0021756473,0.00006211199,0.0004978485,0.0002447053,0.00009606663,0.000078336656],"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":[0.0000015733107,0.0001306735,0.000006453268,0.000022529859,0.00006469586,0.000002992437,0.00020053942,0.7445437,0.00029316838,0.0059672077,0.008549496,0.24021699],"study_design_scores_gemma":[0.00067310076,0.000107027976,0.000034612112,0.000024824534,0.000048979295,0.000009020434,0.000018334322,0.9866973,0.00047599684,0.0029917534,0.008579234,0.0003398101],"about_ca_topic_score_codex":0.000037313686,"about_ca_topic_score_gemma":8.5057076e-7,"teacher_disagreement_score":0.24215361,"about_ca_system_score_codex":0.000094046096,"about_ca_system_score_gemma":0.00019587456,"threshold_uncertainty_score":0.9999682},"labels":[],"label_agreement":null},{"id":"W4388134984","doi":"10.1007/978-981-99-3814-8_4","title":"Evolutionary Computation and the Reinforcement Learning Problem","year":2023,"lang":"en","type":"book-chapter","venue":"Genetic 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Observability; Population; Adaptation (eye); Action selection; Evolutionary computation; Evolutionary robotics; Selection (genetic algorithm); Natural selection; Task (project management); Machine learning; Engineering; Mathematics; Agency (philosophy); Biology","score_opus":0.014655601929874309,"score_gpt":0.22384900742274824,"score_spread":0.20919340549287394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388134984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000086964814,0.0048455405,0.95661724,0.0036193565,0.00031683943,0.0011879377,0.000012353899,0.0004579875,0.032855783],"genre_scores_gemma":[0.12412076,0.012582714,0.46586052,0.00084715383,0.0015010763,0.0007202566,0.001884705,0.00025684747,0.39222598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975516,0.00010240463,0.0006484015,0.00078925496,0.0005876486,0.00032065233],"domain_scores_gemma":[0.9984682,0.00043373884,0.00040952256,0.00027209453,0.00028142362,0.00013501525],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035136926,0.00039984533,0.00035195655,0.00025403296,0.0011663572,0.0001510725,0.00031847003,0.000232881,0.000012635566],"category_scores_gemma":[0.000018500024,0.00035185632,0.00011491518,0.00019084784,0.0004880383,0.00027284035,0.00055739057,0.00044723242,0.00014403767],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002590871,0.000018105817,0.00003891958,0.00006943835,0.0001162843,0.000007307347,0.00041567034,0.3231543,0.0000026831392,0.6241591,0.008087571,0.043904718],"study_design_scores_gemma":[0.00071405945,0.00008751027,0.004986458,0.00007179086,0.000051788065,0.000116825395,0.000025047482,0.63773245,1.2012184e-7,0.3460229,0.009892142,0.00029892687],"about_ca_topic_score_codex":0.000041435862,"about_ca_topic_score_gemma":0.000002888121,"teacher_disagreement_score":0.49075672,"about_ca_system_score_codex":0.00013629664,"about_ca_system_score_gemma":0.00018463077,"threshold_uncertainty_score":0.99989337},"labels":[],"label_agreement":null},{"id":"W4388486995","doi":"10.1007/s10710-023-09462-2","title":"Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search","year":2023,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Johannes Gutenberg-Universität Mainz; Deutscher Akademischer Austauschdienst","keywords":"Computer science; Genetic programming; Autoencoder; Artificial intelligence; Machine learning; Sampling (signal processing); Probabilistic logic; Mathematical optimization; Artificial neural network; Mathematics","score_opus":0.023510881475824343,"score_gpt":0.2816448645220405,"score_spread":0.25813398304621615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388486995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28721106,0.00090218533,0.7089728,0.0018938909,0.00006345218,0.0006532819,0.0000017079517,0.0002790773,0.000022499213],"genre_scores_gemma":[0.66607237,0.00012754738,0.33311117,0.00004550729,0.000060350612,0.00048226805,0.000008689607,0.000014299584,0.000077833414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840474,0.00007790051,0.00029000716,0.0005299625,0.0002447189,0.00045266462],"domain_scores_gemma":[0.99937016,0.000086206644,0.00004663682,0.00025701162,0.0000919066,0.00014809908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037274027,0.00017627157,0.00016972567,0.00030588623,0.0003299161,0.0006305034,0.0002294503,0.000059057904,0.0000016028788],"category_scores_gemma":[0.000026741642,0.00017083353,0.00002370274,0.001061764,0.00006270969,0.0004994241,0.0001498932,0.00010954442,0.000017494327],"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":[0.00000817447,0.00007424298,0.01030846,0.00007421335,0.000013729394,0.000013885077,0.004394839,0.0519958,0.00067344774,0.0027793038,0.00007513156,0.9295888],"study_design_scores_gemma":[0.00050413096,0.00020210426,0.10698988,0.00004580748,0.000010241102,0.000023835926,0.0012348697,0.8782941,0.00003377464,0.011005976,0.0013861516,0.00026911785],"about_ca_topic_score_codex":0.00031545857,"about_ca_topic_score_gemma":0.00012170014,"teacher_disagreement_score":0.9293197,"about_ca_system_score_codex":0.000027037811,"about_ca_system_score_gemma":0.00007362265,"threshold_uncertainty_score":0.6966389},"labels":[],"label_agreement":null},{"id":"W4388912745","doi":"10.1007/s10710-023-09473-z","title":"W. B. Langdon “Jaws 30”","year":2023,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Bioethics Society","funders":"","keywords":"Computer science; Genetic programming; Coevolution; Field (mathematics); Order (exchange); Field-programmable gate array; Computer graphics (images); Artificial intelligence; Operating system; Biology; Mathematics; Evolutionary biology","score_opus":0.01232736480788241,"score_gpt":0.24722457078725016,"score_spread":0.23489720597936775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388912745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31114483,0.0056359335,0.6678851,0.007305944,0.0006719319,0.00078702084,0.000012286364,0.0033489512,0.0032079804],"genre_scores_gemma":[0.501178,0.00038729253,0.493763,0.00020464997,0.0003211713,0.0002783752,0.000030849456,0.000025392526,0.0038112453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914205,0.000018943518,0.00012556704,0.00029903135,0.00013175042,0.0002826286],"domain_scores_gemma":[0.9995125,0.00003803593,0.000031172603,0.00029890877,0.000032849995,0.00008651829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014837962,0.00010168402,0.00009201916,0.00008004163,0.00027393026,0.00014261181,0.0002962606,0.00003619928,0.000012151428],"category_scores_gemma":[0.000011844119,0.00008857622,0.000029575778,0.0005316381,0.000038819562,0.00009230698,0.00020581049,0.00006499736,0.00021368092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.932155e-7,0.000047766174,0.009369572,0.00003681154,0.000013972124,0.000011599642,0.00031356286,0.00040015494,0.00022612944,0.009855318,0.0050399085,0.9746843],"study_design_scores_gemma":[0.0003164014,0.000100433856,0.09665469,0.000021778738,0.000011693931,0.000052448744,0.000059136186,0.68661547,0.00006123783,0.020667344,0.19510289,0.00033650806],"about_ca_topic_score_codex":0.00010629796,"about_ca_topic_score_gemma":0.0000056543827,"teacher_disagreement_score":0.97434783,"about_ca_system_score_codex":0.000007632238,"about_ca_system_score_gemma":0.000020670564,"threshold_uncertainty_score":0.36120334},"labels":[],"label_agreement":null},{"id":"W4389976716","doi":"10.1007/s11042-023-17167-y","title":"PyGAD: an intuitive genetic algorithm Python library","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":198,"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 Ottawa","funders":"","keywords":"Computer science; Python (programming language); Programming language; Algorithm","score_opus":0.01994590268256709,"score_gpt":0.25662432333338914,"score_spread":0.23667842065082204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389976716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028401532,0.00020609003,0.9915419,0.0022361223,0.000070171714,0.000738308,0.0002098232,0.0010062746,0.0011511091],"genre_scores_gemma":[0.017200524,0.0007724875,0.9758542,0.0004895904,0.00074804155,0.0028324367,0.0006920052,0.000043532804,0.0013671779],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987967,0.000033059234,0.00021234654,0.00053084525,0.00015957573,0.0002674805],"domain_scores_gemma":[0.9989356,0.00014178644,0.000059683618,0.0005951944,0.000045348486,0.00022241603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008471445,0.00014349191,0.00011967597,0.00012068609,0.0003826273,0.0002433475,0.00054320256,0.00007241785,0.0000220801],"category_scores_gemma":[0.000008172401,0.0001431482,0.000038917045,0.0009418431,0.00010580161,0.0006939286,0.0002939976,0.00011630307,0.0003419695],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.8449806e-7,0.0000907275,0.00017079127,0.0000043793734,0.000007620816,0.000002233778,0.00025039713,0.000072416515,0.0003232057,0.024703661,0.0021823612,0.97219175],"study_design_scores_gemma":[0.000324977,0.00006505878,0.09181871,0.000008755201,0.000010428351,0.000021410155,0.0001406131,0.6702369,0.0004346675,0.025624694,0.2109598,0.00035400165],"about_ca_topic_score_codex":0.00001020615,"about_ca_topic_score_gemma":0.0000010464777,"teacher_disagreement_score":0.9718377,"about_ca_system_score_codex":0.000010379054,"about_ca_system_score_gemma":0.00006213655,"threshold_uncertainty_score":0.5837414},"labels":[],"label_agreement":null},{"id":"W4391058219","doi":"10.1016/j.eswa.2024.123289","title":"Covariance matrix adaptation evolution strategy based on correlated evolution paths with application to reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":33,"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":"CMA-ES; Reinforcement learning; Benchmark (surveying); Evolution strategy; Computer science; Suite; Covariance matrix; Population; Mathematical optimization; Algorithm; Adaptation (eye); Path (computing); Covariance; Artificial intelligence; Evolutionary algorithm; Mathematics","score_opus":0.009727315906889298,"score_gpt":0.24972470431190918,"score_spread":0.23999738840501988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391058219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007459837,0.0011107503,0.9911848,0.0012294557,0.00014545467,0.0039339317,0.00001232568,0.0011291908,0.0011795273],"genre_scores_gemma":[0.9421569,0.000014750615,0.040230025,0.00006701666,0.00028572752,0.016276188,0.00016786563,0.000047428097,0.00075411564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714893,0.00010644924,0.0005442497,0.0010456019,0.0007398242,0.0004149286],"domain_scores_gemma":[0.9979071,0.00018791087,0.00022473211,0.0010401203,0.00040567224,0.00023444062],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003847488,0.0003441669,0.00023899926,0.00039383778,0.0006922241,0.00037680217,0.000549923,0.00012258912,0.000006928048],"category_scores_gemma":[0.000014103786,0.0002877694,0.000056443252,0.0025348528,0.00005929842,0.0006021142,0.000049311722,0.00034534524,0.0004934973],"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":[0.000025464187,0.00007650411,0.00002683749,0.000038764185,0.000017407885,0.0000016374519,0.0002180624,0.6706179,0.0004174014,0.3232444,0.00053324614,0.004782416],"study_design_scores_gemma":[0.00029635392,0.00041047702,0.0004233003,0.0003512377,0.000017789405,0.000037599973,0.00028045246,0.95274746,0.000030445002,0.00039005096,0.04461827,0.0003965759],"about_ca_topic_score_codex":0.0003969217,"about_ca_topic_score_gemma":0.000020343821,"teacher_disagreement_score":0.95095474,"about_ca_system_score_codex":0.0008579683,"about_ca_system_score_gemma":0.000573383,"threshold_uncertainty_score":0.99995744},"labels":[],"label_agreement":null},{"id":"W4391307056","doi":"10.1109/smc53992.2023.10394281","title":"Feature Selection Using Evolutionary Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Machine learning","score_opus":0.019652971092212472,"score_gpt":0.27912994831970434,"score_spread":0.25947697722749186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002024814,0.000032923523,0.98733073,0.0053341268,0.000077825774,0.00011841233,0.0000015560715,0.001901387,0.0031782317],"genre_scores_gemma":[0.059092127,0.000026128571,0.93255067,0.00024047046,0.00021930177,0.000055650125,0.000012933139,0.00000858042,0.007794115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994505,0.000013852036,0.000058270816,0.00020297282,0.00012745107,0.00014694681],"domain_scores_gemma":[0.9996816,0.000020266274,0.000021916787,0.00017677018,0.000064172265,0.000035278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008028374,0.00005824151,0.000044910714,0.00012007997,0.00025379984,0.000035716974,0.00024294342,0.000048451595,0.000012147841],"category_scores_gemma":[0.0000052971777,0.000054150594,0.00003093478,0.0013294972,0.000016306858,0.00035079694,0.00011410347,0.000074141106,0.00011620038],"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":[0.0000013181791,0.00007258503,0.0010225086,0.000008370057,0.000014571332,0.0000049484324,0.00007482468,0.0014778782,0.025941035,0.62983793,0.30640906,0.03513496],"study_design_scores_gemma":[0.000035778805,0.000017189119,0.0062542926,0.00000503991,0.0000017508677,0.000050021117,0.000011623329,0.9043311,0.0024571258,0.015742142,0.070981786,0.00011214142],"about_ca_topic_score_codex":0.00001804927,"about_ca_topic_score_gemma":0.0000015171624,"teacher_disagreement_score":0.90285325,"about_ca_system_score_codex":0.000052476877,"about_ca_system_score_gemma":0.000050568462,"threshold_uncertainty_score":0.22081971},"labels":[],"label_agreement":null},{"id":"W4391307601","doi":"10.1109/smc53992.2023.10393938","title":"A Pairwise Surrogate Model using GNN for Evolutionary Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Pairwise comparison; Surrogate model; Computer science; Mathematical optimization; Evolutionary algorithm; Evolutionary computation; Artificial intelligence; Mathematics; Machine learning","score_opus":0.0519913286415911,"score_gpt":0.2885033772274616,"score_spread":0.2365120485858705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307601","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011319151,0.000025199151,0.9957875,0.0016791389,0.00008026701,0.00026704033,0.000016150327,0.00055662927,0.00045617527],"genre_scores_gemma":[0.030389598,0.000016711749,0.9674272,0.00013167413,0.000056765824,0.00012125419,0.000048625683,0.000009420938,0.0017987379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922836,0.0000118643375,0.00014632533,0.00027261846,0.00013228322,0.00020857043],"domain_scores_gemma":[0.9994364,0.00006198873,0.000038443926,0.0002803859,0.00012473906,0.000058050417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015297887,0.0000767115,0.00006864171,0.00010610441,0.00030450235,0.00004181282,0.0002979665,0.000038833186,0.000008418891],"category_scores_gemma":[0.000015739679,0.000075831405,0.000058011712,0.0006764842,0.000021184427,0.0004583036,0.00012891396,0.000032531905,0.000035386893],"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":[8.846837e-7,0.000022363092,0.000016495158,0.0000036319507,0.0000033319604,2.9107608e-7,0.000033457363,0.9063213,0.00011246223,0.08868831,0.0041795317,0.000617964],"study_design_scores_gemma":[0.00016032258,0.000010893504,0.00010498695,0.0000038552967,0.0000032818973,0.0000035657245,0.000012438495,0.97187495,0.000031879903,0.027121989,0.00057209394,0.000099732286],"about_ca_topic_score_codex":0.000011511927,"about_ca_topic_score_gemma":9.560146e-7,"teacher_disagreement_score":0.06555368,"about_ca_system_score_codex":0.000049671078,"about_ca_system_score_gemma":0.00010686443,"threshold_uncertainty_score":0.3092315},"labels":[],"label_agreement":null},{"id":"W4391339190","doi":"10.1016/j.engappai.2023.107426","title":"Incremental reinforcement learning for multi-objective analog circuit design acceleration","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; CMC Microsystems","keywords":"Computer science; Reinforcement learning; Electronic circuit; Process (computing); Analogue electronics; Circuit design; Computer engineering; Artificial intelligence; Embedded system; Electrical engineering","score_opus":0.06568879306288473,"score_gpt":0.303612491369805,"score_spread":0.23792369830692028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391339190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001522903,0.00022260961,0.998227,0.00010031089,0.000095381234,0.00082776375,0.0000038935063,0.0003207509,0.000049985043],"genre_scores_gemma":[0.6744414,0.000021863836,0.32429576,0.0000066790867,0.00008122075,0.0010679509,0.00001582209,0.000010664915,0.000058654423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989542,0.000012351281,0.00037064808,0.00033713278,0.00014758334,0.00017807826],"domain_scores_gemma":[0.9992673,0.00021288509,0.00006020503,0.00026824474,0.00014091274,0.000050499973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032087334,0.00011912843,0.00010526726,0.0001979397,0.00017751292,0.000115468574,0.0004089574,0.000048103837,0.000009801706],"category_scores_gemma":[0.000043818854,0.0001325384,0.00007041311,0.0007219671,0.000031280033,0.00031252674,0.00006452509,0.00013054941,0.00003854461],"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":[0.0000011385772,0.000030405667,0.0000012174596,0.00002470232,0.000017212447,1.4002889e-7,0.00025500328,0.52916366,0.013771893,0.39495122,0.000022703982,0.061760712],"study_design_scores_gemma":[0.000013177745,0.000056628483,0.000029502995,0.000022349526,0.000008492929,0.0000021805392,0.000058011497,0.92066413,0.07188021,0.00591681,0.0012255823,0.00012291138],"about_ca_topic_score_codex":0.000023458158,"about_ca_topic_score_gemma":0.0000013601176,"teacher_disagreement_score":0.6742891,"about_ca_system_score_codex":0.00012212039,"about_ca_system_score_gemma":0.00008638956,"threshold_uncertainty_score":0.5404759},"labels":[],"label_agreement":null},{"id":"W4391341579","doi":"10.1109/tevc.2024.3349664","title":"IEEE Transactions on Evolutionary Computation Publication Information","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Southern University of Science and Technology; Department of Artificial Intelligence, Korea University; Zhengzhou University; Xiamen University; Brock University; Aberystwyth University; South China University of Technology; Shenzhen University; Dalhousie University; Università degli Studi di Trento; Victoria University; Griffith University; Victoria University of Wellington; University of Exeter","keywords":"Evolutionary computation; Computer science; Computation; Evolutionary algorithm; Theoretical computer science; Artificial intelligence; Algorithm","score_opus":0.014521498867787738,"score_gpt":0.25264165059608495,"score_spread":0.2381201517282972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391341579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000890077,0.0002204466,0.9836889,0.0061016167,0.003709113,0.001183919,0.0003325095,0.0023922892,0.0014811251],"genre_scores_gemma":[0.9303535,0.00012283637,0.066461064,0.0008390408,0.0002305284,0.0007088138,0.0004043272,0.000059854305,0.00082002813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99534726,0.00028733152,0.0012125736,0.001136529,0.0013417884,0.00067454024],"domain_scores_gemma":[0.99716777,0.00064747693,0.00028535828,0.00070226495,0.0008640623,0.0003330743],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046088253,0.0006261574,0.00036904693,0.0018045302,0.0016146761,0.00066341413,0.00072039594,0.00037002363,0.00012952162],"category_scores_gemma":[0.000011148105,0.00069100613,0.00043442613,0.003197609,0.00019977748,0.0062843505,0.0000042423085,0.00093176507,0.0028219216],"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":[0.00005516522,0.00077427604,0.000001720518,0.00008289774,0.0001310996,0.0000067340684,0.00055181497,0.74841624,0.00021780522,0.01303117,0.015408049,0.221323],"study_design_scores_gemma":[0.0007098296,0.00043649023,0.0008158199,0.00015694446,0.00007243834,0.00018003568,0.000088351415,0.97813153,0.0007933328,0.009890462,0.008018747,0.00070602336],"about_ca_topic_score_codex":0.00008028214,"about_ca_topic_score_gemma":0.0000081708795,"teacher_disagreement_score":0.92946345,"about_ca_system_score_codex":0.0012576557,"about_ca_system_score_gemma":0.0006557524,"threshold_uncertainty_score":0.9996851},"labels":[],"label_agreement":null},{"id":"W4391890201","doi":"10.1007/978-3-031-49295-2_4","title":"Size Optimization","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadore College","funders":"","keywords":"Computer science","score_opus":0.01184608861927017,"score_gpt":0.2223375410829743,"score_spread":0.21049145246370413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391890201","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.3153636e-9,0.00022768126,0.4696705,0.0012824776,0.00013029044,0.000058548343,0.0000038952085,0.00021526619,0.5284113],"genre_scores_gemma":[0.000009075933,0.00010753865,0.24480356,0.00015674972,0.000130394,0.000009662013,0.000010425629,0.000012285401,0.7547603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992993,0.0000011953025,0.000137689,0.00033232776,0.00014712047,0.00008237995],"domain_scores_gemma":[0.99941164,0.000046113157,0.000036062385,0.00040997585,0.000052033127,0.000044180095],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000037690643,0.00012489755,0.000089824054,0.0000493298,0.000058406917,0.000102382306,0.0004020548,0.000112882895,0.00084279425],"category_scores_gemma":[0.0000023398172,0.00011183109,0.00007168953,0.000042454856,0.000021168438,0.00012263024,0.00019560201,0.00013488515,0.0015604951],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.796785e-8,0.000003009443,1.8251692e-8,0.0000048056568,0.000009195068,0.0000029766172,0.0000064731444,0.0017242419,5.658088e-7,0.9826046,0.0135553675,0.0020886573],"study_design_scores_gemma":[0.00001671342,0.000007658364,5.678862e-7,0.000018128727,0.0000065402614,0.000009200271,3.1939615e-7,0.21488129,0.0000015761601,0.31748518,0.46745345,0.00011940268],"about_ca_topic_score_codex":0.0000021651247,"about_ca_topic_score_gemma":7.107864e-7,"teacher_disagreement_score":0.66511947,"about_ca_system_score_codex":0.000035180918,"about_ca_system_score_gemma":0.000050285522,"threshold_uncertainty_score":0.9992169},"labels":[],"label_agreement":null},{"id":"W4392642750","doi":"10.5194/egusphere-egu24-22282","title":"A data-driven approach to understanding esker morphogenesis","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Morphogenesis; Computer science; Business","score_opus":0.1485162418992593,"score_gpt":0.31155008501879433,"score_spread":0.16303384311953503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392642750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000077124656,0.00023249169,0.955042,0.006014806,0.0004269795,0.00046040217,0.00024650068,0.0005090584,0.036990643],"genre_scores_gemma":[0.05550642,0.00003069614,0.940289,0.0003990945,0.00027639823,0.00024848216,0.00030765505,0.000026181533,0.002916077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779534,0.000026210451,0.00024789752,0.0013552764,0.0003198495,0.00025542386],"domain_scores_gemma":[0.9970575,0.000039177685,0.000048307073,0.0026644806,0.00003152942,0.00015902138],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00022536752,0.000223402,0.00020349899,0.00019452778,0.000137612,0.000465521,0.0033011371,0.00013418055,0.000019047538],"category_scores_gemma":[0.0000067868305,0.0002003942,0.000079684614,0.0005260979,0.000027328033,0.00013929163,0.015247243,0.00034575153,0.00043562904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.078103e-7,0.000075345146,0.0000037963437,0.000062965046,0.000061007475,0.0000036535214,0.00015123746,0.013726129,0.000021517775,0.92284536,0.062037874,0.0010107253],"study_design_scores_gemma":[0.000030122554,0.000005471785,0.000045653327,0.000032923494,0.000022028424,0.000012571452,0.00005509097,0.8606217,0.000007725063,0.12936442,0.009537877,0.0002644052],"about_ca_topic_score_codex":0.00008206319,"about_ca_topic_score_gemma":0.000010111407,"teacher_disagreement_score":0.8468956,"about_ca_system_score_codex":0.00023714664,"about_ca_system_score_gemma":0.00024333032,"threshold_uncertainty_score":0.99271727},"labels":[],"label_agreement":null},{"id":"W4392978796","doi":"10.1007/978-3-031-56852-7","title":"Applications of Evolutionary Computation","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"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 Institute of Information and Communications Technology; Universitat Politècnica de València; Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California; College of Engineering, Michigan State University; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Région Normandie; Technische Universität Dortmund; Universidade da Coruña; Universitat Oberta de Catalunya; Eötvös Loránd Tudományegyetem; University of South Africa; University of Tsukuba; Universidad de Granada; Syddansk Universitet; ITMO University; Universidade Nova de Lisboa; Universidade de Coimbra; Silesian University of Technology; Universidad Complutense de Madrid; Universität Bielefeld; Michigan State University; Newcastle University; Università degli Studi di Trento; Technische Universität Darmstadt; Hakim Sabzevari University; King's College London; Centre National de la Recherche Scientifique; Johannes Gutenberg-Universität Mainz; Université du Luxembourg; Università degli Studi di Milano; Università degli Studi di Firenze; Universitetet i Oslo; Universität zu Lübeck; Sorbonne Université; Università degli Studi di Salerno; Queensland University of Technology; Universidad Politécnica de Madrid; Universidad de Extremadura; Universidad de Guadalajara; Euskal Herriko Unibertsitatea; University of Twente; Hong Kong Baptist University; York University; Universiteit Leiden; Indian Council of Agricultural Research; Swansea University; Radboud Universiteit; Universidad Rey Juan Carlos; Edinburgh Napier University; Carl von Ossietzky Universität Oldenburg; University of Southampton; Iran Telecommunication Research Center; De Montfort University; Università per Stranieri di Perugia; University of Exeter; Universidad de Cádiz; Universidad de Málaga; University of Cape Town; Institut \"Jožef Stefan\"; Università degli Studi di Perugia; Heriot-Watt University; Pomona College; Ostravská Univerzita v Ostravě; Technische Universität Dresden; Politecnico di Torino; University of Central Florida; Yeditepe Üniversitesi; Tecnológico Nacional de México; České Vysoké Učení Technické v Praze; Università degli Studi di Parma; Universidad de Sevilla; Sveučilište u Zagrebu; Queen Mary University of London; Universität Basel","keywords":"Computer science; Evolutionary computation; Computation; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.009796253796092282,"score_gpt":0.258472270848703,"score_spread":0.24867601705261072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392978796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000498682,0.0019592876,0.9924142,0.00091690803,0.00063974474,0.0005978786,0.000026328298,0.00020714494,0.003233548],"genre_scores_gemma":[0.022153437,0.00006400944,0.97511655,0.00028159178,0.0006566886,0.00015800117,0.000064907705,0.00003115244,0.0014736862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969896,0.000031056607,0.00058228127,0.0011926721,0.00082366343,0.00038070386],"domain_scores_gemma":[0.99790126,0.00039322468,0.0002476567,0.000983494,0.00036355687,0.00011079233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040491184,0.00031416496,0.00035380843,0.0009755345,0.00020976977,0.00017183162,0.002324962,0.00022086997,0.0000074105046],"category_scores_gemma":[0.000020134139,0.00030929418,0.00013385018,0.002498707,0.0006782316,0.00044279126,0.001008749,0.0005586162,0.00011289676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012665691,0.0001263546,0.00001802711,0.00017651767,0.000017347438,0.000010845938,0.00038889764,0.069296815,0.000068868576,0.09927969,0.0020554776,0.8285599],"study_design_scores_gemma":[0.00006469188,0.000041755415,0.00010137415,0.00015055142,0.000007182483,0.000033863344,1.2314634e-7,0.5660153,0.000079252335,0.42453483,0.008740837,0.00023021922],"about_ca_topic_score_codex":0.000014694488,"about_ca_topic_score_gemma":0.000007794738,"teacher_disagreement_score":0.8283297,"about_ca_system_score_codex":0.0004819642,"about_ca_system_score_gemma":0.0018589203,"threshold_uncertainty_score":0.9999359},"labels":[],"label_agreement":null},{"id":"W4393106948","doi":"10.1007/978-981-99-8413-8_10","title":"The OpenELM Library: Leveraging Progress in Language Models for Novel Evolutionary Algorithms","year":2024,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Implementation; Computer science; Leverage (statistics); Python (programming language); Genetic programming; Evolutionary algorithm; Artificial intelligence; Inference; Machine learning; Programming language; Data science","score_opus":0.020340674609139922,"score_gpt":0.24823851598543117,"score_spread":0.22789784137629127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393106948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007727089,0.075758696,0.9016662,0.004753434,0.00064582605,0.0015196129,0.00020797971,0.0003251738,0.015045795],"genre_scores_gemma":[0.020360487,0.0031882166,0.78734875,0.00032691832,0.0012348965,0.0010773614,0.0010735266,0.00019112235,0.18519872],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977352,0.000021609681,0.0005695047,0.0008978656,0.00037818577,0.00039761703],"domain_scores_gemma":[0.99890447,0.00027327595,0.0001845125,0.00040424464,0.000118498276,0.00011502561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001788487,0.0003847024,0.00027625661,0.000268536,0.00061893946,0.0002986783,0.000633381,0.00022762224,0.000006170717],"category_scores_gemma":[0.0000038685116,0.00034286862,0.00013822773,0.00020139676,0.00022585093,0.00061173673,0.0005330553,0.00032434482,0.000029812689],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014546503,0.000059194095,0.000024173243,0.000084061336,0.00007836459,0.000013570069,0.00041474117,0.03342266,0.0000027638196,0.86956394,0.007308127,0.089013845],"study_design_scores_gemma":[0.00025028022,0.00004485308,0.0013703754,0.00010269737,0.000021244326,0.00006736419,0.000032905282,0.6029698,4.466839e-7,0.36787394,0.027000949,0.00026510458],"about_ca_topic_score_codex":0.000011654175,"about_ca_topic_score_gemma":0.0000033993838,"teacher_disagreement_score":0.5695472,"about_ca_system_score_codex":0.00015620668,"about_ca_system_score_gemma":0.00033282078,"threshold_uncertainty_score":0.9999023},"labels":[],"label_agreement":null},{"id":"W4393106956","doi":"10.1007/978-981-99-8413-8_16","title":"Let’s Evolve Intelligence, Not Solutions","year":2024,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"John Abbott College","funders":"","keywords":"Computer science; Artificial intelligence; Framing (construction); Intelligence cycle; Data science; Knowledge management; Cognitive science; Management science; Engineering; Military intelligence; Psychology","score_opus":0.02750249213461001,"score_gpt":0.2428350913914425,"score_spread":0.21533259925683249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393106956","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013455444,0.014383423,0.88470304,0.0025997462,0.0009628272,0.0004040849,0.000090202404,0.00041408776,0.09642912],"genre_scores_gemma":[0.03525501,0.00569577,0.47112653,0.00060555636,0.0017853401,0.00021076402,0.0006731509,0.00016191039,0.48448598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997584,0.000020527676,0.0005706121,0.0009752813,0.00047729525,0.00037232143],"domain_scores_gemma":[0.998755,0.00016925242,0.00017808264,0.00046770094,0.00024282835,0.00018717822],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014255104,0.00042046094,0.0002981196,0.0003851939,0.00054282125,0.00017508998,0.00047732514,0.00030632422,0.000079773556],"category_scores_gemma":[0.000008548034,0.00045335706,0.00017606227,0.0001882718,0.0002485061,0.0002634708,0.00055783417,0.00041847225,0.001151424],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002601771,0.000027130895,0.0000021771634,0.000041546784,0.00007559823,0.000015245173,0.00010665916,0.0070129675,0.000006765764,0.9046084,0.017571801,0.07052909],"study_design_scores_gemma":[0.00006108546,0.00006772942,0.0007026928,0.000078712445,0.000058886355,0.00016716281,0.0000095358755,0.33867452,0.0000013271522,0.5338543,0.12594502,0.00037903854],"about_ca_topic_score_codex":0.000020050435,"about_ca_topic_score_gemma":0.000004581589,"teacher_disagreement_score":0.41357654,"about_ca_system_score_codex":0.00019630931,"about_ca_system_score_gemma":0.0002579576,"threshold_uncertainty_score":0.9997918},"labels":[],"label_agreement":null},{"id":"W4393107025","doi":"10.1007/978-981-99-8413-8_4","title":"How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions","year":2024,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Simple (philosophy); Neutral network; Genetic programming; Space (punctuation); Phenotype; Neutral theory of molecular evolution; Genetic algorithm; Exponential function; Key (lock); Computer science; Mathematics; Evolutionary biology; Biology; Mathematical optimization; Genetics; Artificial intelligence; Gene; Philosophy","score_opus":0.01903676189982518,"score_gpt":0.2339239389063061,"score_spread":0.21488717700648094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393107025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017561879,0.011720938,0.9724741,0.008109231,0.0005830624,0.001069861,0.00009482993,0.00017073365,0.004021067],"genre_scores_gemma":[0.66988474,0.0011292739,0.20534164,0.0002513264,0.00088297436,0.0003080008,0.00029257537,0.0001272298,0.12178227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981963,0.000029534427,0.0003929795,0.0006423363,0.0003979218,0.0003409258],"domain_scores_gemma":[0.9988669,0.00012569859,0.0001981472,0.00044517824,0.00022427198,0.00013978174],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011329701,0.0003284298,0.0002737524,0.00021554551,0.0004916551,0.0002764597,0.0004978769,0.000161827,0.0000043278847],"category_scores_gemma":[0.00000797041,0.00028012114,0.00014642238,0.00028360166,0.00024653864,0.00018818243,0.0005042303,0.00026398938,0.000036276353],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003817631,0.00005801131,0.00007018571,0.00009779677,0.00009179433,0.0000072810235,0.00034524917,0.025468519,0.000024517949,0.93294775,0.00944238,0.03144272],"study_design_scores_gemma":[0.00020368652,0.00031012727,0.011257872,0.00011163524,0.00015475263,0.00012753253,0.00007369699,0.14127609,0.00000652697,0.7079116,0.13801786,0.00054859853],"about_ca_topic_score_codex":0.000034052493,"about_ca_topic_score_gemma":0.000014872408,"teacher_disagreement_score":0.76713246,"about_ca_system_score_codex":0.00010968913,"about_ca_system_score_gemma":0.00020134817,"threshold_uncertainty_score":0.9999651},"labels":[],"label_agreement":null},{"id":"W4393107140","doi":"10.1007/978-981-99-8413-8","title":"Genetic Programming Theory and Practice XX","year":2024,"lang":"en","type":"book","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","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":"Queen's University","funders":"","keywords":"Genetic programming; Field (mathematics); Computer science; Artificial intelligence; Mathematics","score_opus":0.010807876526377816,"score_gpt":0.2554978167080033,"score_spread":0.2446899401816255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393107140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011382897,0.058145586,0.92190665,0.0014806065,0.00039509832,0.00061651116,0.000020868572,0.00030202017,0.0170188],"genre_scores_gemma":[0.0031232762,0.002973544,0.89297736,0.00043167453,0.00081983116,0.00017315581,0.00014765424,0.00006627119,0.09928724],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979271,0.0001859505,0.00039412398,0.0008555983,0.00035850776,0.0002786872],"domain_scores_gemma":[0.9984829,0.0006825379,0.00018199933,0.00030859874,0.00019135297,0.00015263997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003774612,0.00032072584,0.0002285206,0.00021264213,0.00037223907,0.00025195003,0.0002691699,0.00022542672,0.000008531163],"category_scores_gemma":[0.00005082046,0.0003326364,0.000065501066,0.00021349454,0.00022397739,0.00029697994,0.00041923628,0.0003073457,0.00011426108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002023842,0.000093403265,0.000017242146,0.00025017437,0.00018553172,0.00006932826,0.0007376755,0.0024381117,0.000009720214,0.33278632,0.049485456,0.6139068],"study_design_scores_gemma":[0.00019711429,0.00015136435,0.005790368,0.0001245849,0.00017131951,0.0014542781,0.000055729248,0.117046006,2.3308931e-7,0.5955183,0.27901155,0.00047915548],"about_ca_topic_score_codex":0.00000795451,"about_ca_topic_score_gemma":9.60643e-7,"teacher_disagreement_score":0.61342764,"about_ca_system_score_codex":0.00013505328,"about_ca_system_score_gemma":0.00042385262,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W4393121770","doi":"10.1007/978-3-031-56855-8_21","title":"Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.012782275775669616,"score_gpt":0.2618745035610549,"score_spread":0.24909222778538528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393121770","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004088731,0.0009450917,0.9949943,0.00069133035,0.00066677405,0.00088464754,0.000024675368,0.00010136537,0.0012829759],"genre_scores_gemma":[0.35622862,0.00007510921,0.64192945,0.00041920217,0.0005168308,0.0002433471,0.000014965059,0.000048485592,0.00052399014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976743,0.000012384614,0.00027141572,0.0012073263,0.00041031826,0.00042424529],"domain_scores_gemma":[0.99865144,0.00050672656,0.00009399261,0.0005305069,0.00012072043,0.00009659401],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053403707,0.0003259956,0.00030145422,0.00061657623,0.00023844201,0.0007741904,0.0013466137,0.00018843984,0.0000037608],"category_scores_gemma":[0.00001930532,0.00030551205,0.00008012792,0.00046845333,0.00051723974,0.0007519401,0.00097153307,0.00048285737,0.0000074485915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035676228,0.00004104781,0.00012309782,0.00007213601,0.000011595209,0.00006667088,0.0014416411,0.010262964,0.000109445944,0.67013747,0.000053843833,0.31767654],"study_design_scores_gemma":[0.00019846343,0.0001271003,0.001203722,0.00027274626,0.000009989905,0.00003280356,0.0000021673522,0.38103986,0.000044335156,0.6162703,0.00045285522,0.00034565575],"about_ca_topic_score_codex":0.000053973952,"about_ca_topic_score_gemma":0.00022765766,"teacher_disagreement_score":0.37077692,"about_ca_system_score_codex":0.0002747075,"about_ca_system_score_gemma":0.00031104576,"threshold_uncertainty_score":0.9999397},"labels":[],"label_agreement":null},{"id":"W4393121851","doi":"10.1007/978-3-031-56855-8","title":"Applications of Evolutionary Computation","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"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 Institute of Information and Communications Technology; Universitat Politècnica de València; Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California; College of Engineering, Michigan State University; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Région Normandie; Technische Universität Dortmund; Universidade da Coruña; Universitat Oberta de Catalunya; Eötvös Loránd Tudományegyetem; University of South Africa; University of Tsukuba; Universidad de Granada; Syddansk Universitet; ITMO University; Universidade Nova de Lisboa; Universidade de Coimbra; Silesian University of Technology; Universidad Complutense de Madrid; Universität Bielefeld; Michigan State University; Newcastle University; Università degli Studi di Trento; Technische Universität Darmstadt; Hakim Sabzevari University; King's College London; Centre National de la Recherche Scientifique; Johannes Gutenberg-Universität Mainz; Université du Luxembourg; Università degli Studi di Milano; Università degli Studi di Firenze; Universitetet i Oslo; Universität zu Lübeck; Sorbonne Université; Università degli Studi di Salerno; Queensland University of Technology; Universidad Politécnica de Madrid; Universidad de Extremadura; Universidad de Guadalajara; Euskal Herriko Unibertsitatea; University of Twente; Hong Kong Baptist University; York University; Universiteit Leiden; Indian Council of Agricultural Research; Swansea University; Radboud Universiteit; Universidad Rey Juan Carlos; Edinburgh Napier University; Carl von Ossietzky Universität Oldenburg; University of Southampton; Iran Telecommunication Research Center; De Montfort University; Università per Stranieri di Perugia; University of Exeter; Universidad de Cádiz; Universidad de Málaga; University of Cape Town; Institut \"Jožef Stefan\"; Università degli Studi di Perugia; Heriot-Watt University; Pomona College; Ostravská Univerzita v Ostravě; Technische Universität Dresden; Politecnico di Torino; University of Central Florida; Yeditepe Üniversitesi; Tecnológico Nacional de México; České Vysoké Učení Technické v Praze; Università degli Studi di Parma; Universidad de Sevilla; Sveučilište u Zagrebu; Queen Mary University of London; Universität Basel","keywords":"Computer science; Evolutionary computation; Computation; Theoretical computer science; Artificial intelligence; Algorithm","score_opus":0.009796253796092282,"score_gpt":0.258472270848703,"score_spread":0.24867601705261072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393121851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000498682,0.0019592876,0.9924142,0.00091690803,0.00063974474,0.0005978786,0.000026328298,0.00020714494,0.003233548],"genre_scores_gemma":[0.022153437,0.00006400944,0.97511655,0.00028159178,0.0006566886,0.00015800117,0.000064907705,0.00003115244,0.0014736862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969896,0.000031056607,0.00058228127,0.0011926721,0.00082366343,0.00038070386],"domain_scores_gemma":[0.99790126,0.00039322468,0.0002476567,0.000983494,0.00036355687,0.00011079233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040491184,0.00031416496,0.00035380843,0.0009755345,0.00020976977,0.00017183162,0.002324962,0.00022086997,0.0000074105046],"category_scores_gemma":[0.000020134139,0.00030929418,0.00013385018,0.002498707,0.0006782316,0.00044279126,0.001008749,0.0005586162,0.00011289676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012665691,0.0001263546,0.00001802711,0.00017651767,0.000017347438,0.000010845938,0.00038889764,0.069296815,0.000068868576,0.09927969,0.0020554776,0.8285599],"study_design_scores_gemma":[0.00006469188,0.000041755415,0.00010137415,0.00015055142,0.000007182483,0.000033863344,1.2314634e-7,0.5660153,0.000079252335,0.42453483,0.008740837,0.00023021922],"about_ca_topic_score_codex":0.000014694488,"about_ca_topic_score_gemma":0.000007794738,"teacher_disagreement_score":0.8283297,"about_ca_system_score_codex":0.0004819642,"about_ca_system_score_gemma":0.0018589203,"threshold_uncertainty_score":0.9999359},"labels":[],"label_agreement":null},{"id":"W4393393641","doi":"10.1109/tevc.2024.3377800","title":"IEEE Transactions on Evolutionary Computation Publication Information","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Southern University of Science and Technology; Department of Artificial Intelligence, Korea University; Zhengzhou University; Xiamen University; Brock University; Aberystwyth University; South China University of Technology; Shenzhen University; Dalhousie University; Università degli Studi di Trento; Victoria University; Griffith University; Victoria University of Wellington; University of Exeter","keywords":"Evolutionary computation; Computer science; Computation; Evolutionary algorithm; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.014521498867787738,"score_gpt":0.25264165059608495,"score_spread":0.2381201517282972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393393641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000890077,0.0002204466,0.9836889,0.0061016167,0.003709113,0.001183919,0.0003325095,0.0023922892,0.0014811251],"genre_scores_gemma":[0.9303535,0.00012283637,0.066461064,0.0008390408,0.0002305284,0.0007088138,0.0004043272,0.000059854305,0.00082002813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99534726,0.00028733152,0.0012125736,0.001136529,0.0013417884,0.00067454024],"domain_scores_gemma":[0.99716777,0.00064747693,0.00028535828,0.00070226495,0.0008640623,0.0003330743],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046088253,0.0006261574,0.00036904693,0.0018045302,0.0016146761,0.00066341413,0.00072039594,0.00037002363,0.00012952162],"category_scores_gemma":[0.000011148105,0.00069100613,0.00043442613,0.003197609,0.00019977748,0.0062843505,0.0000042423085,0.00093176507,0.0028219216],"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":[0.00005516522,0.00077427604,0.000001720518,0.00008289774,0.0001310996,0.0000067340684,0.00055181497,0.74841624,0.00021780522,0.01303117,0.015408049,0.221323],"study_design_scores_gemma":[0.0007098296,0.00043649023,0.0008158199,0.00015694446,0.00007243834,0.00018003568,0.000088351415,0.97813153,0.0007933328,0.009890462,0.008018747,0.00070602336],"about_ca_topic_score_codex":0.00008028214,"about_ca_topic_score_gemma":0.0000081708795,"teacher_disagreement_score":0.92946345,"about_ca_system_score_codex":0.0012576557,"about_ca_system_score_gemma":0.0006557524,"threshold_uncertainty_score":0.9996851},"labels":[],"label_agreement":null},{"id":"W4393801284","doi":"10.5281/zenodo.3601563","title":"A Large Scale Empirical Study of the Impact of Spaghetti Code and Blob Anti-patterns on Program Comprehension","year":2020,"lang":"en","type":"dataset","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Polytechnique Montréal; Concordia University","funders":"","keywords":"Code (set theory); Comprehension; Program comprehension; Scale (ratio); Computer science; Empirical research; Programming language; Geography; Mathematics; Statistics; Cartography; Software","score_opus":0.018941593472656233,"score_gpt":0.3008489220331326,"score_spread":0.2819073285604764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393801284","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28855112,0.00021519198,0.034611106,0.0016097025,0.00007318168,0.0032196753,0.6714519,0.00026245517,0.00000563673],"genre_scores_gemma":[0.8709843,0.00030724393,0.027694147,0.0009618048,0.00023074166,0.0014480307,0.09825093,0.000087698325,0.000035103796],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9972911,0.00026071488,0.0006488349,0.00073026074,0.000608991,0.00046009468],"domain_scores_gemma":[0.9972241,0.00013584881,0.00059087743,0.0016680832,0.00014640854,0.00023471913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002931836,0.0004323735,0.00068702095,0.0002588299,0.00025050426,0.000102704194,0.0017355392,0.0002986284,0.000006984907],"category_scores_gemma":[0.000044647153,0.00031403656,0.00028868558,0.0008957628,0.00011548696,0.00014929168,0.0013803978,0.0007178989,0.000005061947],"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":[0.00004420095,0.008388863,0.047990736,0.0001323671,0.00017664679,0.000020263655,0.00048486065,0.0009570511,0.00023113213,0.0002342093,0.938809,0.0025306614],"study_design_scores_gemma":[0.0012847127,0.0029920775,0.7586729,0.00024897946,0.00012131061,0.000069195914,0.00014452412,0.19966249,0.0002763847,0.00029393303,0.03562315,0.0006103296],"about_ca_topic_score_codex":0.00521753,"about_ca_topic_score_gemma":0.0016468524,"teacher_disagreement_score":0.90318584,"about_ca_system_score_codex":0.0001458183,"about_ca_system_score_gemma":0.00025948332,"threshold_uncertainty_score":0.99993116},"labels":[],"label_agreement":null},{"id":"W4394818527","doi":"10.2174/0129503779282967240315040931","title":"Evolutionary Perspectives on Neural Network Generations: A Critical Examination of Models and Design Strategies","year":2024,"lang":"en","type":"article","venue":"Current Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Computer science; Cognitive science; Management science; Artificial intelligence; Psychology; Engineering","score_opus":0.06059255933555021,"score_gpt":0.3205799325048251,"score_spread":0.2599873731692749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394818527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040779347,0.0069034,0.98723143,0.00063798233,0.00074049033,0.00016918009,0.0000030589295,0.00013408627,0.00010243773],"genre_scores_gemma":[0.72917706,0.000111222536,0.27044243,0.000014715358,0.00021913824,0.000028193856,0.000001147044,0.0000029759894,0.0000030860024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985133,0.00007943894,0.00018975427,0.0005939791,0.00039305462,0.00023046906],"domain_scores_gemma":[0.99903095,0.00032716108,0.0000300095,0.00029202062,0.00023488665,0.00008498329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004926007,0.00011526712,0.00009427972,0.00019176806,0.0003872621,0.00047396097,0.0005332172,0.000022926077,0.0000023000898],"category_scores_gemma":[0.000020442405,0.000103654325,0.0000326155,0.0010496163,0.0005601696,0.0022200153,0.00023493485,0.00012820448,0.000004380569],"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":[5.10751e-7,0.000058276055,0.000001557801,0.0000121672665,0.0000018243225,9.3025096e-7,0.0009774958,0.14947534,0.000078588084,0.7629148,0.00025876582,0.08621974],"study_design_scores_gemma":[0.00003236313,0.000108596636,0.0016047041,0.00006679866,0.000003199704,0.000018447568,0.00007938594,0.93049216,0.00003035239,0.067392565,0.00006718382,0.000104262464],"about_ca_topic_score_codex":0.0000017212881,"about_ca_topic_score_gemma":1.5319289e-7,"teacher_disagreement_score":0.7810168,"about_ca_system_score_codex":0.000059570564,"about_ca_system_score_gemma":0.00027974488,"threshold_uncertainty_score":0.45704165},"labels":[],"label_agreement":null},{"id":"W4394891968","doi":"10.3390/educsci14040423","title":"Learning Multiplication by Translating across Microworlds","year":2024,"lang":"en","type":"article","venue":"Education Sciences","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Multiplication (music); Mathematics education; Computer science; Arithmetic; Psychology; Mathematics","score_opus":0.020763428895235247,"score_gpt":0.3483114264254637,"score_spread":0.32754799753022845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394891968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050808854,0.0059198476,0.907268,0.025875978,0.001102312,0.00024590344,0.000005894391,0.0006456319,0.0081275925],"genre_scores_gemma":[0.9291519,0.00004067922,0.067344785,0.0001316884,0.00011447467,0.00009593872,0.000010917144,0.000004598095,0.0031050395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893147,0.000035400288,0.00016966288,0.00044290457,0.00021771743,0.00020284609],"domain_scores_gemma":[0.9995676,0.0001067996,0.0000415916,0.00016794058,0.000057366662,0.00005866478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043548117,0.00007753884,0.000051897347,0.0000727434,0.0007430659,0.0006327606,0.00056562707,0.000029066783,0.000017929002],"category_scores_gemma":[0.00002425,0.0000708833,0.000035939916,0.0013025388,0.00012457959,0.0009940424,0.000045273722,0.00011909355,0.00014157096],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.6497049e-7,0.00009367644,0.0009598406,0.000016187721,0.0000042566007,1.5328013e-7,0.006931356,0.00031718446,0.017024895,0.10701776,0.009273538,0.858361],"study_design_scores_gemma":[0.00008932567,0.00007635777,0.0037781682,0.00013978574,0.0000051349803,0.00003291925,0.0031488126,0.5378712,0.0053793755,0.018498095,0.4305504,0.00043041527],"about_ca_topic_score_codex":0.000053476604,"about_ca_topic_score_gemma":0.000004296504,"teacher_disagreement_score":0.87834305,"about_ca_system_score_codex":0.000038441958,"about_ca_system_score_gemma":0.00030710752,"threshold_uncertainty_score":0.61017245},"labels":[],"label_agreement":null},{"id":"W4395093887","doi":"10.1007/978-3-031-56957-9","title":"Genetic Programming","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Université de Lausanne; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Universidade de Coimbra; Ben-Gurion University of the Negev; Vysoké Učení Technické v Brně; Queen's University; Radboud Universiteit; Universidade de Lisboa; Victoria University; University of Galway; Politechnika Poznańska; Lingnan University; Victoria University of Wellington; Universidade Nova de Lisboa; Indian Council of Agricultural Research; University College London; Universidade Federal de Juiz de Fora; Dalhousie University; University of Exeter; Sveučilište u Zagrebu","keywords":"Computer science; Genetic programming; Programming language; Artificial intelligence","score_opus":0.011434735953502157,"score_gpt":0.24961093494173464,"score_spread":0.23817619898823247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395093887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013899251,0.0016065537,0.9930889,0.0014342988,0.0014036353,0.00038368962,0.000003203792,0.0003332388,0.001732561],"genre_scores_gemma":[0.0040973327,0.00003207175,0.9919201,0.00047491858,0.0009211043,0.000061424274,0.0000048275974,0.000026371103,0.0024618343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685866,0.000021494134,0.00036108482,0.0014476805,0.0007228349,0.000588268],"domain_scores_gemma":[0.9982525,0.0001611079,0.00010418936,0.0011921247,0.00014605492,0.00014402473],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003924017,0.00034150868,0.00027227114,0.00058193546,0.00023791735,0.0008744927,0.0032056984,0.0002001685,0.000008173562],"category_scores_gemma":[0.00002049082,0.00031026732,0.00010860694,0.001699394,0.00045595155,0.00035929307,0.001434599,0.00069981563,0.00022086718],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.4330345e-7,0.000029081026,0.000007712856,0.000044247703,0.0000054743427,0.0000692833,0.00035619867,0.010008736,0.000018465644,0.011748888,0.00053785916,0.9771738],"study_design_scores_gemma":[0.000054980494,0.00005666094,0.00010377638,0.00020670907,0.000005644296,0.000108441556,5.6987382e-8,0.6964088,0.000049677918,0.27300155,0.0296279,0.00037578127],"about_ca_topic_score_codex":0.0000096452595,"about_ca_topic_score_gemma":0.000020456515,"teacher_disagreement_score":0.97679806,"about_ca_system_score_codex":0.00041266077,"about_ca_system_score_gemma":0.0014333364,"threshold_uncertainty_score":0.999935},"labels":[],"label_agreement":null},{"id":"W4395955281","doi":"10.18280/jesa.570214","title":"Balancing of Robustness and Performance for Triple Inverted Pendulum Using μ-Synthesis and Gazelle Optimization","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Inverted pendulum; Control theory (sociology); Computer science; Mathematics; Physics; Biology; Nonlinear system; Artificial intelligence; Control (management); Genetics","score_opus":0.02297723769677302,"score_gpt":0.24924848972530522,"score_spread":0.2262712520285322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395955281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18376337,0.0017019374,0.8139831,0.00013171605,0.00015287001,0.00013778031,0.0000063928965,0.00007176005,0.000051092346],"genre_scores_gemma":[0.60420537,0.00042740276,0.39518982,0.000009200169,0.00006532485,0.000012465776,9.4550126e-7,0.000013322567,0.00007612208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991084,0.000056439087,0.0003301221,0.00019337855,0.0001500772,0.00016163742],"domain_scores_gemma":[0.99937505,0.0001576234,0.00013770297,0.00012611726,0.0001265243,0.000076982185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039864957,0.00010692326,0.00017606832,0.00016355663,0.0003553106,0.00026162906,0.0001777847,0.000038686612,0.000008119795],"category_scores_gemma":[0.00006128412,0.00009323303,0.000041376406,0.00033803517,0.00006490164,0.0008034696,0.00008539077,0.0000878261,5.919365e-7],"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":[0.000013574108,0.000081452374,0.001690949,0.001583406,0.00013877571,0.000016738837,0.0012479945,0.24941519,0.002941327,0.008464384,0.00077584013,0.73363036],"study_design_scores_gemma":[0.00014401742,0.000054350527,0.014763048,0.00038959843,0.000029897592,0.00065903773,0.000038864,0.98289764,0.00032510803,0.00050085253,0.00009337375,0.00010422463],"about_ca_topic_score_codex":0.000008255903,"about_ca_topic_score_gemma":8.209632e-7,"teacher_disagreement_score":0.7335262,"about_ca_system_score_codex":0.000054766828,"about_ca_system_score_gemma":0.000091929265,"threshold_uncertainty_score":0.38019326},"labels":[],"label_agreement":null},{"id":"W4398239758","doi":"10.31468/dwr.1093","title":"Two perspectives on generative AI now","year":2024,"lang":"en","type":"article","venue":"Discourse and Writing/Rédactologie","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Computer science; Artificial intelligence","score_opus":0.059836138254525516,"score_gpt":0.4016007775668646,"score_spread":0.3417646393123391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398239758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24188083,0.016816616,0.58111656,0.12947597,0.00069330394,0.0005528906,0.00008884315,0.00145745,0.027917529],"genre_scores_gemma":[0.9863371,0.00043326465,0.011815336,0.0005525574,0.00031853013,0.000054528315,0.000008353456,0.000009678718,0.00047063833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988958,0.00003619329,0.00013675432,0.0005389261,0.00014321656,0.00024909363],"domain_scores_gemma":[0.9993992,0.00016580752,0.000025017782,0.0002817953,0.000051710453,0.00007647165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013688847,0.00014885484,0.00012759873,0.000088987224,0.00026061994,0.0003292915,0.00028246606,0.000045843426,0.000019701958],"category_scores_gemma":[0.000016831309,0.000118524455,0.00006198785,0.00024715994,0.00015949915,0.00043047627,0.00012575681,0.00023682661,0.00009363889],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017142703,0.000094842944,0.0003236405,0.00000802112,0.000025525773,0.000050730658,0.0011813283,0.000053955577,0.00032172666,0.9688861,0.0047299047,0.024322525],"study_design_scores_gemma":[0.0018113888,0.0012004335,0.029621242,0.00064592646,0.00013070815,0.00050444406,0.034683272,0.591851,0.006679409,0.28670913,0.043523557,0.0026394876],"about_ca_topic_score_codex":0.000010147822,"about_ca_topic_score_gemma":0.000004691066,"teacher_disagreement_score":0.7444563,"about_ca_system_score_codex":0.000039743452,"about_ca_system_score_gemma":0.00008360611,"threshold_uncertainty_score":0.4833287},"labels":[],"label_agreement":null},{"id":"W4398529402","doi":"10.7910/dvn/dnw5rw","title":"TTalk: Ron's Numbers","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mathematics","score_opus":0.015559025699883043,"score_gpt":0.2480815617388779,"score_spread":0.23252253603899486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398529402","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.054389e-7,0.0000020759953,0.021043822,0.000045448363,0.0009516719,0.0002833937,0.9768953,0.00009895081,0.00067854143],"genre_scores_gemma":[0.000001903491,0.00017444257,0.012429285,0.00048799143,0.00024769813,0.000048307167,0.9849808,0.000011920253,0.0016176471],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981419,0.000046707817,0.0002665181,0.0007670725,0.0004174255,0.00036036313],"domain_scores_gemma":[0.9962544,0.00007314723,0.0001869922,0.0032675026,0.00006760278,0.00015039141],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017247301,0.00028731764,0.00026905152,0.00012984782,0.00016858992,0.00020082407,0.00288853,0.00022325413,0.005354843],"category_scores_gemma":[0.000030941865,0.00029166596,0.00012458998,0.00032185685,0.00006650926,0.0007434051,0.0012943409,0.0003858912,0.38831076],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012332487,0.00007333507,0.0000012004838,0.000028681288,0.000026505706,0.000022072265,0.0000068705094,0.000028762242,0.0000022819536,0.0018462403,0.99759763,0.00036520592],"study_design_scores_gemma":[0.00019844061,0.000022230306,0.0000341609,0.000027932592,0.00002614156,0.000033447533,0.000007961261,0.0016758583,0.0000028141508,0.00019838598,0.997422,0.00035064557],"about_ca_topic_score_codex":0.0002672992,"about_ca_topic_score_gemma":0.000028241448,"teacher_disagreement_score":0.3829559,"about_ca_system_score_codex":0.00009523454,"about_ca_system_score_gemma":0.00022578335,"threshold_uncertainty_score":0.99995357},"labels":[],"label_agreement":null},{"id":"W4399151368","doi":"10.48550/arxiv.2405.17631","title":"BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Wu Tsai Neurosciences Institute, Stanford University; Genentech; Defense Advanced Research Projects Agency; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Computer science; Perturbation (astronomy); Artificial intelligence; Physics; Quantum mechanics","score_opus":0.09494131121400051,"score_gpt":0.23107603008475666,"score_spread":0.13613471887075615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399151368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051706478,0.00035389172,0.94602334,0.00022837156,0.00054888526,0.00058411877,0.00004513132,0.00023348995,0.00027626392],"genre_scores_gemma":[0.9513658,0.00007190412,0.04604825,0.0001769661,0.00015273914,0.00002540755,0.000076728444,0.000020192032,0.0020619987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998347,0.00005555749,0.00017305359,0.0010864451,0.00008128237,0.0002566852],"domain_scores_gemma":[0.99879223,0.000039006864,0.00010946412,0.0008321086,0.000109172615,0.000117990545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011078061,0.00023543835,0.00016167988,0.0001886987,0.0002554046,0.00024661256,0.0010275074,0.00015893774,0.000012270507],"category_scores_gemma":[0.000006068421,0.00027127864,0.0001697405,0.00032737525,0.00004771915,0.00031771645,0.0010578771,0.00023943986,0.0000778484],"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":[0.000018234434,0.00038091274,0.00017064095,0.00020490619,0.00015027208,0.000082605584,0.0013309566,0.39817417,0.0011136709,0.59261227,0.002307556,0.0034537963],"study_design_scores_gemma":[0.000173288,0.000061184444,0.00023013302,0.000040702584,0.000052164185,0.0000026239918,0.000063523024,0.8278976,0.00057072163,0.1695511,0.0010657068,0.00029125254],"about_ca_topic_score_codex":0.000039756487,"about_ca_topic_score_gemma":0.0000033357057,"teacher_disagreement_score":0.8999751,"about_ca_system_score_codex":0.00024485993,"about_ca_system_score_gemma":0.00020067106,"threshold_uncertainty_score":0.99997395},"labels":[],"label_agreement":null},{"id":"W4400032549","doi":"10.1016/b978-0-443-28824-1.50289-1","title":"A Deep Reinforcement Learning PI Tuning Strategy for Closed-loop Operation of a Recirculating Aquaculture System","year":2024,"lang":"en","type":"book-chapter","venue":"Computer-aided chemical engineering/Computer aided chemical engineering","topic":"Evolutionary Algorithms and Applications","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 Waterloo","funders":"","keywords":"Reinforcement learning; Loop (graph theory); Control theory (sociology); Closed loop; Aquaculture; Computer science; Artificial intelligence; Engineering; Control engineering; Fishery; Biology; Mathematics; Fish <Actinopterygii>; Control (management)","score_opus":0.012778282181801786,"score_gpt":0.21144299598898555,"score_spread":0.19866471380718376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400032549","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035243965,0.00079913996,0.99407506,0.0000876618,0.0009430985,0.0009994606,0.0000170478,0.0018582117,0.0008678724],"genre_scores_gemma":[0.17569318,0.000042811902,0.8143263,0.00007065552,0.004651923,0.00046445,0.0009693559,0.00054689957,0.003234449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943576,0.0000130507315,0.0019731848,0.0017988032,0.00081485236,0.0010425295],"domain_scores_gemma":[0.9970344,0.0004866179,0.00048408867,0.001083977,0.00040689172,0.00050406327],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00035140562,0.0013823295,0.001499035,0.00046980227,0.00014283998,0.0003802205,0.0016844658,0.0010145707,0.000013330223],"category_scores_gemma":[0.00004498199,0.0015080534,0.000844993,0.00039661652,0.00006585016,0.0004822777,0.0011692214,0.001848677,0.00003387278],"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":[0.0000066873026,0.000030523788,1.6114441e-7,0.0017753323,0.00031277107,0.000024605486,0.000209575,0.76167506,0.058154915,0.17263065,0.00014429669,0.005035424],"study_design_scores_gemma":[0.00077565294,0.00015809058,7.587428e-7,0.002346066,0.00014431456,0.00018616646,0.0000053393633,0.97722846,0.014576383,0.00048814696,0.002672827,0.0014178117],"about_ca_topic_score_codex":0.000006889305,"about_ca_topic_score_gemma":1.1234612e-7,"teacher_disagreement_score":0.21555339,"about_ca_system_score_codex":0.000832987,"about_ca_system_score_gemma":0.0001241324,"threshold_uncertainty_score":0.9998927},"labels":[],"label_agreement":null},{"id":"W4400529018","doi":"10.1145/3626772.3657979","title":"Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?","year":2024,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Generalization; Encoder; Query expansion; Artificial intelligence; Data mining; Information retrieval; Mathematics; Operating system","score_opus":0.012247478305655517,"score_gpt":0.2762828888245987,"score_spread":0.2640354105189432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400529018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0152577525,0.0002548119,0.9807074,0.0012336582,0.00031091226,0.000110735324,0.000009583747,0.00017219932,0.0019429207],"genre_scores_gemma":[0.92898214,0.000028962782,0.06787795,0.00008367002,0.000102377744,0.000027761687,0.00001533499,0.0000068380596,0.0028749434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928194,0.000014023866,0.00017720951,0.00025536655,0.00015383445,0.0001176297],"domain_scores_gemma":[0.9995428,0.000026602387,0.0000276975,0.00028296528,0.00007983846,0.00004012844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084641055,0.00006845772,0.00006632186,0.00006346332,0.00007833273,0.0001075672,0.00024017227,0.000038244907,0.000040817366],"category_scores_gemma":[0.0000044519807,0.000057736837,0.0000461662,0.0003230249,0.00003608072,0.00040642841,0.00008637512,0.00005176376,0.000015769303],"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":[8.9969956e-7,0.00004032858,0.00036119675,0.000030905114,0.000012526991,0.000001864545,0.00032734635,0.0032879564,0.023181284,0.9530514,0.0021464974,0.017557824],"study_design_scores_gemma":[0.000100985955,0.000027862816,0.002564186,0.000016361764,0.000003961554,0.0000044855306,0.000026712469,0.97478044,0.01010258,0.010311581,0.0019552775,0.00010559],"about_ca_topic_score_codex":0.00020942201,"about_ca_topic_score_gemma":0.000020673377,"teacher_disagreement_score":0.97149247,"about_ca_system_score_codex":0.000031872878,"about_ca_system_score_gemma":0.000105545914,"threshold_uncertainty_score":0.235444},"labels":[],"label_agreement":null},{"id":"W4401214196","doi":"10.1145/3638530.3664168","title":"Accelerating GP Genome Evaluation Through Real Compilation with a Multiple Program Single Data Approach","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Computer science; Programming language","score_opus":0.09710511390660542,"score_gpt":0.30142283220778654,"score_spread":0.20431771830118112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401214196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20661654,0.0012654675,0.7864043,0.0011214004,0.00016643228,0.0015491717,0.000042345684,0.0003445924,0.0024897312],"genre_scores_gemma":[0.6734785,0.00005136056,0.32612088,0.000013337791,0.00007820691,0.0000912938,0.0001416801,0.00000952459,0.00001524886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980255,0.00004018114,0.00039938072,0.0007062209,0.0006111246,0.00021761816],"domain_scores_gemma":[0.99862814,0.00008935269,0.00022452077,0.0002835913,0.000716613,0.00005779187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034685794,0.0001999743,0.0001837869,0.000091974165,0.0004247058,0.00036641347,0.00081523135,0.00006700085,0.0000042787174],"category_scores_gemma":[0.000025726295,0.00015369622,0.0000373135,0.00074527244,0.00018593823,0.0012720223,0.00054566667,0.00016255093,0.00000312255],"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":[0.00010936887,0.0027134886,0.033594415,0.0012943734,0.0004956561,0.0000016278813,0.011280451,0.13961609,0.030206123,0.26753056,0.00533121,0.5078266],"study_design_scores_gemma":[0.00025825796,0.00011099728,0.16537458,0.00009820052,0.000044899523,0.000054346277,0.00016260256,0.8231668,0.000046015757,0.010143088,0.00037922148,0.0001609858],"about_ca_topic_score_codex":0.000042170694,"about_ca_topic_score_gemma":0.0000029416929,"teacher_disagreement_score":0.6835507,"about_ca_system_score_codex":0.00009628472,"about_ca_system_score_gemma":0.00021856306,"threshold_uncertainty_score":0.626755},"labels":[],"label_agreement":null},{"id":"W4401389346","doi":"10.3390/proceedings2024105049","title":"Analyzing Power Consumption in a Coaxial Bioreactor Using Machine Learning Techniques with Computational Fluid Dynamics","year":2024,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Coaxial; Power consumption; Computational fluid dynamics; Bioreactor; Computer science; Power (physics); Dynamics (music); Process engineering; Engineering; Chemistry; Physics; Aerospace engineering; Acoustics; Thermodynamics","score_opus":0.014162311089204786,"score_gpt":0.2717983959801246,"score_spread":0.2576360848909198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401389346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09246372,0.00014440341,0.9062478,0.0004093105,0.000036608,0.000112847185,0.0000070919587,0.00037124832,0.00020699194],"genre_scores_gemma":[0.65669644,0.000009165325,0.3431554,0.000022098164,0.00001715184,0.000015287147,0.00003045085,0.0000070285573,0.00004696467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992164,0.000030417967,0.00017021236,0.00029168185,0.00014767966,0.00014360175],"domain_scores_gemma":[0.99969226,0.00008138175,0.00003088052,0.000113285496,0.000043638214,0.000038545895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016494696,0.00010081913,0.00009097061,0.00023835733,0.00011133026,0.0001667303,0.00016424117,0.00004009461,0.000029878933],"category_scores_gemma":[0.000004694167,0.00008454859,0.000028404971,0.00055140146,0.00004582082,0.00043464816,0.000076604134,0.00017691846,0.000012941027],"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":[0.000017509345,0.00024695366,0.079077266,0.00006527098,0.00007132159,0.00008598967,0.0005873178,0.03807118,0.006942835,0.85053384,0.00007975651,0.024220766],"study_design_scores_gemma":[0.00007623931,0.00003846159,0.0038172726,0.000053353302,0.000003867131,0.000061827646,0.00001311266,0.9938104,0.00019658252,0.0014298822,0.00037612114,0.00012288627],"about_ca_topic_score_codex":0.00018190277,"about_ca_topic_score_gemma":0.00007457391,"teacher_disagreement_score":0.9557392,"about_ca_system_score_codex":0.00016073996,"about_ca_system_score_gemma":0.00007551539,"threshold_uncertainty_score":0.34477913},"labels":[],"label_agreement":null},{"id":"W4402294425","doi":"10.1016/j.jpdc.2024.104977","title":"Accelerating Fortran codes: A method for integrating Coarray Fortran with CUDA Fortran and OpenMP","year":2024,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Austrian Science Fund; Russian Foundation for Basic Research","keywords":"Fortran; Computer science; Parallel computing; CUDA; Computational science; Programming language","score_opus":0.021641870002693332,"score_gpt":0.30461748124396143,"score_spread":0.2829756112412681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402294425","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022377497,0.0013191283,0.9743615,0.0013328354,0.00010514394,0.00029512768,0.000040546285,0.00006562873,0.00010260187],"genre_scores_gemma":[0.4845158,0.00003350836,0.5152246,0.000051485582,0.00013462197,0.000007148615,0.0000141468345,0.000009270416,0.000009477386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846697,0.000049754668,0.0006116489,0.00033865738,0.0002153072,0.0003176716],"domain_scores_gemma":[0.99864745,0.00054864597,0.00026893787,0.00014087616,0.00021148114,0.00018258874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084876514,0.00020951037,0.00035958725,0.00010705645,0.0005527797,0.0006512369,0.0003225896,0.000065208216,0.0000015795339],"category_scores_gemma":[0.00003199426,0.00015273872,0.000110004104,0.00034277036,0.00005551801,0.00066026166,0.0000692055,0.0003175412,2.2376801e-7],"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":[0.00017101842,0.000315789,0.0035831654,0.0008057361,0.0007449901,0.00018797783,0.0066715563,0.036813695,0.0028440827,0.51489234,0.0021253293,0.4308443],"study_design_scores_gemma":[0.00096190727,0.00041311843,0.0011588049,0.0004181301,0.00006318027,0.0010661919,0.000704525,0.98409474,0.00008952094,0.00759483,0.0031993212,0.00023574973],"about_ca_topic_score_codex":0.000028201715,"about_ca_topic_score_gemma":0.000026366375,"teacher_disagreement_score":0.947281,"about_ca_system_score_codex":0.000036214406,"about_ca_system_score_gemma":0.00017905755,"threshold_uncertainty_score":0.6279893},"labels":[],"label_agreement":null},{"id":"W4402306010","doi":"10.1016/j.ifacol.2024.08.328","title":"Semi-centralized Multi-agent RL for Irrigation Scheduling","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"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; Alberta Innovates","keywords":"Computer science; Scheduling (production processes); Distributed computing; Mathematical optimization; Mathematics","score_opus":0.031947511233262386,"score_gpt":0.3045211252260924,"score_spread":0.27257361399283003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402306010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007086168,0.0013698661,0.9816441,0.008236175,0.0005875092,0.000423436,0.000061898936,0.00046436486,0.00012648363],"genre_scores_gemma":[0.021451311,0.000063706226,0.9761843,0.00030325784,0.0003549633,0.00014049285,0.00011041106,0.000016900565,0.0013746525],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988085,0.000019850237,0.00023652888,0.00046701013,0.00018058112,0.00028753825],"domain_scores_gemma":[0.99935,0.00011183423,0.000042186144,0.00030620865,0.000081387254,0.00010835669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019218356,0.00014706435,0.00012704571,0.00007344643,0.0002181726,0.0001465561,0.0003455489,0.0000698584,0.000029098594],"category_scores_gemma":[0.000031270567,0.00013400175,0.00013008276,0.00037402366,0.0000314987,0.00037481808,0.00007399654,0.00012356191,0.00012416714],"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":[0.000021442062,0.0009498321,0.00016920385,0.00036862234,0.00023603938,0.00003387184,0.0031109762,0.012756911,0.083009355,0.69711524,0.000385667,0.20184284],"study_design_scores_gemma":[0.00044817504,0.000031363816,0.00030926082,0.000057805326,0.000015920648,0.000012181829,0.000048089867,0.9750863,0.0009358188,0.0017607013,0.021114318,0.00018007791],"about_ca_topic_score_codex":0.00001695989,"about_ca_topic_score_gemma":0.000008520876,"teacher_disagreement_score":0.9623294,"about_ca_system_score_codex":0.000086358734,"about_ca_system_score_gemma":0.00010139254,"threshold_uncertainty_score":0.5464433},"labels":[],"label_agreement":null},{"id":"W4402540999","doi":"10.1093/jas/skae234.178","title":"510 Utilizing python programming for modern genetic evaluation systems","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Python (programming language); Programming language; Computer science; Genetic programming; Software engineering; Computational biology; Biology; Artificial intelligence","score_opus":0.05364888096777835,"score_gpt":0.34361280391383053,"score_spread":0.28996392294605217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402540999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07289505,0.0034768132,0.92187756,0.0006110713,0.00061558414,0.0002911541,0.000001031867,0.000040283845,0.00019146423],"genre_scores_gemma":[0.83327854,0.000015878895,0.16642049,0.000011073773,0.00023334888,0.000019908253,1.205091e-7,0.000003735298,0.000016906937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847054,0.000022308132,0.00030308028,0.0002332791,0.0007517999,0.00021897194],"domain_scores_gemma":[0.9989922,0.00007165192,0.00013508962,0.0001431822,0.0005561019,0.000101786965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021350887,0.000066263055,0.00008918055,0.00018117858,0.0002690639,0.000624119,0.0006967015,0.000022428203,0.0000013491932],"category_scores_gemma":[0.00007562066,0.00005244402,0.000067527464,0.0008216858,0.00008808513,0.0011729671,0.00007318763,0.00009421326,0.000003963831],"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":[0.000012057289,0.00009958769,0.00010089711,0.00007964369,0.000017382023,0.000014568744,0.0010340182,0.0076875305,0.24086003,0.14963947,0.00040692766,0.6000479],"study_design_scores_gemma":[0.000078764795,0.0002946897,0.0019637959,0.00007966793,0.000013361961,0.00023048873,0.00006462213,0.9880577,0.0005698666,0.004339827,0.004239661,0.00006756233],"about_ca_topic_score_codex":0.000004305816,"about_ca_topic_score_gemma":4.0678208e-7,"teacher_disagreement_score":0.98037016,"about_ca_system_score_codex":0.00015580379,"about_ca_system_score_gemma":0.00061914796,"threshold_uncertainty_score":0.6018394},"labels":[],"label_agreement":null},{"id":"W4402812287","doi":"10.1007/978-3-031-64373-6_2","title":"Evolutionary Computation","year":2024,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on learning, networks, and algorithms","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University; University of Guelph; Brock University; St. Francis Xavier University","funders":"","keywords":"Computer science; Evolutionary biology; Biology","score_opus":0.01005193352964428,"score_gpt":0.2260725710332572,"score_spread":0.21602063750361292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402812287","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000065819286,0.024690125,0.7111331,0.0025327695,0.0012774953,0.00059551577,0.00003443812,0.0012262932,0.25850368],"genre_scores_gemma":[0.022110276,0.012897201,0.054118536,0.0011466659,0.006646442,0.00038957238,0.00036987505,0.00044467257,0.90187675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972035,0.00007490974,0.00046382681,0.0012566164,0.00053491764,0.00046618155],"domain_scores_gemma":[0.9980617,0.0008472098,0.00027998965,0.0004820149,0.000119259974,0.00020983872],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003020802,0.00063572,0.0005354445,0.00035367612,0.00067756156,0.0003472395,0.00057181565,0.000531379,0.000120536846],"category_scores_gemma":[0.00004459413,0.00058685616,0.00026851107,0.00017063107,0.00020781839,0.00017752753,0.0002889128,0.0013874228,0.00032210097],"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":[0.0000103750335,0.0000515928,0.0000042759852,0.000067211164,0.00024411657,0.00005961638,0.000119750744,0.08836709,0.0000028993916,0.49881947,0.028153127,0.38410047],"study_design_scores_gemma":[0.00008008058,0.00012807582,0.00008346707,0.00033757943,0.00006952375,0.000074772106,0.0000056522026,0.5199451,0.00000394481,0.07908814,0.3996078,0.00057587406],"about_ca_topic_score_codex":0.000012297499,"about_ca_topic_score_gemma":0.0000027379099,"teacher_disagreement_score":0.65701455,"about_ca_system_score_codex":0.00013535206,"about_ca_system_score_gemma":0.000095360534,"threshold_uncertainty_score":0.9996583},"labels":[],"label_agreement":null},{"id":"W4403305809","doi":"10.1017/9781009302180","title":"How to Think about Algorithms","year":2024,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Algorithm","score_opus":0.016623194362335805,"score_gpt":0.20835828953529031,"score_spread":0.1917350951729545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403305809","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000149028,0.00044089466,0.27228662,0.00077822193,0.00050713716,0.00045558016,0.00025818456,0.00053222844,0.7247396],"genre_scores_gemma":[0.000012889887,0.00005530751,0.02049121,0.00021326635,0.00044526625,0.0000055527216,0.00005566702,0.00003499217,0.97868586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801546,0.00003844596,0.00014997061,0.0009964741,0.00041029826,0.00038932884],"domain_scores_gemma":[0.9980844,0.00006550682,0.000104656145,0.0012149942,0.00019902697,0.00033140043],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010556267,0.0003775397,0.00032257,0.00028702943,0.00030337906,0.00047501017,0.0022102974,0.0003051586,7.278517e-7],"category_scores_gemma":[0.00000663036,0.00042943377,0.00023966315,0.00007766653,0.00011154594,0.00027845142,0.0016147619,0.0005799238,0.00014139779],"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":[0.0000013287769,0.000007066818,2.558464e-8,0.00003213719,0.0000405994,0.000119710705,0.000061005765,0.000003889549,0.0000066516905,0.56648386,0.42903647,0.00420723],"study_design_scores_gemma":[0.00013415296,0.00004190672,0.0000072060684,0.00013873825,0.00006510092,0.000029555797,0.000017334909,0.0047041005,0.000058420428,0.00018781732,0.99413854,0.00047711216],"about_ca_topic_score_codex":0.000047233934,"about_ca_topic_score_gemma":9.168912e-7,"teacher_disagreement_score":0.56629604,"about_ca_system_score_codex":0.00044518476,"about_ca_system_score_gemma":0.00048221176,"threshold_uncertainty_score":0.99981576},"labels":[],"label_agreement":null},{"id":"W4403371077","doi":"10.19184/ejlh.v11i2.43512","title":"Analysing Discrimination based on Genetic Information","year":2024,"lang":"en","type":"article","venue":"Lentera Hukum","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science","score_opus":0.009128101008679345,"score_gpt":0.24327851796331715,"score_spread":0.2341504169546378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403371077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040367204,0.000050316223,0.9890001,0.0038351114,0.00025014434,0.00007688216,0.0000028241902,0.00017523397,0.002572684],"genre_scores_gemma":[0.9636595,0.0000046264713,0.03544337,0.0005935393,0.00006666852,0.000028199709,0.00003663796,0.000003102155,0.000164312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994737,0.00001595722,0.00013246351,0.00013989423,0.00014233688,0.00009566269],"domain_scores_gemma":[0.99966055,0.000030177664,0.00002246329,0.0002272705,0.000030316878,0.000029233417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006203813,0.00006145293,0.000041296033,0.00015948305,0.00009522479,0.0002907195,0.0001967844,0.000019321282,0.000012581978],"category_scores_gemma":[0.000004995487,0.000054819277,0.00004550045,0.0003368575,0.000010287256,0.0007659348,0.000030571,0.00006341891,0.00016300406],"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":[0.0000032435294,0.00013952058,0.00048572043,0.000098539116,0.000023585226,0.000009916936,0.0014994929,0.030457102,0.00044797768,0.36543664,0.004386961,0.5970113],"study_design_scores_gemma":[0.000055493598,0.000026008529,0.009639321,0.00003795138,0.000004817419,0.0000025828447,0.000008572,0.96339834,0.00011055193,0.0025375332,0.024116388,0.000062430394],"about_ca_topic_score_codex":0.000005126654,"about_ca_topic_score_gemma":0.000001416662,"teacher_disagreement_score":0.9596228,"about_ca_system_score_codex":0.000047138667,"about_ca_system_score_gemma":0.0000227918,"threshold_uncertainty_score":0.28034148},"labels":[],"label_agreement":null},{"id":"W4403406779","doi":"10.1145/3638530.3648422","title":"Linear Genetic Programming","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Genetic programming; Artificial intelligence","score_opus":0.01981866932713728,"score_gpt":0.25185721868199507,"score_spread":0.2320385493548578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403406779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22783051,0.002452413,0.7648936,0.0030968084,0.00040299856,0.00044624356,0.0000054004954,0.00026159405,0.0006104549],"genre_scores_gemma":[0.7863374,0.000093009345,0.21330489,0.0000291891,0.00009057405,0.000032680793,0.0000024451824,0.000007419435,0.000102383914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987707,0.000013594745,0.0003220139,0.00040210865,0.0002973324,0.00019423207],"domain_scores_gemma":[0.9993071,0.000055842047,0.00010783515,0.000114744034,0.00034133374,0.00007315259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114307455,0.00014828677,0.00013572403,0.00011080042,0.0002789087,0.00016120584,0.00050696835,0.000055896107,0.0000061307887],"category_scores_gemma":[0.000014521443,0.00011883635,0.00006903084,0.00059920247,0.00016127888,0.00032967882,0.00032097416,0.00013892827,0.0000130781045],"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":[0.000009064729,0.00023848948,0.012545796,0.000542214,0.00009128132,0.0000019853828,0.0018706545,0.007885718,0.0054828483,0.60298723,0.0053483923,0.3629963],"study_design_scores_gemma":[0.00009895295,0.00005025738,0.14087076,0.00012244849,0.00001600252,0.00009931566,0.00006875729,0.79248625,0.00012191037,0.063321844,0.0026039977,0.00013952778],"about_ca_topic_score_codex":0.000014233586,"about_ca_topic_score_gemma":3.8490646e-7,"teacher_disagreement_score":0.7846005,"about_ca_system_score_codex":0.00003880501,"about_ca_system_score_gemma":0.00012313513,"threshold_uncertainty_score":0.48460057},"labels":[],"label_agreement":null},{"id":"W4403919476","doi":"10.1109/ro-man60168.2024.10731289","title":"A Biologically Inspired Program-level Imitation Approach for Robots: Proof-of-Concept","year":2024,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Proof of concept; Robot; Imitation; Computer science; Artificial intelligence; Human–computer interaction; Psychology; Neuroscience","score_opus":0.0716537637075071,"score_gpt":0.30560753306992205,"score_spread":0.23395376936241497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403919476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018527595,0.0002765617,0.9945547,0.0014937213,0.0000689528,0.0009818488,0.00001578428,0.00037749985,0.0020456584],"genre_scores_gemma":[0.13910869,0.0000018623156,0.8591254,0.00006333299,0.000052907773,0.0010349831,0.00005201265,0.000004676152,0.0005561857],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918646,0.000015685386,0.00020599407,0.00034084776,0.00010091373,0.00015009215],"domain_scores_gemma":[0.9995125,0.00008672294,0.000036319176,0.00021406413,0.00011114823,0.000039234896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014543194,0.000083900166,0.00009576996,0.000057651952,0.0000732327,0.000081658756,0.0003726451,0.000056562018,0.0000045417128],"category_scores_gemma":[0.000022109301,0.000060734557,0.00007855087,0.00047220231,0.00005851069,0.00026472585,0.000071103714,0.00005048688,0.0000030454269],"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":[0.0000014969668,0.00023807689,0.000007840612,0.000035857636,0.000015145904,2.2869149e-7,0.00016098634,0.001139394,0.00025746127,0.51938564,0.0014190759,0.47733882],"study_design_scores_gemma":[0.00012584044,0.00023670336,0.00049378636,0.0000115327675,0.0000056847794,0.0000045044617,0.000032840948,0.96761334,0.0020026718,0.022553762,0.006801228,0.00011809823],"about_ca_topic_score_codex":0.0000070365413,"about_ca_topic_score_gemma":6.2972094e-7,"teacher_disagreement_score":0.96647394,"about_ca_system_score_codex":0.000018994515,"about_ca_system_score_gemma":0.00007883222,"threshold_uncertainty_score":0.24766834},"labels":[],"label_agreement":null},{"id":"W4404741643","doi":"10.1080/24751448.2024.2405339","title":"Variegated Coding","year":2024,"lang":"en","type":"article","venue":"Technology|Architecture + Design","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Biology; Computer science","score_opus":0.0144772310707161,"score_gpt":0.24480233153881858,"score_spread":0.23032510046810248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404741643","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029115993,0.0013130589,0.97673494,0.01598779,0.00020536224,0.00027486577,0.0000035818439,0.004153912,0.00103534],"genre_scores_gemma":[0.440899,0.000025917117,0.5581584,0.00017494471,0.00007840172,0.00012625691,0.000003434752,0.000018514571,0.0005151397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987528,0.000040341267,0.00017971646,0.00053210394,0.0001607975,0.00033428392],"domain_scores_gemma":[0.99905795,0.00016068835,0.000028221171,0.00065286155,0.00004276614,0.00005750044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024359596,0.00016803954,0.00013447102,0.0005274796,0.0002301647,0.00014555908,0.0010607798,0.00019796033,0.00002847801],"category_scores_gemma":[0.00003893311,0.00014377359,0.00005495337,0.0022113584,0.00015019538,0.00015129606,0.00022781678,0.00049619394,0.00025059367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015119892,0.000030414489,0.000005499191,0.000013338949,0.000029812145,0.000060523533,0.00015128453,0.0009392953,0.014906308,0.8183616,0.0042165015,0.1612839],"study_design_scores_gemma":[0.00013637808,0.00012247992,0.000064997395,0.000067124616,0.000014557554,0.0004535564,0.000015627336,0.25180534,0.017904434,0.6573576,0.07172497,0.00033295367],"about_ca_topic_score_codex":0.0000036258816,"about_ca_topic_score_gemma":7.651876e-7,"teacher_disagreement_score":0.44060785,"about_ca_system_score_codex":0.00005075779,"about_ca_system_score_gemma":0.00011342098,"threshold_uncertainty_score":0.58629173},"labels":[],"label_agreement":null},{"id":"W4404862210","doi":"","title":"On-chain optimal aggregation of Uniswap v3 clones","year":2024,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Chain (unit); Computer science; Business; Physics","score_opus":0.011859672200662941,"score_gpt":0.2329932973588489,"score_spread":0.22113362515818596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404862210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046167765,0.0014827063,0.91133076,0.021046184,0.0004196821,0.0005226919,0.000099579454,0.0006461673,0.018284455],"genre_scores_gemma":[0.7064011,0.00036031168,0.28744608,0.0000835717,0.000041782165,0.0001549587,0.00021993557,0.000049503138,0.005242734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613935,0.0015310819,0.0005872298,0.00092920585,0.0005297296,0.00028339907],"domain_scores_gemma":[0.99421024,0.0011631261,0.00045851144,0.0026010415,0.0014253058,0.0001417522],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027944064,0.00031321778,0.00032883865,0.0003042502,0.0002663632,0.0003361856,0.002124145,0.00025595707,0.000028769826],"category_scores_gemma":[0.0004020387,0.00032740505,0.0002444512,0.0006997603,0.00022027826,0.00014107056,0.0026778537,0.0006938534,0.00011420267],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025860018,0.00035222823,0.000030601965,0.00012007003,0.000045477394,0.0000031224226,0.002538302,0.0017660487,0.00045727406,0.96365744,0.00148719,0.029539675],"study_design_scores_gemma":[0.00037612533,0.0000017058701,0.00093444646,0.0028008805,0.00004801632,0.000018337934,0.00006952671,0.6766994,0.037079025,0.27398264,0.0073407944,0.00064908905],"about_ca_topic_score_codex":0.0003985884,"about_ca_topic_score_gemma":0.000099532655,"teacher_disagreement_score":0.6896748,"about_ca_system_score_codex":0.000113472925,"about_ca_system_score_gemma":0.00040664477,"threshold_uncertainty_score":0.9999178},"labels":[],"label_agreement":null},{"id":"W4404916257","doi":"10.1109/icons62911.2024.00020","title":"Timing Actions in Games Through Bio-Inspired Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Error-driven learning; Reinforcement; Human–computer interaction; Engineering","score_opus":0.04102234093842048,"score_gpt":0.30151768002617596,"score_spread":0.26049533908775546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404916257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014768599,0.00021988343,0.9597358,0.004350784,0.00014508005,0.00008989152,2.1544204e-7,0.00040006085,0.03358141],"genre_scores_gemma":[0.9035675,0.00009461122,0.08603221,0.0001633669,0.0000646667,0.00006854914,0.0000050111707,0.000005736709,0.009998333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993586,0.000012282964,0.00014201312,0.00022967874,0.00010821134,0.00014919307],"domain_scores_gemma":[0.99971676,0.000051798103,0.000014077561,0.00017652655,0.000015553625,0.00002528588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008772923,0.00006343273,0.000053375687,0.00008165714,0.00013024014,0.00012561923,0.00020427484,0.000025792306,0.000076812525],"category_scores_gemma":[0.000007078416,0.00005691238,0.000033990993,0.000583351,0.000017086584,0.0006080158,0.00010296977,0.00012996615,0.0002035751],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.4810057e-7,0.000028937207,0.00011363122,0.0000118802045,0.000009850274,0.000007378446,0.0010826687,0.03146824,0.0006931568,0.9269188,0.002499204,0.037165932],"study_design_scores_gemma":[0.000051631047,0.000019025842,0.00061450613,0.000028649927,0.0000017050615,0.000007869105,0.00011060096,0.82724696,0.00044918773,0.0042277826,0.16715302,0.000089062414],"about_ca_topic_score_codex":0.00011274831,"about_ca_topic_score_gemma":0.000011367174,"teacher_disagreement_score":0.922691,"about_ca_system_score_codex":0.00006115314,"about_ca_system_score_gemma":0.0000495606,"threshold_uncertainty_score":0.26166138},"labels":[],"label_agreement":null},{"id":"W4404917641","doi":"10.1016/j.conengprac.2024.106183","title":"A semi-centralized multi-agent RL framework for efficient irrigation scheduling","year":2024,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Multi-agent system; Distributed computing; Scheduling (production processes); Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.01596670665170339,"score_gpt":0.28777967915878044,"score_spread":0.27181297250707703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404917641","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012536185,0.002308032,0.9894817,0.0059074378,0.0010070138,0.00051293086,0.000009967528,0.000612042,0.000035512872],"genre_scores_gemma":[0.33561218,0.000018157014,0.66368675,0.00017339655,0.00019395769,0.00025279008,0.000004411375,0.000015963045,0.000042370462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989363,0.000025472795,0.00022161959,0.00036296184,0.0001751506,0.0002784891],"domain_scores_gemma":[0.9979914,0.0014187708,0.000056165707,0.00031716208,0.00011807332,0.00009844467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004587845,0.00013749399,0.00012298817,0.00008201485,0.0001287244,0.00024644885,0.00026796403,0.000076565804,0.0000039203446],"category_scores_gemma":[0.00085278216,0.00013824673,0.00009318976,0.00036042725,0.000009903226,0.0004554688,0.00004167044,0.00023677964,0.00005457699],"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":[0.000004253195,0.00005794851,4.8997725e-7,0.000034757395,0.000040024213,0.0000038067185,0.00015510203,0.58638185,0.00059276115,0.41045415,0.000040852497,0.0022339874],"study_design_scores_gemma":[0.00046823133,0.00001968091,0.000049649294,0.00008851913,0.00004179801,0.000020625013,0.00001502398,0.93162966,0.000082260325,0.00066232553,0.06677296,0.00014927186],"about_ca_topic_score_codex":0.000004847445,"about_ca_topic_score_gemma":9.941688e-8,"teacher_disagreement_score":0.40979183,"about_ca_system_score_codex":0.00010516107,"about_ca_system_score_gemma":0.00007310694,"threshold_uncertainty_score":0.5637538},"labels":[],"label_agreement":null},{"id":"W4405090288","doi":"10.48550/arxiv.2412.03330","title":"Testing CPS with Design Assumptions-Based Metamorphic Relations and Genetic Programming","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; HORIZON EUROPE Framework Programme; Fonds National de la Recherche Luxembourg; European Commission; Université du Luxembourg","keywords":"Genetic programming; Computer science; Metamorphic rock; Programming language; Artificial intelligence; Geology; Geochemistry","score_opus":0.10069527051975839,"score_gpt":0.19483084433641687,"score_spread":0.09413557381665848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405090288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031253986,0.00031383696,0.9667248,0.0004240276,0.00007076449,0.0005193457,0.000008526338,0.0004304795,0.00025419798],"genre_scores_gemma":[0.60660356,0.00000745887,0.392951,0.000015937154,0.00002788956,0.0000124249445,0.0000066590583,0.000012894217,0.00036214237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846923,0.000095230775,0.0001588968,0.0009468997,0.00008831494,0.00024140016],"domain_scores_gemma":[0.99867046,0.00021169678,0.00013671546,0.0006831303,0.00016574476,0.0001322752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016426975,0.00023524021,0.0001744246,0.0002650803,0.00035905058,0.00022140423,0.0005722968,0.00013888588,0.000006734405],"category_scores_gemma":[0.00002186665,0.00024219292,0.00006500717,0.0010813597,0.00013890049,0.00016287781,0.00070874905,0.0005074786,0.00005784822],"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":[0.000004256814,0.00009550872,0.0031838098,0.00009180921,0.00008319324,0.0002160226,0.000063457475,0.9001651,0.000038529033,0.09233793,0.00010079142,0.0036196082],"study_design_scores_gemma":[0.00015668417,0.00005840569,0.010015984,0.000113254864,0.00017108515,0.000024643774,0.000012809195,0.93856287,0.000011282279,0.050376557,0.00020260837,0.00029384595],"about_ca_topic_score_codex":0.000061015042,"about_ca_topic_score_gemma":0.000012771936,"teacher_disagreement_score":0.5753496,"about_ca_system_score_codex":0.000112304704,"about_ca_system_score_gemma":0.00039575587,"threshold_uncertainty_score":0.98763406},"labels":[],"label_agreement":null},{"id":"W4405254983","doi":"10.48550/arxiv.2412.07165","title":"A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Hyperparameter; Sensitivity (control systems); Reinforcement learning; Reinforcement; Machine learning; Artificial intelligence; Psychology; Computer science; Social psychology; Engineering","score_opus":0.11148995560475027,"score_gpt":0.2707874725826567,"score_spread":0.1592975169779064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405254983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046954952,0.00004490843,0.95101994,0.00025977835,0.00017566493,0.0005404193,0.000004988212,0.00016656645,0.0008327884],"genre_scores_gemma":[0.8049072,0.000013739019,0.19331937,0.00004859841,0.00004572531,0.000013539635,0.000016008054,0.000011689423,0.0016241292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829364,0.00017309406,0.00020245287,0.00096505746,0.000084807245,0.00028097566],"domain_scores_gemma":[0.9987566,0.00037906674,0.00013205,0.00053676084,0.000124544,0.000070974806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011463915,0.00020344595,0.00023687983,0.00024273993,0.00014684242,0.00009800041,0.00045664248,0.00015993824,0.0000058661085],"category_scores_gemma":[0.00005231089,0.00023864249,0.00015609314,0.000519465,0.000029791092,0.00014519098,0.0017848369,0.000659482,0.000040548926],"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":[0.000005943477,0.00002487726,0.000095532036,0.00006831711,0.000025314952,0.000032196866,0.00016823536,0.83604205,0.00013170924,0.15982175,0.0000382506,0.003545829],"study_design_scores_gemma":[0.00020328016,0.000052807838,0.0002077985,0.00007346685,0.000038379054,0.0000047971685,0.00004073999,0.88732487,0.000058256584,0.111439474,0.00032691562,0.00022919403],"about_ca_topic_score_codex":0.00020193322,"about_ca_topic_score_gemma":0.000036475067,"teacher_disagreement_score":0.7579523,"about_ca_system_score_codex":0.00027956124,"about_ca_system_score_gemma":0.00020919394,"threshold_uncertainty_score":0.97315586},"labels":[],"label_agreement":null},{"id":"W4405468205","doi":"10.48550/arxiv.2412.10592","title":"Self-Exciting Random Evolutions (SEREs) and their Applications (Version 2)","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","score_opus":0.02734859515317418,"score_gpt":0.17761374449626435,"score_spread":0.15026514934309018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405468205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017669609,0.001115348,0.9731927,0.0007143745,0.00020891293,0.0007349133,0.000102706246,0.0008799095,0.0053815553],"genre_scores_gemma":[0.98516816,0.0005553474,0.012792406,0.000051070067,0.00016419946,0.000026448917,0.000047718582,0.000019889967,0.001174732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981806,0.000077442375,0.00020560628,0.0011678716,0.00007201035,0.00029641652],"domain_scores_gemma":[0.9983171,0.00017578308,0.00014293869,0.0010268993,0.00015286951,0.00018440705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021052046,0.00029765602,0.0002476358,0.00026756234,0.0005822611,0.0001789026,0.0010940107,0.00022852766,0.000013533942],"category_scores_gemma":[0.0000083450595,0.00031102274,0.00018650388,0.000824095,0.000120713994,0.00024640004,0.003098436,0.0006397242,0.00020003128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061766764,0.00014986428,0.00014517047,0.0001972395,0.00012225186,0.000019527142,0.00039884413,0.013214289,0.000046917172,0.9825305,0.0012926733,0.00187659],"study_design_scores_gemma":[0.00033024617,0.000011906056,0.00016059802,0.00004960606,0.00006536767,0.000015806536,0.00015521298,0.73904586,0.00001845856,0.25004306,0.0098049985,0.00029889966],"about_ca_topic_score_codex":0.00007165286,"about_ca_topic_score_gemma":0.000016403308,"teacher_disagreement_score":0.9674986,"about_ca_system_score_codex":0.00020946706,"about_ca_system_score_gemma":0.0002230792,"threshold_uncertainty_score":0.9999342},"labels":[],"label_agreement":null},{"id":"W4406575091","doi":"10.1016/s1558-0164(08)70284-0","title":"10.1016/s1558-0164(08)70284-0","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Sleep (system call); Audiology; Computer science","score_opus":0.005764958076330504,"score_gpt":0.17947520500583586,"score_spread":0.17371024692950535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406575091","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000042372136,0.000050282117,0.0028550245,0.0018799008,0.0000015992938,0.00014382297,0.000008043374,0.00025245134,0.9947665],"genre_scores_gemma":[0.00005294068,2.9102196e-7,0.01494777,0.00007959109,0.00011465285,0.00004590434,0.0000065920444,0.000008178011,0.9847441],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913037,0.000020337444,0.00014137814,0.00030175186,0.00017588086,0.00023025915],"domain_scores_gemma":[0.9992243,0.00003542871,0.000022045604,0.00053296454,0.000043222284,0.00014205216],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000952419,0.000101474165,0.000093763476,0.00004990357,0.00016066307,0.00007210056,0.00069158233,0.00003691453,0.9760274],"category_scores_gemma":[0.0000065737017,0.00010035197,0.0000467348,0.0004126583,0.000024738667,0.00023261119,0.000102247504,0.00007661136,0.99834985],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031568375,0.00008021728,1.423056e-8,0.0000013757339,0.0000061664455,0.0000025764275,0.00001915417,0.00027682094,0.000031161977,0.00032220062,0.6524888,0.34676832],"study_design_scores_gemma":[0.00009597653,0.000051353538,0.00008513047,0.0000046249074,0.000002592409,0.00001303084,2.4679605e-7,0.017378034,0.000035711342,0.00024515967,0.98195064,0.00013750512],"about_ca_topic_score_codex":0.000012796604,"about_ca_topic_score_gemma":6.7694394e-8,"teacher_disagreement_score":0.3466308,"about_ca_system_score_codex":0.000029856494,"about_ca_system_score_gemma":0.00003754902,"threshold_uncertainty_score":0.40922347},"labels":[],"label_agreement":null},{"id":"W4407240655","doi":"10.1016/j.ins.2025.121957","title":"NEEP-ADF: Neuro-encoded expression programming with automatically defined functions","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Taishan Scholar Project of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Expression (computer science); Artificial intelligence; Programming language","score_opus":0.011140181132587027,"score_gpt":0.2515186582510849,"score_spread":0.24037847711849786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407240655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005058174,0.000014710094,0.9724473,0.0045575453,0.00016312358,0.00026028557,0.0000020256045,0.00036994967,0.017126918],"genre_scores_gemma":[0.47461888,0.000004009319,0.52343726,0.0013055681,0.00002162656,0.00022322162,0.000010385175,0.0000021511053,0.00037692033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884766,0.00002522812,0.0003141394,0.00019003938,0.00040179616,0.00022114023],"domain_scores_gemma":[0.9991847,0.00015296703,0.00012564709,0.00030052444,0.00017335116,0.00006279296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002835768,0.00009912915,0.00008700261,0.00026399357,0.00087998743,0.00059011404,0.0007087023,0.00003495439,0.000011057747],"category_scores_gemma":[0.00008796599,0.000071335686,0.000030351086,0.0019443847,0.00018380031,0.0038080604,0.00015258507,0.0000872649,0.000099876306],"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":[0.000007604978,0.00013451348,0.0015092414,0.000039626986,0.000011151856,8.6726965e-7,0.001257124,0.009453923,0.0006649381,0.67204726,0.008658777,0.306215],"study_design_scores_gemma":[0.00039389543,0.00015817359,0.009840301,0.000081053004,0.000007523058,0.000020568586,0.0006209615,0.89344186,0.00072439486,0.0075449995,0.08693467,0.00023157679],"about_ca_topic_score_codex":0.000017935188,"about_ca_topic_score_gemma":0.0000044308676,"teacher_disagreement_score":0.88398796,"about_ca_system_score_codex":0.00002666657,"about_ca_system_score_gemma":0.00027724844,"threshold_uncertainty_score":0.6768241},"labels":[],"label_agreement":null},{"id":"W4407394592","doi":"10.1126/sciadv.adr7338","title":"Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants","year":2025,"lang":"en","type":"article","venue":"Science Advances","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Benchmark (surveying); Machine learning; Identification (biology); Toolbox; Sampling (signal processing); Artificial intelligence; Data mining; Biology","score_opus":0.030003625555809713,"score_gpt":0.3482759882634606,"score_spread":0.3182723627076509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407394592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037480372,0.00045149348,0.9932242,0.0012740962,0.00028979575,0.0008862071,0.000006129364,0.00008892925,0.000031131956],"genre_scores_gemma":[0.22797002,0.000015862708,0.7714032,0.000038900158,0.000019535908,0.00033760758,0.0000012083293,0.0000029025261,0.00021078295],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857366,0.00011464655,0.00034473877,0.0004881353,0.00023513511,0.00024366262],"domain_scores_gemma":[0.99849653,0.00043510372,0.00022790182,0.00048044117,0.00032583837,0.000034168344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031957752,0.00009783927,0.00012853066,0.00012397149,0.0010081545,0.00015419818,0.0015298881,0.00002525934,0.0000010666041],"category_scores_gemma":[0.00045556217,0.000071615104,0.00003925583,0.0014615981,0.00026718946,0.0011330267,0.00020035414,0.00009967475,0.0000017292731],"study_design_candidate":"bench_or_experimental","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":[0.0000050474564,0.000040357616,0.000009996018,0.000030524043,0.000004856465,3.380405e-8,0.00015834767,0.0058202343,0.6462429,0.116669774,8.364267e-7,0.23101707],"study_design_scores_gemma":[0.00012303944,0.000033324544,0.00042296015,0.000014281105,0.000005912172,5.8273065e-7,0.000056458495,0.5723242,0.3813604,0.045048293,0.0005417026,0.00006881093],"about_ca_topic_score_codex":0.000026373298,"about_ca_topic_score_gemma":0.0000012757234,"teacher_disagreement_score":0.566504,"about_ca_system_score_codex":0.00004540961,"about_ca_system_score_gemma":0.0002875555,"threshold_uncertainty_score":0.7754011},"labels":[],"label_agreement":null},{"id":"W4407684169","doi":"10.48550/arxiv.2502.10760","title":"Why is prompting hard? Understanding prompts on binary sequence predictors","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Sequence (biology); Binary number; Psychology; Computer science; Genetics; Mathematics; Biology; Arithmetic","score_opus":0.13581339880455442,"score_gpt":0.30891189849653033,"score_spread":0.1730984996919759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407684169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3075095,0.00041356764,0.6398451,0.037932027,0.0024296027,0.002306706,0.00027957108,0.0016001388,0.00768381],"genre_scores_gemma":[0.937929,0.00019190238,0.050619934,0.0060040494,0.0005898926,0.0007872935,0.00014102765,0.000048282436,0.003688622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723476,0.00008308153,0.00044366077,0.001325021,0.00044961442,0.00046388325],"domain_scores_gemma":[0.9978873,0.00013575844,0.00028157543,0.0014357042,0.00011992974,0.0001397346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030373517,0.00038901618,0.00032084135,0.00024918586,0.00054219173,0.00018258597,0.0017443014,0.00027176776,0.00002829154],"category_scores_gemma":[0.000045137884,0.0003907657,0.00017042787,0.00058401783,0.00011468415,0.00033496416,0.0018522778,0.00086210296,0.00008529014],"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":[0.000052644693,0.0020277915,0.3055003,0.0022735028,0.0010035511,0.00023908506,0.008673216,0.01933897,0.005559825,0.34502962,0.30511004,0.0051914575],"study_design_scores_gemma":[0.001464008,0.00067401805,0.23086126,0.005868923,0.0002168793,0.000055822034,0.0004650988,0.54665464,0.0045887134,0.12170869,0.083586715,0.0038552415],"about_ca_topic_score_codex":0.00009607227,"about_ca_topic_score_gemma":0.000004621858,"teacher_disagreement_score":0.6304195,"about_ca_system_score_codex":0.00056546484,"about_ca_system_score_gemma":0.00044118974,"threshold_uncertainty_score":0.99985445},"labels":[],"label_agreement":null},{"id":"W4408919023","doi":"10.21203/rs.3.rs-6233867/v1","title":"The Alpha-Alternator: Dynamic Adaptation To Varying Noise Levels In Sequences Using The Vendi Score For Improved Robustness and Performance","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Robustness (evolution); Alpha (finance); Alternator; Noise (video); Mathematics; Computer science; Control theory (sociology); Artificial intelligence; Statistics; Biology; Physics","score_opus":0.11772006856972303,"score_gpt":0.403362236107371,"score_spread":0.28564216753764793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408919023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26623973,0.0015532073,0.7236861,0.0053034783,0.00027177262,0.0027459369,0.00011521834,0.000042699925,0.000041840492],"genre_scores_gemma":[0.96095765,0.00048483963,0.03691376,0.000033790806,0.00008637974,0.0012669428,0.00001623807,0.00001046934,0.00022991876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810517,0.00018907397,0.00027124377,0.0005753432,0.0004016829,0.00045750078],"domain_scores_gemma":[0.99797183,0.00072953367,0.00008790626,0.00073112105,0.00040797604,0.000071608425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018583268,0.00015363889,0.00014887923,0.00021805246,0.0012048808,0.00047646603,0.0015015088,0.00009321772,6.790373e-7],"category_scores_gemma":[0.00012785743,0.00010503975,0.000051457886,0.00064629584,0.0001356975,0.00024708448,0.0014949462,0.00061724655,0.000001061111],"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":[0.000064732485,0.00009885075,0.001189964,0.001024622,0.000057775458,0.0000040455598,0.0039200266,0.68552774,0.0010165811,0.016053544,0.000102832855,0.29093924],"study_design_scores_gemma":[0.00012833865,0.000047650647,0.009308461,0.00045220045,0.0000032684195,0.000002974722,0.00021012918,0.9847273,0.0000644951,0.0046646283,0.0002707991,0.000119728094],"about_ca_topic_score_codex":0.0003628151,"about_ca_topic_score_gemma":0.00023231372,"teacher_disagreement_score":0.69471794,"about_ca_system_score_codex":0.0003309057,"about_ca_system_score_gemma":0.0007548179,"threshold_uncertainty_score":0.92670906},"labels":[],"label_agreement":null},{"id":"W4409121959","doi":"10.1007/s11227-025-07132-x","title":"SPINEX-symbolic regression: similarity-based symbolic regression with explainable neighbors exploration","year":2025,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Clemson University","keywords":"Symbolic regression; Computer science; Similarity (geometry); Regression; The Symbolic; Regression analysis; Symbolic data analysis; Artificial intelligence; Theoretical computer science; Machine learning; Statistics; Mathematics; Genetic programming","score_opus":0.01733863392500166,"score_gpt":0.26642008987218996,"score_spread":0.2490814559471883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409121959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11054438,0.000756881,0.8734838,0.014157534,0.00026274132,0.0001922063,6.9243026e-7,0.00007036543,0.0005313949],"genre_scores_gemma":[0.9128422,0.000064809814,0.08595091,0.0007142885,0.00027983662,0.0000064753217,0.0000027276628,0.000012235027,0.00012650782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998228,0.00023024016,0.0005470308,0.00022172478,0.0004641837,0.000308787],"domain_scores_gemma":[0.99829775,0.0003148206,0.00033127982,0.00056852447,0.00038951988,0.00009811266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011405364,0.00020814447,0.0002891422,0.0002654698,0.00088015356,0.00014439673,0.0011313782,0.00007355918,0.0000072245025],"category_scores_gemma":[0.000056056677,0.000116960095,0.000098805205,0.0011417542,0.0000903779,0.0009994231,0.00020634063,0.00046130506,0.0000038023056],"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":[0.00052405964,0.0021793758,0.011937817,0.0004847779,0.00045540734,0.00028215963,0.017524386,0.41170028,0.025493313,0.3149216,0.033036508,0.18146034],"study_design_scores_gemma":[0.0021709977,0.00043754803,0.0070424424,0.0023426036,0.000104017905,0.00033381482,0.002143391,0.9483478,0.009626905,0.021056619,0.0059230053,0.000470873],"about_ca_topic_score_codex":0.0000257799,"about_ca_topic_score_gemma":0.0000032107575,"teacher_disagreement_score":0.80229783,"about_ca_system_score_codex":0.000097069984,"about_ca_system_score_gemma":0.00034397258,"threshold_uncertainty_score":0.6769518},"labels":[],"label_agreement":null},{"id":"W4409200114","doi":"10.1016/j.automatica.2025.112290","title":"Algorithms enhancement for optimal triggering control in logical dynamic systems: Leveraging data structure storage","year":2025,"lang":"en","type":"article","venue":"Automatica","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Innovation and Technology Commission; National Natural Science Foundation of China","keywords":"Computer science; Control (management); Algorithm; Artificial intelligence","score_opus":0.019753706924003616,"score_gpt":0.2931144804648437,"score_spread":0.27336077354084004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409200114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068848575,0.00042240415,0.989766,0.0015364283,0.0002705733,0.0007672508,0.00007370712,0.00014891625,0.00012983575],"genre_scores_gemma":[0.7662038,0.0000067490105,0.23320581,0.00014063616,0.000027391281,0.00018272582,0.000047681795,0.0000052295795,0.00017995603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986318,0.000048599693,0.00037080655,0.00049426354,0.0001666864,0.00028788863],"domain_scores_gemma":[0.9987247,0.00024645973,0.0000795236,0.0008589536,0.000045667697,0.000044685956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033221507,0.00013633675,0.00023637901,0.00011333573,0.0001633534,0.00014566552,0.001170163,0.000061376384,0.000007889284],"category_scores_gemma":[0.000037688544,0.00012336722,0.000032437438,0.00034542452,0.000030589617,0.00034499206,0.00031901035,0.00011949105,0.0000054767397],"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":[0.000046008277,0.0012490362,0.0002330146,0.0011401194,0.00047006944,0.000065514745,0.0016078163,0.16598363,0.008416327,0.53044087,0.008503111,0.28184447],"study_design_scores_gemma":[0.0007066088,0.000025287398,0.001294141,0.00007670911,0.00001118056,0.000005776759,0.000045798668,0.99264175,0.000024029008,0.0018324556,0.00320936,0.00012691328],"about_ca_topic_score_codex":0.00001653312,"about_ca_topic_score_gemma":0.0000031157135,"teacher_disagreement_score":0.8266581,"about_ca_system_score_codex":0.00014004527,"about_ca_system_score_gemma":0.00011638869,"threshold_uncertainty_score":0.503077},"labels":[],"label_agreement":null},{"id":"W4409278218","doi":"10.1101/2025.04.03.646975","title":"REvolutionH-tl: A Fast and Robust Tool for Decoding Evolutionary Gene Histories","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Decoding methods; Gene; Genetics; Biology; Evolutionary biology; Computer science; Computational biology; Algorithm","score_opus":0.016288723684395396,"score_gpt":0.22140098843240097,"score_spread":0.20511226474800556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409278218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069606807,0.00515006,0.9824052,0.0015152572,0.001452677,0.0011934609,0.0006281955,0.00067758246,0.00001690826],"genre_scores_gemma":[0.123528436,0.0004982553,0.8733204,0.000235369,0.00066158193,0.0015795323,0.000002116276,0.000049616934,0.00012470425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699354,0.00007694152,0.0006050623,0.0014362902,0.0003280505,0.0005601251],"domain_scores_gemma":[0.9969373,0.00018797806,0.00035687524,0.0015362165,0.0007841769,0.00019746293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004955401,0.0005035009,0.0004861417,0.00033972843,0.00079584937,0.00027549808,0.0011449168,0.00042071068,0.0000056495805],"category_scores_gemma":[0.00019476979,0.0005948132,0.00018113699,0.00066850084,0.00017002631,0.00047878444,0.0013470188,0.00046620975,0.000008715449],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006105472,0.00085586763,0.0077485233,0.0023190372,0.0006298593,0.00003640433,0.00013161285,0.008373446,0.086918354,0.8612399,0.031509664,0.00017624708],"study_design_scores_gemma":[0.0025857955,0.00022566349,0.13822037,0.0014773727,0.0005002304,6.689496e-7,0.000013188429,0.69000226,0.01871082,0.00181096,0.14150505,0.004947596],"about_ca_topic_score_codex":0.00003238953,"about_ca_topic_score_gemma":0.0000016625721,"teacher_disagreement_score":0.85942894,"about_ca_system_score_codex":0.00072967965,"about_ca_system_score_gemma":0.0011513844,"threshold_uncertainty_score":0.9996503},"labels":[],"label_agreement":null},{"id":"W4409543171","doi":"10.1016/j.eswa.2025.127487","title":"Predicting wind turbines faults using Multi-Objective Genetic Programming","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Genetic programming; Computer science; Wind power; Genetic algorithm; Machine learning; Electrical engineering","score_opus":0.01614303241012859,"score_gpt":0.2805800546306219,"score_spread":0.2644370222204933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409543171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006363664,0.0019533238,0.9884445,0.00030508274,0.00013211198,0.0019286816,0.0000075304097,0.00040338768,0.00046172467],"genre_scores_gemma":[0.54717475,0.000012830845,0.44914603,0.00006926275,0.00020750497,0.0028405196,0.000009542716,0.000016913158,0.0005226351],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840957,0.000048660568,0.00036259825,0.00063864526,0.00022078435,0.00031975884],"domain_scores_gemma":[0.99856305,0.00008933909,0.00016609013,0.00081225595,0.00026985948,0.000099432305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114243114,0.0002037156,0.00019673657,0.00016383387,0.0007780716,0.0001600388,0.00064940925,0.00007951417,9.736171e-7],"category_scores_gemma":[0.000010803262,0.00017665148,0.000048337497,0.0012432849,0.000084864405,0.00029271396,0.00014956489,0.00013167404,0.000014813421],"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":[0.000052762018,0.0047736806,0.16005407,0.0008044068,0.0013406563,0.000036790938,0.019905956,0.13626724,0.027425257,0.4838668,0.0021878465,0.16328451],"study_design_scores_gemma":[0.00075201615,0.000047476446,0.0139729595,0.0002397684,0.000027194923,0.00010504724,0.0013120265,0.9359566,0.00038795365,0.00026770346,0.04648138,0.00044990715],"about_ca_topic_score_codex":0.00058159366,"about_ca_topic_score_gemma":0.0000216991,"teacher_disagreement_score":0.79968935,"about_ca_system_score_codex":0.00014281823,"about_ca_system_score_gemma":0.00020249894,"threshold_uncertainty_score":0.7203638},"labels":[],"label_agreement":null},{"id":"W4409694726","doi":"10.1007/978-3-031-90065-5","title":"Applications of Evolutionary Computation","year":2025,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"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 Institute of Information and Communications Technology; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Sorbonne Université; Région Normandie; Universität Konstanz; University of South Africa; Universidad de Granada; Syddansk Universitet; Technische Universität Berlin; Universidade de Lisboa; Universidade de Coimbra; Universitat Oberta de Catalunya; Universitetet i Oslo; Hong Kong Baptist University; Università degli Studi di Milano-Bicocca; Universidad Complutense de Madrid; University of Waterloo; University of Twente; Universidad de Extremadura; Università degli Studi di Torino; Università degli Studi di Trento; Università degli Studi di Parma; Heriot-Watt University; Queensland University of Technology; Iran Telecommunication Research Center; Swansea University; Edinburgh Napier University; Technische Universität Darmstadt; Cardiff University; Pomona College; Université de Lille; Universidad de Málaga; Carl von Ossietzky Universität Oldenburg; Università Degli Studi di Modena e Reggio Emila; Universiteit Leiden; Institut \"Jožef Stefan\"; Yeditepe Üniversitesi; Massachusetts Institute of Technology; University of North Carolina Wilmington; Eötvös Loránd Tudományegyetem; Silesian University of Technology; Université du Luxembourg; Sveučilište u Zagrebu; Aberystwyth University","keywords":"Computer science; Evolutionary computation; Computation; Artificial intelligence; Algorithm","score_opus":0.008527978703050977,"score_gpt":0.2561397067334402,"score_spread":0.24761172803038925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409694726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031989118,0.0008679052,0.991635,0.0008065401,0.00043392702,0.00066765834,0.000023416893,0.0001346948,0.0054276595],"genre_scores_gemma":[0.010944143,0.0000538282,0.98625016,0.00040194404,0.00031198762,0.00014267777,0.00006453843,0.00001344003,0.0018172798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719256,0.000040679606,0.000595606,0.0010814162,0.00071840314,0.00037130783],"domain_scores_gemma":[0.9973951,0.00056777755,0.0003318765,0.0011006175,0.00051271834,0.00009189453],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037896566,0.00029790145,0.00038407446,0.0010182569,0.00028601504,0.000105683685,0.0026966583,0.00022754885,0.0000059188656],"category_scores_gemma":[0.000035624125,0.00030912823,0.00011981993,0.0026157505,0.00063834945,0.00041931437,0.0010039997,0.00045308948,0.000020676998],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015578257,0.0001456997,0.000045673147,0.00010728571,0.000012457276,0.0000032268788,0.0001754391,0.07903722,0.000035907928,0.07721653,0.0013548445,0.84186417],"study_design_scores_gemma":[0.00013654162,0.000042062213,0.00035719608,0.00017138965,0.00000700438,0.000014041681,1.3172316e-7,0.6144021,0.00012287924,0.37668166,0.0078019467,0.0002630317],"about_ca_topic_score_codex":0.000019121082,"about_ca_topic_score_gemma":0.000008787831,"teacher_disagreement_score":0.84160113,"about_ca_system_score_codex":0.00047070565,"about_ca_system_score_gemma":0.0026490162,"threshold_uncertainty_score":0.9999361},"labels":[],"label_agreement":null},{"id":"W4409771286","doi":"10.1109/tse.2025.3563121","title":"Testing CPS With Design Assumptions-Based Metamorphic Relations and Genetic Programming","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"HORIZON EUROPE European Innovation Council; Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; Science Foundation Ireland","keywords":"Computer science; Genetic programming; Programming language; Software engineering; Artificial intelligence","score_opus":0.01636564723815365,"score_gpt":0.2155209431462641,"score_spread":0.19915529590811046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409771286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018966545,0.00013405457,0.9964948,0.0002426925,0.00010815297,0.00034225214,0.0000039847428,0.0007675651,0.00000980077],"genre_scores_gemma":[0.30198994,0.000002410995,0.69759846,0.000030634077,0.00000893576,0.0002728383,7.3437644e-7,0.000011331631,0.00008473471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999079,0.000024215722,0.00018931023,0.00034368518,0.00014504488,0.00021873841],"domain_scores_gemma":[0.9990232,0.00043996412,0.000035944824,0.00033477557,0.000091002585,0.00007512296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098976125,0.00016539634,0.00012214112,0.00029494823,0.0004087484,0.0001090316,0.00020059707,0.000058706573,0.0000044544167],"category_scores_gemma":[0.000023284541,0.00016474546,0.00003816111,0.0011307904,0.00003409833,0.00023787862,0.0000026616942,0.0002308408,0.000008372981],"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":[0.0000020740583,0.00007039552,0.00022719459,0.000021804375,0.00003406523,0.0000044078097,0.000026078202,0.9625281,0.00025704005,0.00060800597,0.000017604594,0.03620324],"study_design_scores_gemma":[0.0003284605,0.00008833686,0.012711813,0.0001379702,0.00005842032,0.00003046331,0.0000053744693,0.98446625,0.0012879681,0.00011400862,0.0005320268,0.00023893078],"about_ca_topic_score_codex":0.000010294799,"about_ca_topic_score_gemma":0.0000024384021,"teacher_disagreement_score":0.30009326,"about_ca_system_score_codex":0.000065286586,"about_ca_system_score_gemma":0.00011541034,"threshold_uncertainty_score":0.67181253},"labels":[],"label_agreement":null},{"id":"W4409852885","doi":"10.31468/dwr.1119","title":"Ctrl+AI+Learn","year":2025,"lang":"en","type":"article","venue":"Discourse and Writing/Rédactologie","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychology","score_opus":0.050864660082488314,"score_gpt":0.3887336018004602,"score_spread":0.3378689417179719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409852885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14191465,0.003395483,0.71655434,0.09404901,0.0004552915,0.0004039497,0.00001968214,0.0007797796,0.042427827],"genre_scores_gemma":[0.9839,0.00020404716,0.012750056,0.0014991704,0.000072490846,0.000045171633,0.000008480062,0.0000050237504,0.0015155411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990183,0.000029932022,0.0001835059,0.00037998383,0.00010528782,0.00028300125],"domain_scores_gemma":[0.9993155,0.00012761068,0.00004402531,0.00039170656,0.000056119356,0.000065011154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001589532,0.00012291754,0.00014473066,0.0000867575,0.00031732215,0.00015462501,0.0004581401,0.00007260636,0.000012816934],"category_scores_gemma":[0.000034152476,0.00010867098,0.00005121778,0.00032201144,0.00014054477,0.00033332044,0.00027248665,0.00018979183,0.000031845142],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018342552,0.00010294405,0.008651242,0.0000139734775,0.00001869413,0.000012800026,0.00007386881,0.000015695856,0.0002568363,0.8807683,0.008434498,0.101649344],"study_design_scores_gemma":[0.002035982,0.00024596677,0.36748523,0.00030877427,0.00010098357,0.0001279787,0.0028755728,0.09131862,0.0029235773,0.39152083,0.13956031,0.0014961624],"about_ca_topic_score_codex":0.000021443093,"about_ca_topic_score_gemma":0.0000038069145,"teacher_disagreement_score":0.84198534,"about_ca_system_score_codex":0.000020209878,"about_ca_system_score_gemma":0.000097308286,"threshold_uncertainty_score":0.44314742},"labels":[],"label_agreement":null},{"id":"W4409921220","doi":"10.1016/j.patter.2025.101242","title":"Fine-Pruning: A biologically inspired algorithm for personalization of machine learning models","year":2025,"lang":"en","type":"article","venue":"Patterns","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation; Israel Science Foundation; U.S. Department of Energy; National Science Foundation","keywords":"Pruning; Personalization; Computer science; Artificial intelligence; Machine learning; Algorithm; World Wide Web; Biology","score_opus":0.03328274057532203,"score_gpt":0.26833274286982406,"score_spread":0.23505000229450201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409921220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041973847,0.00015798758,0.99409384,0.0008683416,0.000054803706,0.00019124144,0.000038653117,0.00008383981,0.00031390233],"genre_scores_gemma":[0.7583022,0.00002855032,0.24053733,0.00016452858,0.000032854758,0.00012540571,0.00008950142,0.0000051792636,0.0007144491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993587,0.000028353901,0.00017794769,0.00022892328,0.00008055099,0.00012552127],"domain_scores_gemma":[0.9995242,0.000088399385,0.000078123085,0.00017228113,0.00011202013,0.00002499656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012352751,0.00007743195,0.00011154683,0.000077395154,0.00012885658,0.000022727121,0.00034124198,0.00004345667,0.00000788322],"category_scores_gemma":[0.000020373944,0.00006979191,0.000059818227,0.00025213507,0.000019968493,0.00013543734,0.000113571565,0.00006275691,0.0000015224841],"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":[0.000007713136,0.00038510142,0.018403959,0.00013045712,0.00008433131,0.0000016124726,0.0009984603,0.014823643,0.0033748779,0.40616655,0.0004165242,0.5552068],"study_design_scores_gemma":[0.00023384728,0.00005539578,0.0029923415,0.000026881446,0.000005348077,0.0000010149804,0.000014329041,0.9838743,0.00035181476,0.01103242,0.001340727,0.000071531416],"about_ca_topic_score_codex":0.00005075312,"about_ca_topic_score_gemma":0.0000075846115,"teacher_disagreement_score":0.9690507,"about_ca_system_score_codex":0.000018654617,"about_ca_system_score_gemma":0.000035441255,"threshold_uncertainty_score":0.28460315},"labels":[],"label_agreement":null},{"id":"W4409958101","doi":"10.3389/fgene.2025.1569358","title":"Constructing ancestral recombination graphs through reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Reinforcement learning; Recombination; Reinforcement; Computer science; Biology; Evolutionary biology; Artificial intelligence; Genetics; Psychology; Gene; Social psychology","score_opus":0.01157861896777953,"score_gpt":0.25415070241463295,"score_spread":0.2425720834468534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409958101","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007828928,0.0005906933,0.98093307,0.0006641179,0.0009069599,0.00013344262,3.3548167e-7,0.00005521456,0.008887232],"genre_scores_gemma":[0.3701271,0.00028523392,0.6288617,0.00008572664,0.00001444678,0.00002474865,0.0000071313866,0.00000274025,0.00059112615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999283,0.000029618966,0.00020340059,0.00021320808,0.0001028467,0.00016793949],"domain_scores_gemma":[0.99966335,0.00001911554,0.000062811654,0.00019343972,0.000043127726,0.00001816495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011158927,0.000071578215,0.00008635895,0.000105177896,0.00013441814,0.00004719088,0.0003186477,0.000044508302,0.0000020476625],"category_scores_gemma":[0.000015057014,0.00008227085,0.000027407252,0.00059679244,0.000051601713,0.00016388761,0.00009119391,0.00013824884,0.0000017401356],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022350007,0.00004574661,0.13551551,0.000019245805,0.00002133392,0.0000019121371,0.0004861948,0.04605921,0.000043178567,0.60473174,0.0061674765,0.20690621],"study_design_scores_gemma":[0.00044538022,0.000041985277,0.009675669,0.000041045783,0.0000054849775,0.000002767158,0.00065277226,0.7520564,0.00083872356,0.22235526,0.013716722,0.00016776723],"about_ca_topic_score_codex":0.000010581726,"about_ca_topic_score_gemma":0.0000027294773,"teacher_disagreement_score":0.7059972,"about_ca_system_score_codex":0.0000940278,"about_ca_system_score_gemma":0.000059596354,"threshold_uncertainty_score":0.33549082},"labels":[],"label_agreement":null},{"id":"W4410398148","doi":"10.1007/978-1-4614-6624-6_52-1","title":"Computing Distances Between Evolutionary Trees","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Evolutionary biology; Biology","score_opus":0.017450258892019514,"score_gpt":0.24873660732839176,"score_spread":0.23128634843637225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410398148","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.6111653e-7,0.00085638964,0.46817738,0.0011127191,0.00012505015,0.00012004702,0.00003838049,0.00024843594,0.52932096],"genre_scores_gemma":[0.0018800609,0.0001020132,0.13609241,0.0001640514,0.00046649942,0.00000897179,0.00011440996,0.0000132489,0.8611583],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845207,0.000010936991,0.00037769755,0.00062200136,0.0003126607,0.0002246438],"domain_scores_gemma":[0.9987012,0.0002461765,0.00015799263,0.0006914933,0.00011899643,0.00008412409],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009634598,0.00027351177,0.00030452517,0.0001579408,0.00035412394,0.00008384517,0.0011418934,0.00018946444,0.00009647657],"category_scores_gemma":[0.0000057556667,0.0002612348,0.00016554614,0.00010658254,0.00010301404,0.00020679679,0.0005388115,0.00027779074,0.00017391477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.3739067e-7,0.000008487819,0.00005334196,0.000010263281,0.000034845514,0.0000021021597,0.000012087866,0.00001866936,4.322415e-7,0.9208793,0.03578886,0.04319134],"study_design_scores_gemma":[0.0000838234,0.000020240981,0.0018671488,0.00012701708,0.000022786571,0.000003885814,0.0000032542446,0.013454212,0.000002377861,0.27113998,0.7129208,0.00035451548],"about_ca_topic_score_codex":0.000016315173,"about_ca_topic_score_gemma":0.000009794909,"teacher_disagreement_score":0.6771319,"about_ca_system_score_codex":0.00010738207,"about_ca_system_score_gemma":0.00020287998,"threshold_uncertainty_score":0.99998397},"labels":[],"label_agreement":null},{"id":"W4410427548","doi":"10.1109/access.2025.3570922","title":"A Biologically Inspired Program-Level Imitation Approach for Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs","keywords":"Computer science; Robot; Imitation; Artificial intelligence; Human–computer interaction; Psychology","score_opus":0.09777814735732306,"score_gpt":0.3644676978081124,"score_spread":0.26668955045078935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410427548","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025440762,0.000047427646,0.9921243,0.0017502056,0.00016353754,0.0008212459,0.000008964639,0.0002495484,0.0022906917],"genre_scores_gemma":[0.29800355,0.000007695732,0.6991156,0.00053410814,0.00006378053,0.0018203564,0.00003381408,0.000003575722,0.00041755478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921656,0.000019941006,0.0001682247,0.00034011537,0.00008086334,0.00017431293],"domain_scores_gemma":[0.9993787,0.000080356745,0.000058347563,0.00030002627,0.00014787762,0.000034715344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013011096,0.00008514558,0.00009630023,0.00007658545,0.0001985176,0.00020718428,0.0010254101,0.00006261352,9.61608e-7],"category_scores_gemma":[0.000028228607,0.0000716148,0.000054581964,0.0006080226,0.00003718483,0.00044276472,0.000119620934,0.0000543565,0.000003606903],"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":[0.000015144085,0.0010297896,0.0014445739,0.00007163912,0.00004956997,7.2353333e-7,0.00009331271,0.006625986,0.0014088172,0.38268387,0.013538424,0.59303814],"study_design_scores_gemma":[0.00070856337,0.00010819935,0.07407089,0.000017278137,0.000013805588,0.000002485615,0.000013667097,0.83931845,0.0015100082,0.07233266,0.011632951,0.00027103958],"about_ca_topic_score_codex":0.000016566384,"about_ca_topic_score_gemma":0.0000024757767,"teacher_disagreement_score":0.83269244,"about_ca_system_score_codex":0.000026338854,"about_ca_system_score_gemma":0.000071890034,"threshold_uncertainty_score":0.29203668},"labels":[],"label_agreement":null},{"id":"W4410492186","doi":"10.2139/ssrn.5257327","title":"Why is Dating Exclusive? Signaling, Mimicking Behavior, and the Impact of Dating Technologies","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Laval","funders":"","keywords":"Dating violence; Medicine","score_opus":0.013026445975623647,"score_gpt":0.29490185030259225,"score_spread":0.2818754043269686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410492186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07193843,0.01335873,0.9093935,0.00403284,0.00014661181,0.000532269,0.000026492713,0.00015858772,0.00041255742],"genre_scores_gemma":[0.96577036,0.0061534923,0.02757653,0.00010001055,0.00012546897,0.0000821593,0.000008795096,0.000014876009,0.00016829328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977107,0.00011163051,0.0005097071,0.00041410743,0.00025164406,0.0010022238],"domain_scores_gemma":[0.9982196,0.00022085325,0.0007372484,0.0006388759,0.00015483967,0.00002855742],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0014513035,0.000247078,0.0003429647,0.00020807686,0.00058594404,0.00021961179,0.0018593546,0.00017389253,0.0000053926146],"category_scores_gemma":[0.000089859386,0.00017162874,0.00025076183,0.00034660407,0.00019835946,0.0002255537,0.0021094235,0.0031749872,4.0148535e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024272967,0.00020073373,0.0043748445,0.00007396232,0.00086621515,0.000004360461,0.0018850387,0.005440692,0.0015491064,0.7466608,0.0009166306,0.23800334],"study_design_scores_gemma":[0.00076519244,0.00011646346,0.00043395677,0.00034700352,0.00012691088,0.0003749958,0.0018690461,0.09967585,0.0006468354,0.8951624,0.000133701,0.00034764988],"about_ca_topic_score_codex":0.00035990227,"about_ca_topic_score_gemma":0.000016738777,"teacher_disagreement_score":0.89383197,"about_ca_system_score_codex":0.00036075147,"about_ca_system_score_gemma":0.0020170826,"threshold_uncertainty_score":0.9991247},"labels":[],"label_agreement":null},{"id":"W4410610472","doi":"10.1016/j.buildenv.2025.113170","title":"An economic room-level thermal management of air-cooled cloud data centers based on human brain emotional intelligence","year":2025,"lang":"en","type":"article","venue":"Building and Environment","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Emotional intelligence; Psychology; Environmental science; Business; Engineering; Computer science; Social psychology; Operating system","score_opus":0.033883484528934395,"score_gpt":0.28036408985791267,"score_spread":0.24648060532897828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410610472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13110593,0.000036004672,0.8662406,0.0019964555,0.00006677933,0.00013134241,0.000042465595,0.00002283404,0.0003575747],"genre_scores_gemma":[0.927116,0.000029201552,0.07239673,0.00024350034,0.0000250896,0.000014132758,0.00003793022,0.000003832596,0.00013360142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991631,0.000029501422,0.0001721953,0.00041034172,0.000103944934,0.00012092317],"domain_scores_gemma":[0.999131,0.000033554395,0.00005104684,0.0007385937,0.0000020900636,0.00004370873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022567403,0.000091254406,0.000080374164,0.000053242627,0.00015781318,0.000022937218,0.00068356603,0.00002317459,0.000021940377],"category_scores_gemma":[7.647264e-7,0.000092020295,0.000021156577,0.000041111685,0.00004977942,0.00012625786,0.00033033366,0.000050829018,0.000006822135],"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":[0.00001388436,0.00073391077,0.002294116,0.000052377964,0.00007810017,0.0000034645338,0.00006772524,0.43014038,0.0037371526,0.4328555,0.0014139136,0.1286095],"study_design_scores_gemma":[0.00026915895,0.00006927462,0.18941206,0.00007709087,0.000011794614,7.292419e-7,0.00003617095,0.80303985,0.000797074,0.0029119481,0.003232555,0.00014229071],"about_ca_topic_score_codex":0.000023164746,"about_ca_topic_score_gemma":7.4017595e-7,"teacher_disagreement_score":0.7960101,"about_ca_system_score_codex":0.00006875109,"about_ca_system_score_gemma":0.000010965996,"threshold_uncertainty_score":0.37524787},"labels":[],"label_agreement":null},{"id":"W4410738697","doi":"10.1109/ciescompanion65073.2025.11010904","title":"Validation-based Decision Making in Data-driven Evolutionary Computation: A Case Study in Multi-objective Feature Selection","year":2025,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Wilfrid Laurier University","funders":"","keywords":"Feature selection; Computer science; Evolutionary computation; Artificial intelligence; Machine learning; Selection (genetic algorithm); Computation; Feature (linguistics); Data mining; Algorithm","score_opus":0.036870381968374624,"score_gpt":0.35318435986654967,"score_spread":0.31631397789817506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410738697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094911344,0.000049430848,0.90312034,0.000708718,0.00012049395,0.0008171998,0.000008633663,0.000120234385,0.00014360268],"genre_scores_gemma":[0.66062874,8.160026e-7,0.3390918,0.00008610542,0.000012722715,0.00009222882,0.000026701025,0.0000036419342,0.00005723343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983523,0.00017502581,0.00033597008,0.0007206251,0.00022503192,0.00019106142],"domain_scores_gemma":[0.99873537,0.00047876753,0.00007827033,0.0005076305,0.00016979506,0.000030160252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037598266,0.00014408122,0.0001585608,0.000600745,0.0002625424,0.00009875559,0.0005544783,0.00007755531,0.0000069821017],"category_scores_gemma":[0.000070065675,0.0001469319,0.00002678377,0.0027587896,0.00002384186,0.000739642,0.00033190666,0.0002430558,0.000009155114],"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":[0.000036403602,0.0034494945,0.22346625,0.000018595496,0.000033532182,0.00049589,0.0011520187,0.7227259,0.00003717333,0.005321463,0.002997523,0.040265735],"study_design_scores_gemma":[0.0009845075,0.00003239568,0.17806603,0.000047511592,0.000005035003,0.0000729595,0.0005518657,0.8189817,0.000006025189,0.0010875294,0.000047336704,0.00011707189],"about_ca_topic_score_codex":0.0005938008,"about_ca_topic_score_gemma":0.0037422625,"teacher_disagreement_score":0.5657174,"about_ca_system_score_codex":0.0003494318,"about_ca_system_score_gemma":0.00034590924,"threshold_uncertainty_score":0.5991709},"labels":[],"label_agreement":null},{"id":"W4411335038","doi":"10.1101/2025.06.13.659495","title":"Paralog interference preserves genetic redundancy","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; PROTEO","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Interference (communication); Computer science; Telecommunications","score_opus":0.01650423017213794,"score_gpt":0.23464784609830824,"score_spread":0.2181436159261703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411335038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070286386,0.0045563825,0.9182367,0.0019304255,0.0020971692,0.0011492034,0.0002601204,0.0013508745,0.00013274001],"genre_scores_gemma":[0.7905924,0.0004046105,0.20785068,0.00016120999,0.0002858994,0.00059640524,2.485574e-7,0.000028674684,0.000079896075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99707675,0.00012985032,0.0005604354,0.0013814005,0.00032154002,0.00053001376],"domain_scores_gemma":[0.9961229,0.0000893715,0.00030539112,0.0028079452,0.00044930095,0.00022510598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000264499,0.0004713733,0.00041876774,0.00025832347,0.00026908956,0.00044096375,0.0030808984,0.00036659135,0.000020918082],"category_scores_gemma":[0.00009219266,0.00051644596,0.00016373226,0.0007838392,0.0001263295,0.0003030343,0.0025691057,0.0007158158,0.00009297261],"study_design_candidate":"observational","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":[0.00005029279,0.003045813,0.036105912,0.0037010903,0.0013616572,0.0003320381,0.00020818491,0.006994383,0.2586342,0.6573118,0.031918205,0.00033642995],"study_design_scores_gemma":[0.00091535185,0.00016924665,0.75421,0.0020052148,0.00017623825,1.00220525e-7,0.0000027335402,0.13082674,0.07174327,0.0014672868,0.03522678,0.003257028],"about_ca_topic_score_codex":0.000071586706,"about_ca_topic_score_gemma":0.0000022996176,"teacher_disagreement_score":0.720306,"about_ca_system_score_codex":0.00017331373,"about_ca_system_score_gemma":0.00093704765,"threshold_uncertainty_score":0.99972874},"labels":[],"label_agreement":null},{"id":"W4412066749","doi":"10.55214/25768484.v9i7.8530","title":"Self-optimization of falaj irrigation using case-based reasoning algorithms","year":2025,"lang":"en","type":"article","venue":"Edelweiss Applied Science and Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Algorithm","score_opus":0.008829321260474858,"score_gpt":0.2508229381819166,"score_spread":0.24199361692144172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412066749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015945623,0.000101692305,0.9804107,0.0013751595,0.00008188867,0.00025415415,0.0000016018251,0.00028226845,0.0015469106],"genre_scores_gemma":[0.5069914,0.0000048812776,0.49289674,0.0000621629,0.0000070579063,0.000028414537,9.163844e-7,0.0000023782738,0.0000060739176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987946,0.000008966341,0.0002396401,0.00048592425,0.0002238383,0.0002470299],"domain_scores_gemma":[0.99889445,0.000039578434,0.00012978546,0.0004521807,0.00043962378,0.00004436799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047932577,0.00010865,0.00014541185,0.0007270664,0.00065329653,0.00007148065,0.0005569101,0.00011260129,0.0000017609295],"category_scores_gemma":[0.000049001475,0.000108963475,0.0000149146645,0.004419791,0.00055507827,0.00027881173,0.0002474907,0.00011787442,0.0000010333112],"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":[0.0000011902282,0.00008318889,0.00018736912,0.000020078453,0.0000069783287,0.000007008974,0.00007332765,0.011798173,0.010405827,0.9502578,0.000027619764,0.027131487],"study_design_scores_gemma":[0.00020375213,0.000021076308,0.00005097481,0.000016723423,0.000009048989,0.000070091686,0.00013997966,0.9705378,0.014470094,0.01410394,0.00027309274,0.00010341578],"about_ca_topic_score_codex":0.00002445613,"about_ca_topic_score_gemma":0.000001706998,"teacher_disagreement_score":0.95873964,"about_ca_system_score_codex":0.000090664755,"about_ca_system_score_gemma":0.00045868306,"threshold_uncertainty_score":0.5024695},"labels":[],"label_agreement":null},{"id":"W4412505633","doi":"10.70777/si.v2i3.15063","title":"Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents","year":2025,"lang":"en","type":"article","venue":"SuperIntelligence - Robotics - Safety & Alignment","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Darwin (ADL); Computer science; Software engineering","score_opus":0.017501471754553053,"score_gpt":0.28034707022915495,"score_spread":0.26284559847460187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412505633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006381384,0.00047698081,0.98963404,0.0017859992,0.0005485562,0.0008136421,0.000030892566,0.00015512809,0.005916605],"genre_scores_gemma":[0.6902934,0.00025921525,0.3083371,0.00018422543,0.000034181732,0.00007816588,0.000020549987,0.000014191617,0.0007790382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739313,0.00009314172,0.0008180709,0.0007510316,0.00047988506,0.0004647527],"domain_scores_gemma":[0.99817175,0.00009567905,0.00017557912,0.0012279561,0.00018662328,0.000142402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005214629,0.00028547324,0.00033762158,0.0001691227,0.00040893155,0.00014002167,0.002503368,0.000106936306,0.00007027271],"category_scores_gemma":[0.000033638415,0.00029277866,0.00013668682,0.00094070804,0.00010074625,0.00057665183,0.0016412474,0.00019723298,0.000058096117],"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":[0.000017513898,0.00083278614,0.0009451335,0.00008521036,0.000114276954,0.0000051757706,0.0005003161,0.09974301,0.0025804946,0.87201864,0.0005345713,0.022622854],"study_design_scores_gemma":[0.00048889295,0.00016220604,0.0072418493,0.00012771724,0.000063230254,0.000014620993,0.0003885903,0.94661796,0.008902059,0.02680484,0.008665624,0.0005223814],"about_ca_topic_score_codex":0.0003715551,"about_ca_topic_score_gemma":0.000018142764,"teacher_disagreement_score":0.84687495,"about_ca_system_score_codex":0.000611991,"about_ca_system_score_gemma":0.00041349765,"threshold_uncertainty_score":0.99995244},"labels":[],"label_agreement":null},{"id":"W4412621057","doi":"10.1016/bs.host.2025.04.003","title":"Active learning of computer experiment with both quantitative and qualitative inputs","year":2025,"lang":"en","type":"book-chapter","venue":"Handbook of statistics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.026381038918473338,"score_gpt":0.3044409708634153,"score_spread":0.278059931944942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412621057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010990976,0.0008944685,0.9849703,0.00005410875,0.000039458424,0.00027025703,0.0005168663,0.000021200432,0.013222366],"genre_scores_gemma":[0.0006134444,0.0005409182,0.97293484,0.000032734366,0.000019231133,0.000021385245,0.00007155898,0.000016152386,0.025749743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988028,0.000048921476,0.00036905173,0.00036089763,0.00029384374,0.00012448503],"domain_scores_gemma":[0.99805635,0.00075299846,0.00051033386,0.00024169675,0.00038341715,0.000055182423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009399287,0.00022940572,0.0004406043,0.00014632527,0.000098050135,0.000019770925,0.00023101119,0.00008794942,0.000015743128],"category_scores_gemma":[0.000013830705,0.00020880823,0.00003599082,0.000053318807,0.00038094673,0.00009994468,0.00020055534,0.00021890571,0.0000026731889],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002742078,0.00003799249,0.0000017508783,0.00011383204,0.00016052653,0.000006124655,0.008987526,0.00008377732,0.000092004135,0.97847736,0.001219569,0.010792131],"study_design_scores_gemma":[0.006195832,0.012404224,0.0005668284,0.015384179,0.00072506693,0.00006762644,0.003912903,0.25121516,0.015884593,0.6264266,0.06354628,0.0036707267],"about_ca_topic_score_codex":0.000022654714,"about_ca_topic_score_gemma":0.000006020617,"teacher_disagreement_score":0.35205078,"about_ca_system_score_codex":0.00004190432,"about_ca_system_score_gemma":0.00020545533,"threshold_uncertainty_score":0.85149527},"labels":[],"label_agreement":null},{"id":"W4412846920","doi":"10.5376/cgg.2025.16.0015","title":"AI-Assisted Genomic Prediction Models in Cotton Breeding","year":2025,"lang":"en","type":"article","venue":"Cotton Genomics and Genetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Genomic selection; Biology; Computational biology; Computer science; Biotechnology; Genetics; Genotype; Gene; Single-nucleotide polymorphism","score_opus":0.015345241814357199,"score_gpt":0.23835623768540784,"score_spread":0.22301099587105064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412846920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3645884,0.0027426996,0.62889576,0.0018373029,0.00027138187,0.0002575751,0.00001919637,0.000059453134,0.0013282183],"genre_scores_gemma":[0.9510534,0.0019263399,0.04583727,0.0006748435,0.00006930353,0.000040069277,0.000016867276,0.000010201325,0.000371731],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902505,0.000020491176,0.0002897076,0.00037417628,0.00008203868,0.00020852523],"domain_scores_gemma":[0.99945366,0.000031919637,0.00005382895,0.00033572788,0.00006159921,0.00006328977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001688349,0.00011981772,0.00013613424,0.00016571037,0.00016942252,0.000114159324,0.00034740337,0.00008980664,0.000001496339],"category_scores_gemma":[0.0000032550145,0.00013576391,0.000031774147,0.0003400044,0.0000485075,0.00012343681,0.00026744156,0.00013015448,0.0000039383644],"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":[0.000020517136,0.0003549135,0.010026546,0.000087570595,0.0000740859,0.000006595721,0.0021636202,0.08493936,0.12030823,0.53651786,0.004243203,0.24125747],"study_design_scores_gemma":[0.00036726418,0.00003643097,0.050279666,0.00001614684,0.000008680573,0.000007796043,0.000041448857,0.8947236,0.00046282882,0.043331895,0.010597177,0.00012707857],"about_ca_topic_score_codex":0.000025968257,"about_ca_topic_score_gemma":0.000021027614,"teacher_disagreement_score":0.80978423,"about_ca_system_score_codex":0.00011937922,"about_ca_system_score_gemma":0.00012852295,"threshold_uncertainty_score":0.55362916},"labels":[],"label_agreement":null},{"id":"W4413195088","doi":"10.1016/j.chaos.2025.117007","title":"Connection-based framework for assessing natural complexity in nonlinear adaptive systems","year":2025,"lang":"en","type":"article","venue":"Chaos Solitons & Fractals","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundação para a Ciência e a Tecnologia; Fulbright Portugal; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Connection (principal bundle); Nonlinear system; Natural (archaeology); Computer science; Mathematics; Mathematical optimization; Geology; Physics; Geometry","score_opus":0.050451259360212136,"score_gpt":0.3436959667600328,"score_spread":0.29324470739982067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413195088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013393949,0.0005546842,0.980191,0.0032917028,0.0010926946,0.0007058551,0.000042102143,0.0001700932,0.00055790244],"genre_scores_gemma":[0.7388645,0.000002704353,0.26018637,0.00034378693,0.00017621518,0.00030387804,0.00002861982,0.000007479787,0.000086436055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998616,0.000075294745,0.00035261048,0.0004582438,0.00015824026,0.0003396393],"domain_scores_gemma":[0.9979516,0.0011489914,0.0001378917,0.0004892739,0.0002082849,0.00006393807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029396766,0.00016584554,0.00026480362,0.00019938889,0.0003895294,0.00022455906,0.0005166067,0.00013253497,0.000003946455],"category_scores_gemma":[0.00017206567,0.00016787183,0.00010263101,0.00075939076,0.00010578548,0.00041919562,0.00009347614,0.0003113289,0.000010750692],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011869993,0.00030075887,0.00076048455,0.00005546215,0.000028256065,0.0000028481716,0.00012851626,0.0032921962,0.00028313228,0.99133795,0.0006484499,0.0031500643],"study_design_scores_gemma":[0.00035518725,0.000024221921,0.00602371,0.00020353502,0.0000071261675,0.000002721181,0.00023445884,0.9316191,0.00041884012,0.057537764,0.0033871408,0.00018620021],"about_ca_topic_score_codex":0.00017380346,"about_ca_topic_score_gemma":0.00002562299,"teacher_disagreement_score":0.9338002,"about_ca_system_score_codex":0.00018801089,"about_ca_system_score_gemma":0.00031835138,"threshold_uncertainty_score":0.68456143},"labels":[],"label_agreement":null},{"id":"W4413216311","doi":"10.1145/3712255.3716528","title":"Linear Genetic Programming","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Evolutionary Algorithms and Applications","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":"Queen's University","funders":"","keywords":"Genetic programming; Computer science; Linear programming; Artificial intelligence; Algorithm","score_opus":0.015980443105565623,"score_gpt":0.25074904061007236,"score_spread":0.23476859750450674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413216311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23651253,0.0007200045,0.7574623,0.0032326013,0.00024077325,0.00045821883,0.0000026694674,0.00011303088,0.0012578346],"genre_scores_gemma":[0.7624449,0.000053677453,0.23719423,0.000071092356,0.000032901316,0.000034709494,0.0000017445002,0.0000036979745,0.00016306323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988632,0.000015834672,0.000346637,0.0003545054,0.00023179161,0.00018802406],"domain_scores_gemma":[0.9990378,0.000050484392,0.0001849725,0.00013934387,0.00053366023,0.000053740838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010349635,0.00014120349,0.00015607708,0.000121047306,0.00038370548,0.00007724718,0.0006193303,0.000058811365,0.0000031223897],"category_scores_gemma":[0.000025271636,0.00011910447,0.000059058642,0.0006678341,0.00017835361,0.00021327348,0.00042948677,0.00011674182,0.000003841694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001633202,0.00038395618,0.06875582,0.00028911157,0.00008207066,3.995646e-7,0.00077347044,0.008512216,0.0041084955,0.68694884,0.004871963,0.2252573],"study_design_scores_gemma":[0.00021576772,0.00003355732,0.4216758,0.00008201907,0.000015233517,0.000019428797,0.000080676866,0.49501705,0.0001840469,0.081195645,0.0013677921,0.00011299379],"about_ca_topic_score_codex":0.000018006196,"about_ca_topic_score_gemma":6.106117e-7,"teacher_disagreement_score":0.60575324,"about_ca_system_score_codex":0.00003901256,"about_ca_system_score_gemma":0.00013622947,"threshold_uncertainty_score":0.48569393},"labels":[],"label_agreement":null},{"id":"W4414153585","doi":"10.1145/3767167","title":"The Havoc Paradox in Generator-Based Fuzzing—RCR Report","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fuzz testing; Artifact (error); Scripting language; Exploit; Affect (linguistics)","score_opus":0.04416990580043428,"score_gpt":0.3086626128790792,"score_spread":0.2644927070786449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414153585","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040703886,0.0005121166,0.9920315,0.0025751216,0.00048592302,0.00010837883,0.0000022556792,0.00020127375,0.0000130291155],"genre_scores_gemma":[0.04340915,0.000093534836,0.95578825,0.00021560218,0.000021478714,0.00017226252,0.0000018762266,0.000006977837,0.00029085364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990843,0.00011195709,0.00022383372,0.00030488113,0.00007113532,0.00020391385],"domain_scores_gemma":[0.9970869,0.002153463,0.0000297123,0.0006497352,0.000035888435,0.00004430578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073730253,0.00011087331,0.00013936496,0.00016094465,0.00026006522,0.00003689207,0.00037584174,0.00008422513,0.000001511616],"category_scores_gemma":[0.00043642538,0.0000937772,0.000047728576,0.0004789313,0.000043108677,0.000067830224,0.0000145343065,0.0002471501,0.0000018585621],"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":[0.000026645814,0.00021718687,0.0012207205,0.000060205264,0.00010761761,0.0000797916,0.00028906,0.35347426,0.0017404151,0.0832514,0.00041602858,0.55911666],"study_design_scores_gemma":[0.0024526995,0.00029342293,0.058016468,0.00021192548,0.00009652975,0.00053609477,0.00014318242,0.6139586,0.016849462,0.039128106,0.26707453,0.001238972],"about_ca_topic_score_codex":0.000026770313,"about_ca_topic_score_gemma":0.000013155032,"teacher_disagreement_score":0.5578777,"about_ca_system_score_codex":0.00003599487,"about_ca_system_score_gemma":0.000083338986,"threshold_uncertainty_score":0.3824123},"labels":[],"label_agreement":null},{"id":"W4414158670","doi":"10.1007/978-3-031-94940-1_22","title":"Genetic Algorithm Visualization","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Knapsack problem; Attractor; Crossover; Visualization; Set (abstract data type); Genetic algorithm; Mutation","score_opus":0.02142362223658602,"score_gpt":0.29258255861860377,"score_spread":0.27115893638201777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414158670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013628269,0.00046768706,0.83315575,0.00078118447,0.00018516768,0.00030402697,0.000014442392,0.00010427427,0.16498612],"genre_scores_gemma":[0.00079936476,0.0051215696,0.98672974,0.0012158504,0.00005317916,0.00007346983,0.000085829204,0.000007489015,0.005913521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839,0.000022631793,0.0006517952,0.00033258155,0.00039419183,0.00020878727],"domain_scores_gemma":[0.9969371,0.00014110841,0.00025740478,0.0020971538,0.00047694388,0.00009031716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000496706,0.000205169,0.00019955657,0.0011196731,0.0006148792,0.0005228225,0.003198403,0.00012810229,0.00000785912],"category_scores_gemma":[0.00002132061,0.00022160365,0.000042655494,0.0007737025,0.0006031079,0.004567386,0.0024416626,0.00028528585,0.0000627244],"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":[1.2610981e-7,0.000009285619,0.0000056025815,0.0000075240564,0.000002054431,1.0897174e-7,0.0001669997,0.00008264974,2.8692398e-7,0.6085924,0.0004434788,0.39068946],"study_design_scores_gemma":[0.0001250689,0.000017759323,0.0010167294,0.000091150665,0.0000037365862,0.000012489297,0.0000042638994,0.7335948,0.0000018950136,0.025130887,0.23980443,0.0001968322],"about_ca_topic_score_codex":0.000011477952,"about_ca_topic_score_gemma":0.0000024977637,"teacher_disagreement_score":0.7335121,"about_ca_system_score_codex":0.00016222065,"about_ca_system_score_gemma":0.00047846243,"threshold_uncertainty_score":0.90367347},"labels":[],"label_agreement":null},{"id":"W4414359075","doi":"10.1108/jpbm-10-2024-5527","title":"How to identify line extensions that survive","year":2025,"lang":"en","type":"article","venue":"Journal of Product & Brand Management","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Penetration rate; Penetration (warfare); Market penetration; Product (mathematics); Product line","score_opus":0.022085224514505595,"score_gpt":0.2976231254936046,"score_spread":0.27553790097909897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004026544,0.00071462523,0.89316756,0.099322736,0.000963183,0.0003107394,0.0000012502966,0.000025097277,0.001468249],"genre_scores_gemma":[0.75063246,0.00059177,0.2207973,0.0017894743,0.00044609685,0.00003087539,0.0000016242844,0.000008596491,0.025701797],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999055,0.00003580458,0.00022715292,0.00023707654,0.00028967534,0.00015523541],"domain_scores_gemma":[0.99906516,0.000028766777,0.00013935195,0.00045364877,0.0002344568,0.00007859198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005278748,0.000091090675,0.00015223178,0.0002817192,0.0001536958,0.00020384618,0.0006786937,0.000014231224,0.00000514414],"category_scores_gemma":[0.00003379187,0.00007327708,0.000084546926,0.00060675014,0.00001749869,0.0004326401,0.000338338,0.00010731347,0.000010877432],"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":[0.000040247964,0.0007499441,0.00029381944,0.00013133672,0.00043698892,0.00012688199,0.00047960444,0.001975559,0.0038570247,0.30932564,0.30495298,0.37762997],"study_design_scores_gemma":[0.0013145698,0.00016828,0.114569075,0.00026346394,0.00013960792,0.00007209181,0.00041189103,0.001577829,0.0041182362,0.041568495,0.83547187,0.00032459252],"about_ca_topic_score_codex":0.000003103867,"about_ca_topic_score_gemma":0.0000032990881,"teacher_disagreement_score":0.74660593,"about_ca_system_score_codex":0.000040730283,"about_ca_system_score_gemma":0.00003496876,"threshold_uncertainty_score":0.29881525},"labels":[],"label_agreement":null},{"id":"W4414437473","doi":"10.1007/978-3-032-05134-9_14","title":"Arithmetic Circuit Compilation Using Symbolic Probabilistic Inference and Indicator-Determined Buckets","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Regina","funders":"","keywords":"Benchmark (surveying); Inference; Probabilistic logic; Bayesian network; Key (lock); Reduction (mathematics); Construct (python library); Variable elimination","score_opus":0.026544622731242473,"score_gpt":0.269126548051733,"score_spread":0.24258192532049055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414437473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006387243,0.0004076648,0.99626076,0.0003729841,0.00044982362,0.000606891,0.000016661425,0.00013997627,0.0011064892],"genre_scores_gemma":[0.5520597,0.00005861387,0.44669625,0.0006393824,0.00026218963,0.000035071695,0.000017911945,0.00002520409,0.00020566788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684453,0.000037209422,0.0005420665,0.0014568106,0.00063373684,0.00048562736],"domain_scores_gemma":[0.9975861,0.00064313004,0.00029945164,0.0010871228,0.00021434245,0.00016990972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004904049,0.00044359395,0.0004668098,0.0009160428,0.0004622033,0.00047543403,0.0018086528,0.00027719204,0.000008014216],"category_scores_gemma":[0.00011709139,0.00044445836,0.00006968567,0.00079357886,0.0008217171,0.0005049195,0.0011720258,0.000599796,0.000010769161],"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":[0.0000024346057,0.00007548485,0.00057881436,0.00015291682,0.000018117893,0.000029393954,0.00067389157,0.019955518,0.00027421172,0.24613127,0.000007175709,0.7321008],"study_design_scores_gemma":[0.00014621315,0.000046812467,0.002314928,0.0003146368,0.000013295552,0.000052988904,8.751889e-8,0.7160468,0.00006493678,0.2804479,0.00016608511,0.00038532223],"about_ca_topic_score_codex":0.000023534536,"about_ca_topic_score_gemma":0.000018149614,"teacher_disagreement_score":0.73171544,"about_ca_system_score_codex":0.00031858214,"about_ca_system_score_gemma":0.0009140033,"threshold_uncertainty_score":0.99980074},"labels":[],"label_agreement":null},{"id":"W4414558358","doi":"","title":"Theory of arrays, high-performance code generation. Denotational semantics and mathematical problems","year":2025,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Denotational semantics; Semantics (computer science); Algebra over a field; Denotational semantics of the Actor model; Domain theory; Code (set theory); Calculus (dental)","score_opus":0.011269956160855089,"score_gpt":0.22421618346018934,"score_spread":0.21294622729933427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414558358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07649354,0.00050934573,0.91805464,0.004088574,0.000044197037,0.0003643066,0.000017364779,0.00017582807,0.0002521778],"genre_scores_gemma":[0.60306823,0.00012627772,0.395656,0.00038053672,0.000031384,0.00021474024,0.000011029652,0.000007619509,0.00050418545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878234,0.00006160118,0.0003869008,0.00030323217,0.00022245693,0.00024344337],"domain_scores_gemma":[0.99895084,0.00015463814,0.00013316714,0.00053064345,0.00015079454,0.000079919795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004973713,0.00015218994,0.00019609128,0.00020760855,0.00024522105,0.00009039621,0.00046897912,0.00010519445,0.00000809972],"category_scores_gemma":[0.00005766218,0.00014599982,0.00004122841,0.0004945257,0.00012359153,0.00034975706,0.00020411257,0.00015405324,0.0000047057006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003371352,0.00013862574,0.0017022251,0.000056319943,0.000023381666,8.5930293e-7,0.00011927139,0.0071311956,0.0062443726,0.9754984,0.0005393181,0.008542698],"study_design_scores_gemma":[0.00020807706,0.00004013421,0.018420678,0.00005256053,0.00001518831,0.000024983257,0.000015673479,0.81169295,0.010860633,0.15821938,0.00030655408,0.00014318248],"about_ca_topic_score_codex":0.00007751137,"about_ca_topic_score_gemma":0.00004158752,"teacher_disagreement_score":0.817279,"about_ca_system_score_codex":0.000076057266,"about_ca_system_score_gemma":0.00018885564,"threshold_uncertainty_score":0.59536994},"labels":[],"label_agreement":null},{"id":"W4414786717","doi":"10.1177/02783649251364000","title":"FORESEER: Recognize and utilize uncertainties by integrating data-based learning and symbolic feedback","year":2025,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities; Beijing Nova Program; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Adaptability; Robot; Parametric statistics; Feed forward; Feature (linguistics); Online model; Control (management)","score_opus":0.07047487487588197,"score_gpt":0.39788590281290953,"score_spread":0.3274110279370276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414786717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02238353,0.0043741805,0.88928086,0.08201616,0.00036914504,0.00020140744,0.000016240496,0.00002524617,0.001333205],"genre_scores_gemma":[0.8996715,0.001645807,0.09669374,0.00028012885,0.00017083091,0.0000070537853,0.000015345195,0.000009168191,0.0015064224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984931,0.00016777779,0.0002880573,0.00018526323,0.0006916545,0.00017417288],"domain_scores_gemma":[0.9973724,0.0012194047,0.00011958999,0.00023625282,0.0009918909,0.000060469134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023117233,0.00007492415,0.00011383811,0.00023246961,0.00032441257,0.0004493695,0.0018874798,0.00003505282,0.0000062003255],"category_scores_gemma":[0.00073308626,0.00005190088,0.000025539604,0.000330596,0.00023478943,0.0003326831,0.00097634946,0.0006324749,0.0000021469518],"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":[0.00016939195,0.00036321505,0.01322855,0.000069174,0.0006090648,0.000038343038,0.0011537429,0.017504517,0.005087075,0.49537748,0.07785703,0.3885424],"study_design_scores_gemma":[0.0005793887,0.00010264055,0.0015406209,0.00019140406,0.000011604194,0.00007363775,0.0006863717,0.93202025,0.00038095308,0.029538166,0.034781843,0.00009309919],"about_ca_topic_score_codex":0.000073706375,"about_ca_topic_score_gemma":0.000013404602,"teacher_disagreement_score":0.91451573,"about_ca_system_score_codex":0.0000845972,"about_ca_system_score_gemma":0.00026437175,"threshold_uncertainty_score":0.43332803},"labels":[],"label_agreement":null},{"id":"W4415018261","doi":"10.1016/b978-0-443-33633-1.00017-4","title":"Applications of generative AI for optimizing research in the agricultural business sector","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Campbell Scientific (Canada); Semtech (Canada)","funders":"","keywords":"Food security; Agribusiness; Supply chain; Agriculture; Resilience (materials science); Agricultural productivity; Carbon footprint; Production (economics); Big data","score_opus":0.04158669488209784,"score_gpt":0.31305694918031973,"score_spread":0.2714702542982219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415018261","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015123633,0.0007921985,0.13740407,0.003387886,0.00007731241,0.0024756088,0.00007945535,0.000029853461,0.8557521],"genre_scores_gemma":[0.0004389059,0.000091684364,0.09422263,0.00037811254,0.0004597379,0.0037794807,0.000086236614,0.000018023244,0.9005252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998559,0.000056101908,0.00036442102,0.00044977397,0.00034151057,0.00022920383],"domain_scores_gemma":[0.9979241,0.0004041169,0.0001331087,0.000700238,0.00080660015,0.000031831107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055603124,0.00018482724,0.00025231575,0.00023000466,0.0003220872,0.00008669632,0.0012956382,0.00014813463,0.0000043340146],"category_scores_gemma":[0.000010207526,0.00012770502,0.00011487421,0.00022527234,0.0001479847,0.000082505074,0.00025241263,0.00041591245,0.000007273054],"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":[0.0000012088644,0.000019012527,2.360508e-7,0.00006034588,0.000017048033,4.79835e-7,0.0003049208,0.000037914513,0.000039979433,0.5469424,0.0010719699,0.45150447],"study_design_scores_gemma":[0.00016193982,0.00002587486,0.000057606267,0.00019915642,0.000015908383,0.00000576309,0.0000526404,0.0014330135,0.00005688559,0.08743616,0.9103651,0.00018994891],"about_ca_topic_score_codex":0.000002080936,"about_ca_topic_score_gemma":0.00002108328,"teacher_disagreement_score":0.9092931,"about_ca_system_score_codex":0.00008480054,"about_ca_system_score_gemma":0.00029556747,"threshold_uncertainty_score":0.52076596},"labels":[],"label_agreement":null},{"id":"W4415340732","doi":"10.1016/j.tcs.2025.115597","title":"Degrees are useless in Snort when measuring temperature","year":2025,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland; Mount Saint Vincent University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vertex (graph theory); Degree (music); Graph; Measure (data warehouse); Simple (philosophy); Position (finance)","score_opus":0.013955894777330653,"score_gpt":0.2413300981591317,"score_spread":0.22737420338180106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415340732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06428794,0.00009285349,0.92825836,0.0038102055,0.00031310084,0.00017117528,0.0000011858724,0.0001603655,0.002904825],"genre_scores_gemma":[0.837678,0.000004123971,0.16175114,0.00045709035,0.000050567396,0.000019312633,3.9295074e-7,0.0000030132994,0.0000363274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814653,0.00006479564,0.0002405043,0.0006821978,0.00043309477,0.00043285787],"domain_scores_gemma":[0.9987779,0.0001406681,0.00004180397,0.00074429816,0.00017221564,0.00012308246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008659633,0.00014465574,0.00017340368,0.00028147374,0.00041218134,0.00035783573,0.002442935,0.000057609956,0.000009737392],"category_scores_gemma":[0.000046089935,0.0001238466,0.000042940945,0.0018889148,0.0010288564,0.00058334117,0.0009599795,0.00025836832,0.000023821445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014408706,0.00006681928,0.0018593483,0.0000057755224,0.0000018611966,0.0000064881974,0.00015510146,0.0005368267,0.0003851634,0.988828,0.00011260791,0.00804059],"study_design_scores_gemma":[0.00022278307,0.00002643664,0.064057805,0.00011871591,0.0000024411124,0.000014418844,0.000015224259,0.64809805,0.0014471837,0.285479,0.00029567716,0.00022228363],"about_ca_topic_score_codex":0.000008107928,"about_ca_topic_score_gemma":0.000006846146,"teacher_disagreement_score":0.7733901,"about_ca_system_score_codex":0.00009740773,"about_ca_system_score_gemma":0.0001963978,"threshold_uncertainty_score":0.50503176},"labels":[],"label_agreement":null},{"id":"W4415367403","doi":"10.1109/isit63088.2025.11195651","title":"Communication with Perfect Feedback for Bit Flips and Erasures","year":2025,"lang":"","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bit (key); Encoding (memory); Sequence (biology); Decoding methods; Noise (video)","score_opus":0.012786026886733212,"score_gpt":0.25933863284913855,"score_spread":0.24655260596240533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415367403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042608804,0.005790501,0.95599914,0.02165468,0.00008238856,0.0009587021,0.000013880775,0.00008750595,0.011152314],"genre_scores_gemma":[0.5871025,0.0013050163,0.39639762,0.0008091094,0.000050566607,0.0003560629,0.000016118525,0.000011134717,0.013951892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988009,0.00006567069,0.00026026162,0.0004866942,0.00013521907,0.00025128966],"domain_scores_gemma":[0.9982647,0.00037407933,0.000083210114,0.0009152622,0.00028461363,0.00007816031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033871268,0.00018340562,0.00018814432,0.00010686348,0.0009727129,0.00032152515,0.00065389875,0.000082410406,0.000026347912],"category_scores_gemma":[0.000017664572,0.00015396139,0.00005129208,0.00062232453,0.00024853204,0.00039611303,0.00031165418,0.00014263974,0.00001180186],"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":[0.00003184545,0.00026421912,0.0011354632,0.00009236673,0.00008682534,1.8529839e-7,0.00038333083,0.00013992793,0.0001419673,0.78068113,0.011634923,0.20540778],"study_design_scores_gemma":[0.0016213005,0.00036468782,0.0360202,0.0002352003,0.000094145165,0.000017123886,0.00026295372,0.8310089,0.00059587276,0.028250277,0.101103015,0.00042631096],"about_ca_topic_score_codex":0.00010396932,"about_ca_topic_score_gemma":0.00010133215,"teacher_disagreement_score":0.83086896,"about_ca_system_score_codex":0.000042441156,"about_ca_system_score_gemma":0.0002196918,"threshold_uncertainty_score":0.74814194},"labels":[],"label_agreement":null},{"id":"W4415528645","doi":"10.1002/9781394296262.ch3","title":"Animal‐Behavior‐Inspired Algorithms in Analog Circuit Sizing Optimization","year":2025,"lang":"en","type":"other","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Firefly algorithm; Cuckoo search; Metaheuristic; Particle swarm optimization; Population; Ant colony optimization algorithms; Sizing; Extremal optimization","score_opus":0.018685776941413157,"score_gpt":0.260050342670579,"score_spread":0.24136456572916587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415528645","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.965289e-7,0.00035581304,0.5870254,0.00019625065,0.0001397623,0.00029362112,0.000015622203,0.0003553162,0.41161773],"genre_scores_gemma":[0.00042381184,0.0003089403,0.4574407,0.00025996848,0.00020307236,0.0002857491,0.00010866126,0.00011202163,0.5408571],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985173,0.000036985544,0.00029920883,0.000653514,0.00022208126,0.00027093737],"domain_scores_gemma":[0.99906075,0.000031616553,0.00013289336,0.0006584386,0.00005140581,0.000064881184],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010982643,0.00023677351,0.00025701695,0.00056269567,0.0000732639,0.00009840916,0.0008587784,0.0002572544,0.0006407258],"category_scores_gemma":[0.000010750747,0.00024855323,0.00007302039,0.0010626463,0.000034743938,0.00016673171,0.00016518794,0.00019301781,0.000075014046],"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":[0.0000020200412,0.001057304,0.0010807009,0.00009921481,0.00008256267,0.00009505667,0.00014675407,0.008255594,0.000101404854,0.57971364,0.2468507,0.16251506],"study_design_scores_gemma":[0.0007660556,0.00007240647,0.00405847,0.00030899068,0.000045109962,0.000015714018,0.000031845135,0.765499,0.000034418917,0.0021228038,0.22593312,0.0011120845],"about_ca_topic_score_codex":0.00072096154,"about_ca_topic_score_gemma":0.00011448118,"teacher_disagreement_score":0.7572434,"about_ca_system_score_codex":0.00010504067,"about_ca_system_score_gemma":0.00019865684,"threshold_uncertainty_score":0.99999666},"labels":[],"label_agreement":null},{"id":"W4416530745","doi":"10.1007/s00170-025-17034-0","title":"Correction: Reinforcement learning for self-adaptive genetic algorithm in assembly sequence planning","year":2025,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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 Manitoba","funders":"","keywords":"Sequence (biology); Reinforcement learning; Genetic algorithm; Reinforcement; Genetic programming; Production planning","score_opus":0.011899152124682493,"score_gpt":0.2846941335728143,"score_spread":0.2727949814481318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416530745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027547674,0.0002243081,0.9655914,0.0049570086,0.0012132337,0.0001658239,4.1759074e-7,0.0000865653,0.00021358683],"genre_scores_gemma":[0.59527117,0.00012179476,0.40403804,0.00015181734,0.0000828762,0.000049456954,8.876871e-7,0.000005178912,0.0002787948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903893,0.000018689187,0.00038894126,0.00017244155,0.00020995042,0.00017106185],"domain_scores_gemma":[0.9989993,0.00023482212,0.00032463437,0.0001777498,0.00024447907,0.000019024441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024195215,0.000102309255,0.0001362852,0.00048115812,0.00014067547,0.000045157165,0.0013920759,0.00006188647,0.0000014663241],"category_scores_gemma":[0.00007064292,0.00008449736,0.000059096394,0.00023640692,0.000043326367,0.00027376376,0.00025127397,0.00039522254,0.000001456473],"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":[0.00003323094,0.00005291294,0.00008956515,0.00000351292,0.0001033771,0.00003189048,0.00018174437,0.6455813,0.0014176607,0.018059134,0.00028615375,0.33415952],"study_design_scores_gemma":[0.0012665755,0.00032173196,0.0032445213,0.00026827815,0.000016984004,0.00045739644,0.0005032182,0.7835444,0.09679484,0.09395659,0.019426696,0.00019872654],"about_ca_topic_score_codex":0.0000059040167,"about_ca_topic_score_gemma":0.0000016729413,"teacher_disagreement_score":0.56772345,"about_ca_system_score_codex":0.00031040717,"about_ca_system_score_gemma":0.0001241162,"threshold_uncertainty_score":0.34457025},"labels":[],"label_agreement":null},{"id":"W4416727775","doi":"10.1109/mwscas53549.2025.11244398","title":"SymXplorer: A Designer's Toolbox for Automated Analog Circuit Topology Exploration","year":2025,"lang":"","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Network topology; Python (programming language); Transimpedance amplifier; Toolbox; Bayesian optimization; Circuit design; Analogue electronics; Electronic circuit; Topology (electrical circuits); Amplifier","score_opus":0.045182734352124,"score_gpt":0.30363177053484974,"score_spread":0.25844903618272574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416727775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024948432,0.00056585134,0.96684456,0.018679347,0.0011582103,0.0014757726,0.00003193348,0.0010350341,0.009959815],"genre_scores_gemma":[0.738938,0.00025452778,0.23773238,0.0034016238,0.00039665488,0.002428974,0.00014448611,0.00003326004,0.016670125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716955,0.00014097866,0.0007830586,0.001038512,0.00021961164,0.00064830633],"domain_scores_gemma":[0.9976954,0.00043422918,0.00019138545,0.0009843353,0.0005488586,0.00014579932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005089823,0.00033362868,0.00039341007,0.00038661974,0.0009445047,0.0003401342,0.0010948732,0.00028228865,0.00012535848],"category_scores_gemma":[0.00010913924,0.0003462892,0.00021601153,0.0017609231,0.00021702904,0.0012040356,0.00021000646,0.00016925391,0.00013717526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011316279,0.00030691398,0.000050086375,0.000045774384,0.00009075949,0.000002000331,0.0003747791,0.0018690214,0.00058873504,0.90814257,0.03796639,0.050551634],"study_design_scores_gemma":[0.0006803267,0.00022622813,0.0008601699,0.000040208502,0.00006362165,0.000007449703,0.00019423805,0.8378057,0.0006777767,0.13679597,0.022329785,0.00031851442],"about_ca_topic_score_codex":0.00008758558,"about_ca_topic_score_gemma":0.00003823912,"teacher_disagreement_score":0.83593667,"about_ca_system_score_codex":0.00019709182,"about_ca_system_score_gemma":0.0007433527,"threshold_uncertainty_score":0.9998989},"labels":[],"label_agreement":null},{"id":"W4417124673","doi":"10.1162/isal.a.858","title":"Genetic Encoding and Shared Knowledge in Reinforcement Learning with Structured Memory","year":2025,"lang":"","type":"article","venue":"ALIFE","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Encoding (memory); Genetic programming; Reinforcement; Constant (computer programming); Control (management)","score_opus":0.011801822491799889,"score_gpt":0.2528365909080778,"score_spread":0.2410347684162779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417124673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25542507,0.011665365,0.7070535,0.0026961064,0.0005688108,0.0013076318,0.0000055820738,0.00014105487,0.021136925],"genre_scores_gemma":[0.981603,0.00026974984,0.014715812,0.00018060057,0.00007298535,0.00006884246,0.000004760657,0.000008474994,0.0030757517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984314,0.00008578105,0.00038727256,0.0005743354,0.0001729481,0.00034825626],"domain_scores_gemma":[0.99920934,0.00010379904,0.000109600456,0.00037943007,0.000092981056,0.000104818486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019885875,0.00020824649,0.00021932201,0.00021952495,0.00039215072,0.00017816985,0.00040101237,0.00008244018,0.000054325177],"category_scores_gemma":[0.00002334314,0.0002024549,0.000025826504,0.0009342845,0.000117463365,0.0002566543,0.00040864656,0.00028994653,0.000014445211],"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":[0.000109681336,0.0004591974,0.06325524,0.0008285777,0.0003977518,0.00009131465,0.024502927,0.36259255,0.0037119866,0.18724209,0.0035184657,0.35329023],"study_design_scores_gemma":[0.0011065251,0.00015365292,0.13304007,0.00037595304,0.00003167776,0.000017119466,0.0004651843,0.8570592,0.00025248286,0.0009642278,0.0062047094,0.000329203],"about_ca_topic_score_codex":0.00010859534,"about_ca_topic_score_gemma":0.000096968484,"teacher_disagreement_score":0.72617793,"about_ca_system_score_codex":0.000105929044,"about_ca_system_score_gemma":0.00033123905,"threshold_uncertainty_score":0.8255871},"labels":[],"label_agreement":null},{"id":"W4417133656","doi":"10.1162/isal.a.897","title":"Integrating Neuroplasticity into Genetic Programming Agents for Adaptive Decision Making","year":2025,"lang":"","type":"article","venue":"ALIFE","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Core (optical fiber); Memetics; Path (computing); Genetic programming; Adaptation (eye); Reinforcement; Order (exchange); Ant colony","score_opus":0.02546671363974189,"score_gpt":0.3179001603880881,"score_spread":0.2924334467483462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417133656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01237415,0.00080366136,0.9833854,0.0007009424,0.0011661694,0.0011844735,0.0000128990305,0.00009876744,0.00027357778],"genre_scores_gemma":[0.47905296,0.00002635227,0.5198884,0.00046987174,0.00015351626,0.00023022274,0.0000025790794,0.000012270765,0.00016384225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973794,0.00010005466,0.0007059048,0.00094488193,0.00033713563,0.0005326048],"domain_scores_gemma":[0.9975575,0.0010813632,0.00023917944,0.0006016095,0.00039747375,0.00012288043],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033050534,0.00030865433,0.00028555765,0.00019676267,0.0011791338,0.00040008238,0.0009945795,0.00012255287,0.0000136665285],"category_scores_gemma":[0.0005151623,0.00031724683,0.00018620775,0.0011420514,0.00013229836,0.00031601012,0.00071439095,0.0002611579,0.000032860575],"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":[0.000028320264,0.0001965093,0.0006476119,0.0000532731,0.00005230327,0.0000055699898,0.0006442634,0.002806419,0.000056886864,0.079077944,0.0011614122,0.9152695],"study_design_scores_gemma":[0.0004058877,0.00021490664,0.0065318523,0.00062259723,0.00006523482,0.0000071809604,0.00021523888,0.94628805,0.000056708013,0.021697005,0.023611972,0.00028334776],"about_ca_topic_score_codex":0.000058218353,"about_ca_topic_score_gemma":0.000048877275,"teacher_disagreement_score":0.9434816,"about_ca_system_score_codex":0.000160915,"about_ca_system_score_gemma":0.00035119324,"threshold_uncertainty_score":0.99992794},"labels":[],"label_agreement":null},{"id":"W4417470439","doi":"10.2139/ssrn.5744303","title":"&lt;div&gt; Evolutionary Market Intelligence Framework for Self-Adaptive Quantitative System&lt;/div&gt;","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Key (lock); Interpretability; Expert system; Order (exchange)","score_opus":0.017813132719415495,"score_gpt":0.28435438349982534,"score_spread":0.26654125078040986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417470439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017081511,0.04634408,0.93765414,0.003742894,0.004093838,0.0034546573,0.0005156662,0.00042949367,0.0035944004],"genre_scores_gemma":[0.1396525,0.047194246,0.7997933,0.0002633783,0.0029236288,0.0018185298,0.00010215134,0.000163032,0.008089222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98325115,0.0013263944,0.0030909497,0.0032031732,0.0019172208,0.0072111],"domain_scores_gemma":[0.98751384,0.003162719,0.0028732948,0.002312527,0.0034468712,0.00069075683],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.007632743,0.001789222,0.0018373677,0.0011152843,0.0041604177,0.0008150336,0.0065450654,0.0015210434,0.00009811389],"category_scores_gemma":[0.0006178133,0.0019045194,0.0017246823,0.0026378322,0.00061425864,0.001311279,0.0026090129,0.011049012,0.00022442256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00035253115,0.00085049676,0.000034080218,0.00038903454,0.002216232,0.000014841749,0.0011654105,0.004454746,0.000014335873,0.9710888,0.0019833564,0.017436098],"study_design_scores_gemma":[0.0004425667,0.0011542893,0.00027204098,0.001724898,0.0004369418,0.00080620387,0.0027804333,0.38635474,0.00001994638,0.59472805,0.010071261,0.0012086417],"about_ca_topic_score_codex":0.00009433218,"about_ca_topic_score_gemma":0.000159883,"teacher_disagreement_score":0.38189998,"about_ca_system_score_codex":0.013734432,"about_ca_system_score_gemma":0.03224445,"threshold_uncertainty_score":0.9997752},"labels":[],"label_agreement":null},{"id":"W54112846","doi":"10.1007/978-1-4615-1681-1_3","title":"Software Techniques for Efficient Polymorphic Calls","year":2001,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Section (typography); Software; Operating system","score_opus":0.019462620312918136,"score_gpt":0.2457890954890806,"score_spread":0.22632647517616244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W54112846","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.1142223e-7,0.00024093784,0.7245279,0.0008843848,0.00007828305,0.00049419934,0.00003590269,0.0005502634,0.2731879],"genre_scores_gemma":[0.00003493769,0.00006162998,0.4311752,0.0003737186,0.00022011387,0.00016471696,0.000036900732,0.000025450843,0.56790733],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987605,0.0000031977545,0.00025531027,0.000531576,0.00022061722,0.00022880115],"domain_scores_gemma":[0.9988261,0.00009650536,0.00011445019,0.00071380235,0.00015653997,0.00009261537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010475955,0.00023545399,0.00020662464,0.00012926527,0.00018667085,0.00006399574,0.0008092273,0.0002241291,0.00014595893],"category_scores_gemma":[0.0000070970227,0.00021316306,0.00017647306,0.00005244654,0.000055601446,0.00006298457,0.00021863729,0.00014906256,0.00013212502],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.227036e-7,0.000023281296,4.1078178e-7,0.000008312978,0.000010336852,0.0000024779645,0.000006905168,0.000014595443,0.000007283835,0.9317977,0.0143534895,0.053774573],"study_design_scores_gemma":[0.0000689185,0.000059072463,0.0000059250715,0.000045679164,0.000011193646,0.00003292672,5.6207875e-7,0.007768219,0.000101720725,0.12788863,0.8636897,0.0003274377],"about_ca_topic_score_codex":0.000012751023,"about_ca_topic_score_gemma":0.0000033312938,"teacher_disagreement_score":0.8493362,"about_ca_system_score_codex":0.0000748834,"about_ca_system_score_gemma":0.00010932724,"threshold_uncertainty_score":0.8692537},"labels":[],"label_agreement":null},{"id":"W58691558","doi":"","title":"The evaluation of a stochastic regular motif language for protein sequences","year":2001,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Regular expression; Hidden Markov model; DNA sequencing; Motif (music); Sequence motif; Natural language processing; Probabilistic logic; Markov chain; Artificial intelligence; DNA; Theoretical computer science; Computational biology; Genetics; Biology; Programming language; Machine learning","score_opus":0.034976796700390514,"score_gpt":0.31268179699816545,"score_spread":0.27770500029777495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W58691558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034012724,0.00022539105,0.9622428,0.002170836,0.00002433403,0.0006865895,0.0000015053809,0.00003195094,0.0006038721],"genre_scores_gemma":[0.94102454,0.0000015032148,0.05733199,0.000017582483,0.000034793462,0.00044732215,0.0000021075248,0.000002034563,0.001138105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994207,0.000030280586,0.00010539635,0.00011601146,0.00023995897,0.00008764339],"domain_scores_gemma":[0.99941474,0.000059494963,0.000050509545,0.00026760699,0.00019033352,0.00001729222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067478995,0.00003643206,0.000038203594,0.000017179567,0.0001532051,0.00002639284,0.00031514812,0.0000142453255,0.0000081527105],"category_scores_gemma":[0.00006463997,0.00002324868,0.000026282873,0.0001760082,0.000037466958,0.00011541116,0.00003088764,0.000019025509,0.0000042463253],"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":[0.000003282276,0.000060800387,0.000006732011,0.000004997515,0.000012619393,1.7776405e-7,0.00041745466,0.004298545,0.010876274,0.78343993,0.00037772223,0.20050144],"study_design_scores_gemma":[0.00015441871,0.000035990906,0.00034419133,0.0000063778243,0.0000058666797,0.000003954471,0.00015284804,0.9172795,0.0013934709,0.080138326,0.00044361647,0.000041472365],"about_ca_topic_score_codex":0.000030582247,"about_ca_topic_score_gemma":0.000018888986,"teacher_disagreement_score":0.9129809,"about_ca_system_score_codex":0.0000185919,"about_ca_system_score_gemma":0.000073098694,"threshold_uncertainty_score":0.117834516},"labels":[],"label_agreement":null},{"id":"W586938665","doi":"","title":"Reflections on adaptive behavior : essays in honor of J.E.R. Staddon","year":2008,"lang":"de","type":"book","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Behaviorism; Honor; Psychology; Psychoanalysis; Sociology; Humanities; Cognitive science; Epistemology; Philosophy; Computer science","score_opus":0.06352086191095974,"score_gpt":0.32111016593329367,"score_spread":0.25758930402233393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W586938665","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003032794,0.0005619468,0.23444809,0.0007930884,0.0009068631,0.0019260838,0.0003772596,0.00018083301,0.7605026],"genre_scores_gemma":[0.056022994,0.0038693787,0.16787179,0.0005349159,0.001223322,0.001875161,0.00041303947,0.00015043757,0.768039],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99711543,0.00010853101,0.0008213403,0.0009625233,0.0005599881,0.0004322109],"domain_scores_gemma":[0.9978012,0.00025134656,0.00040706573,0.0010885113,0.00029751373,0.0001543488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018471552,0.00043332644,0.0005475206,0.0006225712,0.00031807943,0.000032448843,0.0009840803,0.00040329824,0.00020256526],"category_scores_gemma":[0.000014754715,0.00044456855,0.00024632938,0.00089072465,0.00032154119,0.0002560249,0.0002779269,0.00077370956,0.0005752119],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023150844,0.0016900954,0.000070844646,0.00001728435,0.00004364091,0.000043116204,0.00056907296,0.0006289462,0.00007654052,0.9745282,0.015271588,0.0070375386],"study_design_scores_gemma":[0.005566922,0.009240477,0.05941844,0.0023278522,0.00058594556,0.00037544878,0.00064914575,0.09599377,0.00128667,0.13500124,0.68343794,0.006116132],"about_ca_topic_score_codex":0.00041024544,"about_ca_topic_score_gemma":0.00029685817,"teacher_disagreement_score":0.83952695,"about_ca_system_score_codex":0.00052222534,"about_ca_system_score_gemma":0.0010884509,"threshold_uncertainty_score":0.9998006},"labels":[],"label_agreement":null},{"id":"W649579275","doi":"","title":"Learning Classifier Systems : 11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, Montreal, QC, Canada, July 9, 2009 : revised selected papers","year":2010,"lang":"en","type":"book","venue":"Springer eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Atlanta; Library science; Artificial intelligence; Geography; Operations research; Computer science; Engineering; Archaeology","score_opus":0.00955745056348549,"score_gpt":0.21491400588531884,"score_spread":0.20535655532183333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W649579275","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008574071,0.0051153377,0.02864915,0.0030012166,0.00760432,0.0024694058,0.00064853736,0.00076331914,0.9508913],"genre_scores_gemma":[0.010850574,0.0006551915,0.004104008,0.00044443403,0.0026459247,0.0002757369,0.00049004436,0.00014957365,0.9803845],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99430364,0.00013612246,0.0012073279,0.0017798643,0.0015506414,0.0010223796],"domain_scores_gemma":[0.9958874,0.00044183127,0.0009783725,0.0011725756,0.00092252117,0.00059727614],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00053672027,0.000984524,0.0008613097,0.0005367852,0.00073735794,0.0008641202,0.0025553966,0.00085784757,0.00024104312],"category_scores_gemma":[0.00014892285,0.0010263361,0.00023796373,0.0002880121,0.0002630777,0.00039015996,0.0008116832,0.0028736023,0.00017029673],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007709562,0.00015359123,0.0010010191,0.00014101768,0.0009248568,0.0002844779,0.00048790532,0.0016512218,0.000538684,0.005458933,0.9341667,0.05511445],"study_design_scores_gemma":[0.000786723,0.000040227827,0.0062924423,0.0006652897,0.0000962384,0.00017889145,0.000034693672,0.023044901,0.000021236438,0.00025469027,0.9674104,0.0011742542],"about_ca_topic_score_codex":0.022917675,"about_ca_topic_score_gemma":0.08877039,"teacher_disagreement_score":0.06585272,"about_ca_system_score_codex":0.00095973053,"about_ca_system_score_gemma":0.0029125516,"threshold_uncertainty_score":0.9994268},"labels":[],"label_agreement":null},{"id":"W6539208","doi":"10.1016/0277-5379(84)90243-8","title":"Enhancing the GA's ability to cope with dynamic environments","year":2000,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Genetic algorithm; Balance (ability); Distributed computing; Dynamic balance; Wright; Flexibility (engineering); Machine learning; Engineering; Programming language; Mathematics; Biology","score_opus":0.007714113838109909,"score_gpt":0.21976280536040188,"score_spread":0.21204869152229197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6539208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20217985,0.00017150247,0.793353,0.0034512223,0.000033054348,0.0002951602,0.0000055733403,0.000049358503,0.00046128107],"genre_scores_gemma":[0.85507506,0.00006848363,0.14385286,0.00033692896,0.000021821515,0.00006950232,0.000009781423,0.000004915102,0.00056065014],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99882436,0.000069199436,0.00021346784,0.00043623248,0.0002462304,0.00021049313],"domain_scores_gemma":[0.9993748,0.00009209339,0.00004517224,0.00031730763,0.000053654134,0.000116942145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108853346,0.00013428608,0.00009952237,0.000035319812,0.00047504145,0.000083210245,0.00034686842,0.000032379445,0.00011032951],"category_scores_gemma":[0.0000047925364,0.00010181149,0.000019779669,0.0002643197,0.00013865823,0.00017868234,0.000096184034,0.00008784238,0.00016339352],"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":[0.000051189436,0.00034599786,0.0026829583,0.000029935058,0.00004711281,0.000008906163,0.0025393944,0.26559147,0.0029185198,0.01680062,0.0015658196,0.7074181],"study_design_scores_gemma":[0.00022519923,0.00013734034,0.5281505,0.000017499957,0.000007980936,0.00006793917,0.000065418695,0.45608476,0.000028777178,0.008622051,0.006389866,0.0002026589],"about_ca_topic_score_codex":0.000029447716,"about_ca_topic_score_gemma":0.000015378007,"teacher_disagreement_score":0.7072154,"about_ca_system_score_codex":0.00005173449,"about_ca_system_score_gemma":0.000105324776,"threshold_uncertainty_score":0.41517523},"labels":[],"label_agreement":null},{"id":"W66126747","doi":"10.1007/978-3-662-44303-3_4","title":"Semantic Crossover Based on the Partial Derivative Error","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Crossover; Symbolic regression; Genetic programming; Computer science; Operator (biology); Genetic algorithm; Semantics (computer science); Artificial intelligence; Theoretical computer science; Algorithm; Machine learning; Programming language","score_opus":0.01993822553721742,"score_gpt":0.2539230823723656,"score_spread":0.2339848568351482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W66126747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005708145,0.00004101806,0.98831934,0.007539223,0.0006661809,0.00040205172,0.0000056437507,0.000106638545,0.002862826],"genre_scores_gemma":[0.69527274,0.0000054110187,0.29076004,0.01215663,0.0009111274,0.000068930014,0.000007727779,0.000040357107,0.0007770273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971088,0.000046554505,0.0003377637,0.001154449,0.00086540374,0.00048700217],"domain_scores_gemma":[0.99684376,0.0010389425,0.00022247202,0.0016053094,0.0001796732,0.00010987313],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000685149,0.00038485145,0.00029413,0.00026049564,0.00061343436,0.00043081658,0.0030004417,0.00017595458,0.000046310637],"category_scores_gemma":[0.000089077774,0.00026840737,0.00012556519,0.00051267084,0.0009008933,0.00019346538,0.0006249059,0.0006470486,0.00013920444],"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":[0.000009010861,0.00007672722,0.000034408873,0.000020363186,0.000012782356,0.000027098089,0.00046950387,0.23673837,0.00007988855,0.6028717,0.00040541677,0.15925473],"study_design_scores_gemma":[0.00014601802,0.00009862736,0.00026225953,0.00012413207,0.0000037192776,0.000009659015,5.940154e-8,0.8965461,0.0003850378,0.09710171,0.004999173,0.00032348806],"about_ca_topic_score_codex":0.000012504637,"about_ca_topic_score_gemma":0.00001282089,"teacher_disagreement_score":0.6975593,"about_ca_system_score_codex":0.00015763952,"about_ca_system_score_gemma":0.00040449848,"threshold_uncertainty_score":0.9999768},"labels":[],"label_agreement":null},{"id":"W6893963877","doi":"10.5281/zenodo.5967132","title":"Pleocoma laker Marshall 2018, new species","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Seta; Type locality; Arthropod; Groove (engineering); Holotype","score_opus":0.04159437940257591,"score_gpt":0.24381786294624444,"score_spread":0.20222348354366854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893963877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020982358,0.000074101015,0.6039906,0.007017861,0.00021139506,0.0003538297,0.00005418294,0.0014557787,0.38474402],"genre_scores_gemma":[0.6879829,0.00027210976,0.14580257,0.002684044,0.005197269,3.1821426e-7,0.0016900696,0.0028400717,0.15353061],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987009,0.00009981952,0.00017770183,0.00040743416,0.0003032118,0.0003108927],"domain_scores_gemma":[0.99866396,0.000017209475,0.00006799688,0.00068030745,0.00037252484,0.00019798827],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002882579,0.00010798136,0.00008831823,0.00012711632,0.0019470457,0.0007693473,0.0017817401,0.00004277737,0.008734869],"category_scores_gemma":[0.00010826184,0.000110995185,0.000040581835,0.0006604752,0.00017136957,0.00047127536,0.00148506,0.00013936743,0.025534578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049630617,0.00007433477,0.000003559934,0.0000044024255,0.00001116432,0.0000033713718,0.0005092052,0.000005460305,0.0015153292,0.16079924,0.7703961,0.06667288],"study_design_scores_gemma":[0.00021593017,0.00013941614,0.0028092093,0.000008610824,0.0000027744088,0.00007008152,0.000039880655,0.0026341628,0.00031142344,0.0030567797,0.99057764,0.00013407384],"about_ca_topic_score_codex":0.0000134101,"about_ca_topic_score_gemma":5.785252e-7,"teacher_disagreement_score":0.6858847,"about_ca_system_score_codex":0.000072097086,"about_ca_system_score_gemma":0.0000072570724,"threshold_uncertainty_score":0.9993523},"labels":[],"label_agreement":null},{"id":"W6908337423","doi":"10.26071/eee1e907-cc6a-4e04","title":"Mémoires et évaluations environnementales stratégiques rédigés par le Comité ZIP CNG","year":2025,"lang":"en","type":"dataset","venue":"OGSL repository","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bay; Environmental impact assessment; Submarine pipeline; Offshore oil and gas; Strategic environmental assessment; Fossil fuel","score_opus":0.011374596972211942,"score_gpt":0.2657174862730781,"score_spread":0.2543428893008662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6908337423","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000079732265,0.0030879856,0.047994673,0.0048669474,0.001360426,0.0008908258,0.9324672,0.00038647171,0.008865696],"genre_scores_gemma":[0.00089722825,0.00076215575,0.025984168,0.0007226755,0.0004260607,0.00055035565,0.96630895,0.000014642138,0.004333765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99789023,0.00024204081,0.0004704293,0.0007056025,0.00042571564,0.0002659744],"domain_scores_gemma":[0.99779826,0.00025534685,0.00026113426,0.0015044969,0.00007874593,0.00010203224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002742935,0.0003018519,0.00027774263,0.00016213296,0.0008474196,0.0002949814,0.0014111659,0.00023529452,0.000013997029],"category_scores_gemma":[0.00002768577,0.00031852227,0.00015723011,0.00023916482,0.00016358965,0.00037040462,0.00055730995,0.00042056257,0.000023350416],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.002788e-7,0.00025124903,0.000014103384,0.00003724322,0.00005034462,0.000016343363,0.00002261546,0.000072252675,0.00017900793,0.016168391,0.9826358,0.0005517465],"study_design_scores_gemma":[0.0001629078,0.000043455675,0.0010170145,0.000104425606,0.000047863556,0.00003065493,0.000025697676,0.0011767698,0.00059025345,0.0018956506,0.9945711,0.0003342045],"about_ca_topic_score_codex":0.0007043497,"about_ca_topic_score_gemma":0.00009782221,"teacher_disagreement_score":0.033841707,"about_ca_system_score_codex":0.00013932904,"about_ca_system_score_gemma":0.00083964283,"threshold_uncertainty_score":0.9999267},"labels":[],"label_agreement":null},{"id":"W6924581681","doi":"10.15468/dl.wyw3tt","title":"Occurrence Download","year":2019,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Order (exchange)","score_opus":0.016684649335686825,"score_gpt":0.22463040174169718,"score_spread":0.20794575240601035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924581681","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016985103,0.000021878612,0.002567843,0.00046717317,0.00058090733,0.00033278318,0.9957738,0.00011773333,0.00012087798],"genre_scores_gemma":[0.0000037942636,0.00004936227,0.00006649628,0.0005301088,0.0000015888912,0.000006751693,0.9993416,6.488742e-9,2.8847916e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985846,0.000039412727,0.00032617993,0.00030738304,0.00047445222,0.00026793743],"domain_scores_gemma":[0.99826634,0.000022599688,0.00026764587,0.0010509985,0.0002596312,0.00013278214],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018484822,0.0002320627,0.00021004664,0.00007948125,0.0002983511,0.0002407657,0.0016078078,0.00025262445,0.00036543742],"category_scores_gemma":[0.000035988196,0.00024038856,0.00013304719,0.0004907209,0.00008841578,0.001987946,0.00078065816,0.00024849112,0.34645897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028956913,0.000036763548,0.00011010753,0.000050145372,0.000012508328,5.523608e-7,0.00001707287,0.000035667104,1.7081673e-8,0.000005283098,0.9982731,0.0014559008],"study_design_scores_gemma":[0.00018564244,0.000023911185,0.000108834334,0.0000015736564,0.000012404592,0.0000065287745,0.000017706228,0.000014978512,7.349248e-7,0.000007274126,0.99936295,0.00025744148],"about_ca_topic_score_codex":0.00014840133,"about_ca_topic_score_gemma":0.0000019951594,"teacher_disagreement_score":0.34609354,"about_ca_system_score_codex":0.0002486468,"about_ca_system_score_gemma":0.00022280624,"threshold_uncertainty_score":0.9802761},"labels":[],"label_agreement":null},{"id":"W6924798317","doi":"10.15468/dl.rdaxx5","title":"Occurrence Download","year":2023,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); R package; Data set","score_opus":0.017232849828101738,"score_gpt":0.2276852754107917,"score_spread":0.21045242558268995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924798317","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010622493,0.0000114557915,0.0016078723,0.00069681706,0.0006155295,0.0002561575,0.9963837,0.0003671482,0.00005068507],"genre_scores_gemma":[7.725255e-7,0.000072489165,0.00004890079,0.00033593734,0.0000020391863,0.000012224801,0.99952734,9.126675e-9,3.1860057e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99854326,0.000038536222,0.0003367547,0.0003011357,0.00049368007,0.0002866381],"domain_scores_gemma":[0.9984683,0.000029608507,0.00023136807,0.00087198144,0.0002451632,0.00015361715],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023021305,0.00022190876,0.0001893422,0.00010263896,0.0004287316,0.00024646407,0.0015483391,0.00023705258,0.000091805254],"category_scores_gemma":[0.00006387722,0.00023458792,0.00012892387,0.0008555541,0.000104505554,0.0016077714,0.0008930767,0.00023633312,0.58419555],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019963209,0.000027363292,0.00004691009,0.00003922725,0.000013231287,0.0000014695888,0.000016864891,0.00002378103,1.07070335e-8,0.0000057391817,0.9981423,0.0016810874],"study_design_scores_gemma":[0.00014473626,0.000017647542,0.00016609236,0.0000016149414,0.000011491567,0.00000491825,0.000024370296,0.000012556754,4.4824478e-7,0.000017818315,0.99935764,0.00024066701],"about_ca_topic_score_codex":0.0005066032,"about_ca_topic_score_gemma":0.000008908784,"teacher_disagreement_score":0.5841037,"about_ca_system_score_codex":0.00023529514,"about_ca_system_score_gemma":0.00019005171,"threshold_uncertainty_score":0.95662177},"labels":[],"label_agreement":null},{"id":"W6925067063","doi":"10.17605/osf.io/he3wa","title":"A new tool to assess quality of epidemiological studies with their risk of bias – Extension of The Newcastle Ottawa Scale and The Risk Of Bias In Non-randomized Studies Tools (ROBINS-E and ROBINS-I)","year":2024,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Observational study; Scale (ratio); Quality (philosophy); Risk assessment; Extension (predicate logic); Information bias; Observational methods in psychology","score_opus":0.10561222441925909,"score_gpt":0.34744514346721006,"score_spread":0.24183291904795096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925067063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9267107,0.00023832168,0.0690126,0.0010633894,0.000088300476,0.0016224796,0.000037217833,0.000024104007,0.0012028815],"genre_scores_gemma":[0.96581656,0.002322607,0.030451834,0.000033345084,0.000015762665,0.00018983516,7.1393123e-7,0.000008766335,0.0011605946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943736,0.0029067479,0.001190282,0.0010172477,0.00031663117,0.00019545884],"domain_scores_gemma":[0.983701,0.013052828,0.000794682,0.0020224256,0.0003642819,0.00006480271],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019552946,0.00023417955,0.0012544642,0.00010159303,0.00012564196,0.000028630919,0.0008031234,0.00008393816,0.00014543142],"category_scores_gemma":[0.013051308,0.00011676058,0.0002431746,0.0005908219,0.0013405159,0.0002143636,0.002221227,0.000295839,0.00010065368],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011141036,0.0017274403,0.43055913,0.0022505228,0.0055715214,0.00000713799,0.099365234,0.056416955,0.014541331,0.21412037,0.0067088003,0.15759051],"study_design_scores_gemma":[0.018456984,0.00003161778,0.78651816,0.0015056634,0.00040396262,0.000027413835,0.0049656257,0.053529784,0.012949492,0.12059354,0.00052681204,0.0004909288],"about_ca_topic_score_codex":0.0012416464,"about_ca_topic_score_gemma":0.00029530525,"teacher_disagreement_score":0.35595903,"about_ca_system_score_codex":0.00004230378,"about_ca_system_score_gemma":0.00012607155,"threshold_uncertainty_score":0.9952622},"labels":[],"label_agreement":null},{"id":"W6926466286","doi":"10.25316/ir-13514","title":"The Nanaimo Free Press [Saturday, April 18, 1891]","year":2019,"lang":"en","type":"other","venue":"VIUSpace (Vancouver Island University Library)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.007455022191139195,"score_gpt":0.1834933708990702,"score_spread":0.176038348707931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926466286","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.6211467e-7,0.0011081777,0.031023609,0.0033179172,0.0013593026,0.0004757361,0.00020849668,0.0006292011,0.9618768],"genre_scores_gemma":[0.000037550984,0.0015573441,0.0089177145,0.00015566546,0.00031568643,0.0000031194004,0.000008458476,0.00012424354,0.9888802],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99826956,0.000099238096,0.00013268834,0.00068540283,0.00038008858,0.00043299742],"domain_scores_gemma":[0.9973645,0.00015276694,0.00025007708,0.002054621,0.000030440513,0.00014754935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000046800313,0.00035057616,0.00029524494,0.00017610526,0.0004504192,0.00022487548,0.0036771062,0.00030927855,0.00020573303],"category_scores_gemma":[0.000007724848,0.00028309814,0.00017946382,0.00051144767,0.00015330764,0.0008396657,0.0016150757,0.0003997045,0.0002261506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006596032,0.00003939373,0.0000055500595,0.000019257348,0.000065864064,0.00001994825,0.000067748595,0.000021740198,7.9281926e-7,0.0362226,0.96281207,0.0007184134],"study_design_scores_gemma":[0.0005559708,0.000031832027,0.000030094823,0.000056736364,0.000025391502,0.0000021193314,0.00007531097,0.0017189437,0.000010254155,0.0009946835,0.9961023,0.00039641105],"about_ca_topic_score_codex":0.00053944637,"about_ca_topic_score_gemma":0.03763254,"teacher_disagreement_score":0.03709309,"about_ca_system_score_codex":0.000053558193,"about_ca_system_score_gemma":0.0002460213,"threshold_uncertainty_score":0.9999621},"labels":[],"label_agreement":null},{"id":"W6928604205","doi":"10.3886/e235344v1","title":"Investigating Nature-based Preschoolers Gains in Early Literacy and Select Executive Function Skills","year":2025,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Learning Partnership","funders":"","keywords":"Early literacy; Early childhood; Literacy; Executive functions; Working memory; Function (biology); Early childhood education","score_opus":0.01356173153692664,"score_gpt":0.2900138833691069,"score_spread":0.27645215183218025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6928604205","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010306131,0.0010109748,0.014155458,0.00087445177,0.00035448463,0.0005881024,0.9818203,0.00012537719,0.000040272113],"genre_scores_gemma":[0.000937776,0.00018754268,0.02396371,0.0015194484,0.00015462172,0.00011774501,0.973004,0.00001030623,0.000104851024],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973495,0.00010719191,0.0004291523,0.0013597118,0.00036635582,0.00038807938],"domain_scores_gemma":[0.99675524,0.00039294065,0.0002793316,0.0023236473,0.00010331119,0.00014553696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004922486,0.0003525653,0.0003211138,0.0004730956,0.00024956436,0.0005810681,0.0024226352,0.00041777937,0.0000046820737],"category_scores_gemma":[0.00038612497,0.00037313925,0.000041279734,0.0013202076,0.00011320807,0.0017386475,0.0014696345,0.0013149893,0.00000707515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027597412,0.000092003225,0.00031476596,0.00008288826,0.00002394883,0.0000067263245,0.000051665786,0.000034446533,0.000020564708,0.0010786622,0.99651706,0.0017745257],"study_design_scores_gemma":[0.00054296746,0.00007037896,0.008192448,0.00068061927,0.00005023357,0.0000065978534,0.000006721739,0.025929723,0.000041311938,0.002012763,0.96194917,0.0005170481],"about_ca_topic_score_codex":0.00065665203,"about_ca_topic_score_gemma":0.00005029383,"teacher_disagreement_score":0.034567855,"about_ca_system_score_codex":0.000108575034,"about_ca_system_score_gemma":0.00040356308,"threshold_uncertainty_score":0.999872},"labels":[],"label_agreement":null},{"id":"W6929325549","doi":"10.48550/arxiv.1708.04316","title":"A High Resolution Survey of the Galactic Plane at 408 MHz","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Galactic plane; Angular resolution (graph drawing); Telescope; Longitude; Radio telescope; Observatory; Latitude; Interstellar medium; Geographic coordinate system","score_opus":0.08303354448292977,"score_gpt":0.19911620096719226,"score_spread":0.1160826564842625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929325549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6210695,0.00012956143,0.37448525,0.00059085834,0.0010309523,0.0005028678,0.00022042735,0.00013198589,0.001838598],"genre_scores_gemma":[0.9950333,0.000082947496,0.0013379735,0.000017506176,0.000039270362,0.0000013909226,0.000043156597,0.000007656626,0.0034367933],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9986613,0.0002011798,0.00016586753,0.0006513539,0.000113995055,0.00020630565],"domain_scores_gemma":[0.9968438,0.00014198972,0.0004987767,0.002232775,0.00020787488,0.000074768504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035288357,0.00017466862,0.00021617154,0.00007873481,0.0005028232,0.000047513277,0.0025785866,0.00018361509,0.000017029763],"category_scores_gemma":[0.000066237655,0.00016155481,0.00012768005,0.00029089474,0.00020757581,0.00020158158,0.0030502153,0.00033643024,0.00005130101],"study_design_candidate":"observational","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":[0.00010840396,0.0005426232,0.08521425,0.00018080111,0.00023622513,0.000055579785,0.00028574123,0.38835177,0.00025022443,0.5176632,0.006554395,0.0005568066],"study_design_scores_gemma":[0.00030499455,0.000025672332,0.71582097,0.00006078104,0.00004501574,0.0000076074234,0.0000045189786,0.2572772,0.00011459304,0.025327342,0.00073788746,0.00027342932],"about_ca_topic_score_codex":0.003908445,"about_ca_topic_score_gemma":0.0007631726,"teacher_disagreement_score":0.6306067,"about_ca_system_score_codex":0.00022811728,"about_ca_system_score_gemma":0.00023226731,"threshold_uncertainty_score":0.65880144},"labels":[],"label_agreement":null},{"id":"W6930206760","doi":"10.5281/zenodo.11716417","title":"Fly pattern encyclopedia pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Encyclopedia; Trout; Fishing; On the fly; Fish <Actinopterygii>","score_opus":0.018174348772054486,"score_gpt":0.23459296043622999,"score_spread":0.2164186116641755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930206760","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011637391,0.000440504,0.1627489,0.0009729929,0.0002566506,0.0002954339,0.00019052318,0.0019819364,0.8331119],"genre_scores_gemma":[0.0003519696,0.0005205112,0.0036721851,0.00020965855,0.00079292944,1.9584043e-7,0.0012142608,0.005464109,0.9877742],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828714,0.000118726246,0.00020586552,0.00068801845,0.00037357895,0.00032664617],"domain_scores_gemma":[0.99872714,0.000009959476,0.00010266558,0.0008696531,0.00012783622,0.00016275936],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00024323685,0.00020251704,0.00015435749,0.0003497727,0.0006445511,0.0009524269,0.0022405856,0.00012945235,0.05607105],"category_scores_gemma":[0.000044677454,0.00021083684,0.00007313196,0.00056399376,0.00009360936,0.00015507203,0.001993947,0.0003557294,0.31939825],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.563221e-7,0.000047951035,9.068556e-8,0.000051619783,0.000028745979,0.0000143544,0.00017646601,0.0000016163395,0.000019606052,0.009987294,0.88661116,0.10306066],"study_design_scores_gemma":[0.00010269182,0.00004968065,0.000015273583,0.00006819776,0.00001104533,0.000076377524,0.000021666428,0.0011353174,0.0000049002715,0.00111433,0.99717,0.0002305634],"about_ca_topic_score_codex":0.000028269045,"about_ca_topic_score_gemma":5.027045e-7,"teacher_disagreement_score":0.2633272,"about_ca_system_score_codex":0.00008866465,"about_ca_system_score_gemma":0.0000060417183,"threshold_uncertainty_score":0.94479185},"labels":[],"label_agreement":null},{"id":"W6930209331","doi":"10.5281/zenodo.11247719","title":"Careocallus densicollis Cherman and Smith 2024","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Museum of Nature","funders":"","keywords":"Paratype; Holotype; Matrix (chemical analysis); Class (philosophy); Trinomial","score_opus":0.03027393476302507,"score_gpt":0.24296890767239468,"score_spread":0.21269497290936962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930209331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0955708,0.00089325215,0.43698308,0.030972596,0.0007828148,0.0020118442,0.00044199105,0.012048343,0.42029527],"genre_scores_gemma":[0.95671475,0.00084666425,0.01277158,0.0007173455,0.00056405633,5.48187e-7,0.00107711,0.0016818085,0.025626153],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99899834,0.00008316365,0.0001203771,0.00034259813,0.00021559533,0.00023994381],"domain_scores_gemma":[0.9992063,0.000020234136,0.00003509846,0.00038549904,0.0002197775,0.00013307069],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030490698,0.00008004352,0.000072016766,0.00015206815,0.0016016434,0.0005922889,0.00080073986,0.000033560184,0.0005968236],"category_scores_gemma":[0.00008315191,0.00008360582,0.000022832115,0.0010040023,0.000090718255,0.00026894748,0.0014017835,0.00013067682,0.005950496],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004347053,0.00006391757,0.0000075939283,0.000034389705,0.00002500707,0.000026212203,0.0013686009,0.00013582247,0.001414448,0.07474339,0.6971049,0.22507142],"study_design_scores_gemma":[0.0001770271,0.000060751605,0.0038310403,0.000009638345,0.0000034136867,0.000082314196,0.000104162755,0.027606228,0.000084358086,0.0016217072,0.9663016,0.00011774343],"about_ca_topic_score_codex":0.000008023451,"about_ca_topic_score_gemma":1.2139049e-7,"teacher_disagreement_score":0.86114395,"about_ca_system_score_codex":0.000049184167,"about_ca_system_score_gemma":0.0000040099885,"threshold_uncertainty_score":0.99969816},"labels":[],"label_agreement":null},{"id":"W6931321244","doi":"10.5281/zenodo.5595764","title":"Geographic homogenization but little net change in the local richness of Canadian butterflies","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Species richness; Generalist and specialist species; Biodiversity; Species diversity; Beta diversity; Range (aeronautics); Butterfly; Global biodiversity; Taxon","score_opus":0.030200388838904947,"score_gpt":0.22490379363660265,"score_spread":0.1947034047976977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931321244","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044445245,0.0018334435,0.06380782,0.007029932,0.00030587497,0.0022675612,0.0019374164,0.0010445449,0.92132896],"genre_scores_gemma":[0.79720473,0.0067014815,0.010908719,0.004736027,0.0025776825,0.000018497662,0.037645485,0.018852824,0.12135455],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984751,0.0003063892,0.00019547029,0.0003688521,0.00036811607,0.00028606923],"domain_scores_gemma":[0.9989238,0.000018544242,0.00013172634,0.00069701916,0.00013680928,0.000092123795],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044778732,0.0001351335,0.00013455888,0.001308994,0.00091739505,0.0002441508,0.0023984788,0.00008098925,0.008620401],"category_scores_gemma":[0.00004109119,0.00012929171,0.000043549182,0.0026792714,0.00015331568,0.0001421475,0.00080435356,0.00026450423,0.00056372216],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036305692,0.0002027287,0.00007414743,0.000088326706,0.000039142586,0.00002376392,0.0025341322,0.00010334347,0.000020715075,0.08313474,0.82972383,0.08405149],"study_design_scores_gemma":[0.00012403679,0.000058393776,0.0019074078,0.000018790737,0.000005763125,0.000035452482,0.00022285065,0.0014611764,0.0000028899804,0.00020272564,0.9958253,0.00013524437],"about_ca_topic_score_codex":0.017014246,"about_ca_topic_score_gemma":0.00073137134,"teacher_disagreement_score":0.7999744,"about_ca_system_score_codex":0.00010803434,"about_ca_system_score_gemma":0.000013497324,"threshold_uncertainty_score":0.99228585},"labels":[],"label_agreement":null},{"id":"W6931380245","doi":"10.5281/zenodo.4298244","title":"Fig. 51 in The 'red-tailed' Lasioglossum (Dialictus) (Hymenoptera: Halictidae) of the western Nearctic","year":2020,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Nearctic ecozone; Principle of maximum entropy; Georeference; Niche; Entropy (arrow of time)","score_opus":0.024904850325662645,"score_gpt":0.23575087438584233,"score_spread":0.2108460240601797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931380245","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010758713,0.00035141705,0.019188432,0.019093059,0.00027096027,0.0015974762,0.00035027598,0.00085044326,0.9581903],"genre_scores_gemma":[0.4690015,0.0019329111,0.011748523,0.00940149,0.004014875,0.0000063003863,0.003260686,0.019268138,0.4813656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790245,0.00048575486,0.0002938813,0.00048203155,0.00054627314,0.00028957793],"domain_scores_gemma":[0.99838656,0.00003292739,0.00023164178,0.0011409947,0.00012463659,0.00008322951],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00039097207,0.00019120236,0.00019783058,0.00017011112,0.0007729171,0.00048774548,0.0042277207,0.00010414976,0.0024189283],"category_scores_gemma":[0.00013155183,0.0001385547,0.00008842292,0.0011872202,0.00018798356,0.00014306119,0.0018868626,0.00043400048,0.0021320037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048035977,0.00015732736,0.000026220934,0.00007284694,0.000037882994,0.000007255908,0.0017195955,0.000031207554,0.000100515004,0.018939126,0.96633697,0.012566223],"study_design_scores_gemma":[0.00022966524,0.00006750108,0.0014729382,0.000072539435,0.000010423117,0.00003653022,0.00005395736,0.0007908861,0.000015961155,0.00026328934,0.9968438,0.00014251257],"about_ca_topic_score_codex":0.00010286862,"about_ca_topic_score_gemma":0.0000042657143,"teacher_disagreement_score":0.47682476,"about_ca_system_score_codex":0.00006836227,"about_ca_system_score_gemma":0.0000117900045,"threshold_uncertainty_score":0.99864495},"labels":[],"label_agreement":null},{"id":"W6967885775","doi":"10.5281/zenodo.12444636","title":"ad&amp;d 1st edition fiend folio pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ridiculous; Subject (documents); Staring; Paraphernalia; TSG101","score_opus":0.02960183482362941,"score_gpt":0.2513329299512042,"score_spread":0.2217310951275748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967885775","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013349049,0.0005504954,0.18861148,0.001416351,0.0003849904,0.00033870386,0.00028932738,0.001972625,0.8064347],"genre_scores_gemma":[0.00033986353,0.0003835119,0.009535692,0.00017861089,0.000984167,3.151092e-7,0.0029421716,0.004390841,0.9812448],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982011,0.00012400409,0.00021640495,0.00070030254,0.00042366015,0.0003345636],"domain_scores_gemma":[0.99862593,0.000012339549,0.00012068585,0.0008753431,0.00019756265,0.00016813415],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00028130764,0.00020559155,0.00015784412,0.00044872737,0.0008366841,0.0010523915,0.0018258451,0.00015000532,0.052227374],"category_scores_gemma":[0.000080887956,0.0002189902,0.00008178626,0.0008448813,0.00011631754,0.0002188344,0.0015819801,0.00036958008,0.25000465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012419866,0.00006431563,1.8710082e-8,0.000059089387,0.0000303308,0.0000070350425,0.00013124681,0.0000034919983,0.00008030157,0.027258206,0.94800204,0.024362702],"study_design_scores_gemma":[0.00012529183,0.000052480158,0.000010503478,0.00009490734,0.000013351298,0.00009346233,0.000018875598,0.0006781244,0.000011478123,0.0026038701,0.9960644,0.000233235],"about_ca_topic_score_codex":0.000017396698,"about_ca_topic_score_gemma":0.0000010558581,"teacher_disagreement_score":0.19777727,"about_ca_system_score_codex":0.000120089426,"about_ca_system_score_gemma":0.000007334809,"threshold_uncertainty_score":0.9999846},"labels":[],"label_agreement":null},{"id":"W6977082686","doi":"10.6084/m9.figshare.26670396","title":"Additional file 1 of Evolutionary shift detection with ensemble variable selection","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Feature selection; Variable (mathematics); Selection (genetic algorithm); Pattern recognition (psychology)","score_opus":0.011011703944231468,"score_gpt":0.20593272718519942,"score_spread":0.19492102324096794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977082686","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.413953e-7,0.00006665803,0.029272212,0.00004870915,0.000024100076,0.00010506824,0.9654297,0.0002612736,0.0047915],"genre_scores_gemma":[0.004425392,3.9659943e-7,0.12269072,0.0000379753,0.00021583904,0.0021339813,0.86861765,0.000013543305,0.0018645215],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992745,0.000015551987,0.00011461615,0.00026758845,0.00019653836,0.00013122009],"domain_scores_gemma":[0.9990977,0.0005294127,0.00004520156,0.00015804626,0.0001269468,0.000042709926],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000013451613,0.000082985054,0.00006414772,0.000093201044,0.00014264866,0.000056770037,0.0001905115,0.00005289943,0.9333155],"category_scores_gemma":[0.000112938564,0.00007652069,0.000037031572,0.00085962034,0.0000070629912,0.0005162101,0.000060471317,0.00011310763,0.001842137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011353668,0.000025713747,1.2604573e-7,0.000036758076,0.000010658011,0.0000015458977,0.00001165134,0.00021432091,0.000039380127,0.0023016857,0.99408734,0.003269676],"study_design_scores_gemma":[0.000028264609,0.00006017711,0.00092173024,0.0006452273,0.0000022110667,0.000060237733,0.00000240501,0.13446507,0.00016968492,0.0030511974,0.86050135,0.000092414295],"about_ca_topic_score_codex":0.0000061442533,"about_ca_topic_score_gemma":0.0000063353327,"teacher_disagreement_score":0.93147343,"about_ca_system_score_codex":0.000060298644,"about_ca_system_score_gemma":0.00027100937,"threshold_uncertainty_score":0.99893504},"labels":[],"label_agreement":null},{"id":"W6977343171","doi":"10.6084/m9.figshare.14490230","title":"Additional file 5 of Joint hypermobility in athletes is associated with shoulder injuries: a systematic review and meta-analysis","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Memorial Chiropractic College","funders":"","keywords":"Athletes; Joint hypermobility; Joint (building); Hypermobility (travel); Joint instability","score_opus":0.059057525288557594,"score_gpt":0.26682440472603886,"score_spread":0.20776687943748126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977343171","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000012258525,0.005270897,0.000047685895,0.00043167215,4.92577e-7,0.00027465975,0.9938179,0.000017919368,0.00013750102],"genre_scores_gemma":[0.001252953,0.000022047072,0.008883712,0.0016421564,0.0000052733567,0.0077793365,0.9789702,0.000007733448,0.0014365738],"study_design_codex":"not_applicable","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9990272,0.000096461714,0.0003052856,0.00027490346,0.00020542023,0.0000907215],"domain_scores_gemma":[0.9981422,0.0008804861,0.00019723256,0.000401737,0.00034149576,0.000036882855],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000067121124,0.000090899935,0.00069640845,0.000051018993,0.000044775665,0.000025866346,0.00016761987,0.000032321608,0.90504366],"category_scores_gemma":[0.0017563181,0.00006687193,0.00021482579,0.0011659085,0.000009827165,0.0001466966,0.00012180526,0.00006739405,0.000046540623],"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":[7.3022456e-8,0.0000643587,6.231674e-7,0.0076238425,0.0038957237,0.0000028541617,0.000034160796,0.000005676261,2.6110285e-7,0.00003551565,0.98833275,0.0000041458347],"study_design_scores_gemma":[0.0014166727,0.0005107202,0.06860135,0.28249633,0.36704856,0.00021675418,0.0004877029,0.13369499,0.00047915816,0.0055519594,0.1349178,0.0045780116],"about_ca_topic_score_codex":0.0000023543448,"about_ca_topic_score_gemma":0.000020635434,"teacher_disagreement_score":0.90499717,"about_ca_system_score_codex":0.000022368644,"about_ca_system_score_gemma":0.00010199103,"threshold_uncertainty_score":0.2726958},"labels":[],"label_agreement":null},{"id":"W6977418317","doi":"10.6084/m9.figshare.28873966","title":"Additional file 1 of Connecting women who are diagnosed and treated for breast cancer to engage in physical activity: a two-arm randomized controlled trial","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; University of Calgary; University of Toronto; University Health Network; Queen's University; McMaster University","funders":"","keywords":"Randomized controlled trial; Breast cancer; MEDLINE; Clinical trial","score_opus":0.01654812176527181,"score_gpt":0.28193962177878573,"score_spread":0.2653915000135139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977418317","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064090504,0.000012834717,0.00015009548,0.0003789383,0.000015102123,0.0027552808,0.9900332,0.000036451165,0.00020903762],"genre_scores_gemma":[0.45174408,0.000003232079,0.004138011,0.00047222778,0.0005135209,0.34552705,0.1959797,0.000028561059,0.0015936238],"study_design_codex":"not_applicable","study_design_gemma":"randomized_trial","domain_scores_codex":[0.999206,0.000106973,0.00017007682,0.00026164137,0.00009435992,0.00016093317],"domain_scores_gemma":[0.98912287,0.010422108,0.00014549553,0.00013141344,0.00012600784,0.000052136747],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008743574,0.00010462222,0.00060247816,0.00009833203,0.0001109128,0.00004007432,0.00016535056,0.000033300515,0.22175822],"category_scores_gemma":[0.0025724978,0.000089301684,0.00011324825,0.00033099478,0.000009005043,0.00012970818,0.00009465362,0.000085014304,0.000008901838],"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":[0.06966707,0.00047699024,4.788702e-7,0.000049994153,0.000088085544,0.0000020118055,0.00040343523,0.00013984065,0.000018151575,0.00025429297,0.9113487,0.017550932],"study_design_scores_gemma":[0.7857408,0.000067920024,0.0011834322,0.0017163934,0.000011852106,0.000001206715,0.00007626814,0.19606154,0.00004053431,0.00049639645,0.014434412,0.00016926299],"about_ca_topic_score_codex":0.00002022704,"about_ca_topic_score_gemma":0.000020659836,"teacher_disagreement_score":0.8969143,"about_ca_system_score_codex":0.00006523534,"about_ca_system_score_gemma":0.00012616045,"threshold_uncertainty_score":0.7789532},"labels":[],"label_agreement":null},{"id":"W6986098463","doi":"","title":"Opening doors : building your career in Ontario : a guide to finding work and training for the internationally trained","year":2004,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Doors; Training (meteorology); Work (physics); Craft; On-the-job training","score_opus":0.019005081570915294,"score_gpt":0.23018008015093278,"score_spread":0.21117499858001748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986098463","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012400537,0.00013066182,0.008030091,0.0018073552,0.00018855743,0.0009267239,0.000026663998,0.000079866324,0.9886861],"genre_scores_gemma":[0.0025160396,0.000011774136,0.12654738,0.0002714022,0.00007455581,0.00012881274,0.000023246466,0.000038025963,0.87038875],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871373,0.00001842656,0.00049206096,0.00024940178,0.0002698777,0.0002564923],"domain_scores_gemma":[0.9990864,0.00021332648,0.00027040424,0.00026226672,0.00008879557,0.000078808305],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037449156,0.00021784003,0.00024330612,0.00012617825,0.0001256438,0.0001337981,0.0007024836,0.00013667608,0.0050090523],"category_scores_gemma":[0.00009059683,0.00018904913,0.000082996405,0.000026690124,0.000043057018,0.0000010657544,0.00018549182,0.00021680549,0.00008370509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014232356,0.000012627052,0.000010381225,0.00003475874,0.000033431603,0.0000031794848,0.0025093511,0.009258867,3.1496074e-7,0.0022793123,0.9787673,0.0070762876],"study_design_scores_gemma":[0.0004679661,0.000043666754,0.00028094195,0.00032418297,0.000012885961,0.000024763945,0.00017542578,0.00054870633,0.0000010728486,0.00009539812,0.9978113,0.00021365793],"about_ca_topic_score_codex":0.028851835,"about_ca_topic_score_gemma":0.05746696,"teacher_disagreement_score":0.11851729,"about_ca_system_score_codex":0.00019363144,"about_ca_system_score_gemma":0.00014123159,"threshold_uncertainty_score":0.9959005},"labels":[],"label_agreement":null},{"id":"W6986175427","doi":"","title":"Osgoode LLM candidate Veronica Guido receives $5K CELF scholarship to study Anishinaabe law","year":2020,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scholarship; Foundation (evidence); Energy (signal processing); Latin Americans","score_opus":0.025607057600995388,"score_gpt":0.2727552639437389,"score_spread":0.2471482063427435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986175427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6631491,0.0029898244,0.11357556,0.1346724,0.0018273573,0.0061350246,0.00031445242,0.0037349428,0.07360129],"genre_scores_gemma":[0.9393237,0.000027898184,0.05136259,0.008423978,0.00030716357,0.00018089502,0.000014603763,0.000029975332,0.00032916473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99720037,0.00020680077,0.0004546946,0.0010170519,0.00055584084,0.0005652609],"domain_scores_gemma":[0.99760437,0.000087770146,0.0001131896,0.0012148577,0.00015524768,0.0008245774],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045371157,0.00029105114,0.00030842394,0.000026951975,0.00079807045,0.00061871076,0.0022024352,0.00009445392,0.00015443916],"category_scores_gemma":[0.00010535699,0.00029560341,0.00010212793,0.00085387484,0.000080874524,0.0012875764,0.0008446844,0.0005122865,0.0016932673],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035388097,0.0009929034,0.0032112931,0.000022029173,0.00010528021,0.000074310636,0.0016242675,0.0005404301,0.002877033,0.9773485,0.010350002,0.0028185768],"study_design_scores_gemma":[0.0040466553,0.003182217,0.071607,0.00011617168,0.00011159496,0.000049505645,0.0007984378,0.0068589137,0.008914897,0.05438212,0.84749115,0.0024413383],"about_ca_topic_score_codex":0.0013787793,"about_ca_topic_score_gemma":0.0011521049,"teacher_disagreement_score":0.92296636,"about_ca_system_score_codex":0.00011140307,"about_ca_system_score_gemma":0.00014680032,"threshold_uncertainty_score":0.99994963},"labels":[],"label_agreement":null},{"id":"W7002062273","doi":"","title":"L'Organisation d'un club science au Québec /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Club; Work (physics); Agency (philosophy); Government (linguistics); Subject (documents)","score_opus":0.013672864456473397,"score_gpt":0.231958138665434,"score_spread":0.2182852742089606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002062273","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020384265,0.011724459,0.097297534,0.08021941,0.0010594473,0.0016075348,0.0005263811,0.000625552,0.7865554],"genre_scores_gemma":[0.2749503,0.00068632816,0.028273966,0.002900011,0.0021185025,0.0004906426,0.00008136017,0.00037999943,0.6901189],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99219644,0.00034377954,0.0011474271,0.0019378731,0.0033056864,0.0010688016],"domain_scores_gemma":[0.9917642,0.00430645,0.0011328184,0.0015023014,0.0004218597,0.0008723748],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013118016,0.0009849913,0.000703693,0.0017148743,0.0015839909,0.0012180539,0.004050724,0.00023214357,0.009997059],"category_scores_gemma":[0.0030713745,0.0010174235,0.00034072826,0.0033737123,0.0024377322,0.0022193028,0.0020287689,0.0007800143,0.004669582],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00008031103,0.0023015735,0.03155243,0.00023399157,0.00045284943,0.00004922153,0.03062312,0.0011912785,0.0034671666,0.4916558,0.42370403,0.014688223],"study_design_scores_gemma":[0.0009748078,0.00020359957,0.13083315,0.00019963145,0.00009596692,0.00016733841,0.00019504127,0.016782166,0.00043429696,0.00262801,0.84635025,0.0011357429],"about_ca_topic_score_codex":0.65304506,"about_ca_topic_score_gemma":0.98236436,"teacher_disagreement_score":0.4890278,"about_ca_system_score_codex":0.027248545,"about_ca_system_score_gemma":0.3867796,"threshold_uncertainty_score":0.9998188},"labels":[],"label_agreement":null},{"id":"W7010613354","doi":"","title":"Fiscal Year 2018","year":2019,"lang":"en","type":"report","venue":"The Portal to Texas History (University of North Texas)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fiscal year; Quarter (Canadian coin); Commission; State (computer science); Duration (music)","score_opus":0.025989778917925553,"score_gpt":0.21720376371252273,"score_spread":0.19121398479459717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010613354","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02381569,0.002706301,0.10376477,0.00485788,0.0068515074,0.0026044233,0.0006794607,0.0007080079,0.85401195],"genre_scores_gemma":[0.08835831,0.00077633833,0.021733407,0.00041709296,0.00072506035,0.0000027126885,0.00032298392,0.00008236542,0.8875817],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99789083,0.00004415312,0.00024916508,0.0006184633,0.00088768976,0.0003096844],"domain_scores_gemma":[0.9974769,0.00005572683,0.00044146567,0.0015558745,0.000288169,0.00018190504],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003386989,0.0002614111,0.00042031935,0.00022975367,0.00020884631,0.000012033448,0.0026031695,0.00016668999,0.0006642576],"category_scores_gemma":[0.000016304077,0.00026553348,0.00029702508,0.00036373915,0.0002551604,0.00025950916,0.0008154716,0.0004174731,0.0021634896],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000080777945,0.00012174972,0.01380757,0.00005427566,0.000089501315,0.000044845838,0.00020667308,0.00010594841,7.7812075e-7,0.003115761,0.9788912,0.0035536094],"study_design_scores_gemma":[0.00009927264,0.000059899405,0.29321575,0.000025256924,0.000046025787,0.000024550847,0.000010201952,0.00023683625,2.9572595e-7,0.000053785294,0.7059946,0.0002335289],"about_ca_topic_score_codex":0.001345486,"about_ca_topic_score_gemma":0.0010119852,"teacher_disagreement_score":0.2794082,"about_ca_system_score_codex":0.0007379933,"about_ca_system_score_gemma":0.0013604994,"threshold_uncertainty_score":0.9999797},"labels":[],"label_agreement":null},{"id":"W7024220225","doi":"","title":"Rapid evidence profile #41: What do we know from the best-available evidence and from the experiences of other jurisdictions about the functions that local-systems are responsible for and the ways in which they are held accountable for performing these functions?","year":2022,"lang":"en","type":"other","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"McMaster University","keywords":"Empirical evidence; Qualitative research; Compliance (psychology); Context (archaeology); Evidence-based policy","score_opus":0.06182326533319117,"score_gpt":0.27133030949802756,"score_spread":0.2095070441648364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024220225","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017639128,0.74293345,0.20777394,0.029514298,0.0021202331,0.011121409,0.001275059,0.00024871906,0.0032489726],"genre_scores_gemma":[0.14063063,0.3712753,0.01866654,0.003148964,0.0036551838,0.120847106,0.00016314129,0.00077663193,0.3408365],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99672705,0.00060329563,0.0005663923,0.0010365294,0.0006076528,0.00045908187],"domain_scores_gemma":[0.9847746,0.012225716,0.000731636,0.0019358314,0.00026306615,0.00006913149],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026583173,0.00044269138,0.0005205744,0.00013201214,0.002361485,0.0011076648,0.0019754705,0.00020680121,0.0011319364],"category_scores_gemma":[0.00036939833,0.00020386056,0.0001715896,0.00087102957,0.0006337617,0.0014532505,0.0006695107,0.00051408034,0.000024351477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008405011,0.0006691058,0.006155458,0.00039456727,0.0010474182,0.0000014818891,0.129905,0.0024670209,0.00009005801,0.044375993,0.74159724,0.072456166],"study_design_scores_gemma":[0.00061646034,0.00012280137,0.0004056232,0.0028208578,0.00017240043,0.000010663213,0.20107956,0.06188917,0.00002318024,0.0021023084,0.7303876,0.00036934114],"about_ca_topic_score_codex":0.0072481153,"about_ca_topic_score_gemma":0.0076583126,"teacher_disagreement_score":0.37165815,"about_ca_system_score_codex":0.000099353565,"about_ca_system_score_gemma":0.00039778935,"threshold_uncertainty_score":0.9999293},"labels":[],"label_agreement":null},{"id":"W7024889260","doi":"","title":"Traitement de l'islam et du monde musulman à l'école : perceptions des jeunes musulmans(es) du cégep au Québec","year":2008,"lang":"fr","type":"other","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Nucleofection; Gestational period; TSG101; Hyporeflexia; Pretext; Dysgeusia","score_opus":0.04713461931900343,"score_gpt":0.30991081777856067,"score_spread":0.26277619845955724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024889260","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04706486,0.0023207697,0.39831576,0.44795108,0.009312444,0.007216256,0.005611475,0.00034904425,0.08185831],"genre_scores_gemma":[0.26595274,0.00089567114,0.059791926,0.001165687,0.0033170267,0.0035452682,0.0020654814,0.00027694847,0.66298926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9901501,0.00050126965,0.0009119813,0.0020127317,0.0044496404,0.0019742893],"domain_scores_gemma":[0.99296975,0.0008488691,0.00034839177,0.0011650809,0.0036914386,0.00097649044],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.0037370932,0.00070019375,0.0005434296,0.0010702064,0.008205405,0.0011562443,0.0028977587,0.0003206095,0.0014132697],"category_scores_gemma":[0.0011838657,0.00074313075,0.00033261298,0.0026367663,0.0037068275,0.0013626043,0.0007268778,0.0009152213,0.00014462369],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000015577869,0.00067182985,0.001056721,0.0001651422,0.00015675045,0.000048551487,0.0031524573,0.037288867,0.0002842625,0.49938667,0.45335767,0.004415516],"study_design_scores_gemma":[0.00061113946,0.000094899544,0.02487349,0.00019687312,0.000023127684,0.000103556085,0.0008197085,0.114059635,0.000050771618,0.0025113462,0.8560015,0.0006539475],"about_ca_topic_score_codex":0.9403929,"about_ca_topic_score_gemma":0.99968666,"teacher_disagreement_score":0.5811309,"about_ca_system_score_codex":0.010412789,"about_ca_system_score_gemma":0.08970844,"threshold_uncertainty_score":0.9998807},"labels":[],"label_agreement":null},{"id":"W7027379868","doi":"","title":"Colliers Brokers Sale of New 1 MSF Industrial Building in Metro Louisville Leased to Canadian Solar","year":2024,"lang":"en","type":"other","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Metropolitan area","score_opus":0.024248697762346786,"score_gpt":0.25784678657058285,"score_spread":0.23359808880823607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027379868","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013455738,0.0013501977,0.26099622,0.010488819,0.0017660084,0.0015499041,0.00026797946,0.00037052634,0.72307575],"genre_scores_gemma":[0.002124176,0.000020708756,0.21153162,0.00056096056,0.00054616906,0.000051568026,0.000019462199,0.00016162811,0.7849837],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987811,0.000020148571,0.00023092722,0.00045242792,0.00020902693,0.00030638726],"domain_scores_gemma":[0.9990622,0.000023209423,0.000060809554,0.0004437017,0.000021661186,0.00038843259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013551027,0.00016994776,0.00024361648,0.000958428,0.000030507243,0.00005742248,0.0007226187,0.00023182618,0.00059883355],"category_scores_gemma":[0.000026114416,0.00017417147,0.00006892874,0.0013869356,0.000024038472,0.000055892928,0.00012001767,0.00022213063,0.00027917602],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.6880457e-7,0.000011873006,0.0000901438,0.000008816301,0.000023500954,0.0000071008158,0.000054932527,0.00007873921,0.000014317298,0.015094016,0.98115337,0.0034624184],"study_design_scores_gemma":[0.00023528033,0.000028301964,0.000031221676,0.00012475188,0.000009061355,0.0000013265733,0.000059305414,0.002617782,0.000049989292,0.0007130944,0.99591094,0.00021896836],"about_ca_topic_score_codex":0.4526529,"about_ca_topic_score_gemma":0.38372543,"teacher_disagreement_score":0.06892747,"about_ca_system_score_codex":0.0003227936,"about_ca_system_score_gemma":0.0015330857,"threshold_uncertainty_score":0.7102506},"labels":[],"label_agreement":null},{"id":"W7028641559","doi":"","title":"Féminisation des titres et des textes /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rickettsiales; Ethnic community; Derogation","score_opus":0.022338961720341034,"score_gpt":0.2493713284822736,"score_spread":0.22703236676193259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028641559","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01179129,0.023728946,0.084397726,0.021366257,0.0002348085,0.00083758525,0.00058093114,0.00038704855,0.8566754],"genre_scores_gemma":[0.059606105,0.0030871932,0.06447613,0.0018172753,0.00087321363,0.0004754879,0.00012842727,0.00027394271,0.8692622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954933,0.0004749807,0.0008039522,0.0011202688,0.0014820942,0.00062540226],"domain_scores_gemma":[0.991715,0.006035102,0.00069826964,0.0007960142,0.00030957448,0.00044603818],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006711847,0.0007589914,0.0005364303,0.00095761684,0.0006769857,0.00096100575,0.0017291519,0.00018863996,0.008166137],"category_scores_gemma":[0.0026616934,0.0007623596,0.00026824884,0.0015262119,0.001543286,0.0016309823,0.00095499575,0.000515536,0.0022881427],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00004743898,0.0012919954,0.023730999,0.00027927462,0.000365708,0.00002332508,0.015809571,0.0013736075,0.0005033042,0.49150136,0.45491654,0.0101568615],"study_design_scores_gemma":[0.0006905796,0.00022429196,0.15062374,0.00043635923,0.0000652773,0.000105716594,0.0001717188,0.010266702,0.00007667987,0.02052599,0.81601787,0.0007950785],"about_ca_topic_score_codex":0.08505954,"about_ca_topic_score_gemma":0.8932636,"teacher_disagreement_score":0.80820405,"about_ca_system_score_codex":0.004972499,"about_ca_system_score_gemma":0.05162914,"threshold_uncertainty_score":0.99948275},"labels":[],"label_agreement":null},{"id":"W7068997215","doi":"","title":"Page 105","year":2011,"lang":"en","type":"article","venue":"Pittsburg State University Digital Commons (Pittsburg State University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ledger; Period (music); Register (sociolinguistics); Quarter (Canadian coin); FLAGS register","score_opus":0.01947301086159729,"score_gpt":0.18423680498057007,"score_spread":0.1647637941189728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7068997215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10960769,0.00006242224,0.7663672,0.00059490046,0.00029744508,0.0006096121,0.00045167087,0.0014050317,0.12060405],"genre_scores_gemma":[0.96219903,0.00026563235,0.0067294203,0.00010382804,0.00003494559,8.494809e-7,0.00007533342,0.000047721558,0.030543214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970034,0.0001392031,0.00033068523,0.0010832143,0.0004883657,0.0009551315],"domain_scores_gemma":[0.9971197,0.00017601466,0.0002877878,0.0013909675,0.0003675589,0.00065795274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001465196,0.00051391666,0.00044387975,0.0010213859,0.0009841141,0.0002536089,0.0029915026,0.00013071888,0.00009262442],"category_scores_gemma":[0.000019592957,0.00063933025,0.00033549368,0.0024987664,0.00048212725,0.0045876214,0.0017116359,0.00048491653,0.0002920106],"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":[0.0007007111,0.004465156,0.026683643,0.00013224165,0.0009749822,0.008513584,0.013994796,0.0007566095,0.0006905035,0.8222994,0.025944684,0.09484364],"study_design_scores_gemma":[0.0036367404,0.000662298,0.03459836,0.00007449356,0.00014888923,0.00012459066,0.0038659682,0.004930357,0.00040798768,0.062347006,0.88655424,0.0026490805],"about_ca_topic_score_codex":0.00039347925,"about_ca_topic_score_gemma":0.00007073895,"teacher_disagreement_score":0.86060953,"about_ca_system_score_codex":0.00046981184,"about_ca_system_score_gemma":0.00028344206,"threshold_uncertainty_score":0.9996058},"labels":[],"label_agreement":null},{"id":"W7089663828","doi":"10.1007/978-3-032-06112-6_13","title":"Primary Graft Dysfunction","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"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":"Hypoxemia; ARDS; Pulmonary edema; Extracorporeal membrane oxygenation; Lung; Extracorporeal","score_opus":0.01012619801359335,"score_gpt":0.20809581139230685,"score_spread":0.1979696133787135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7089663828","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.1830208e-8,0.0001843259,0.4419909,0.000988816,0.00019311035,0.00009187173,0.00000434689,0.00017606054,0.5563705],"genre_scores_gemma":[0.000007930322,0.00012257183,0.06307771,0.00081633753,0.00013086777,0.000020612126,0.00005071197,0.0000069466114,0.93576634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999127,0.0000035944292,0.00017791917,0.00039689842,0.000184527,0.00011006766],"domain_scores_gemma":[0.9991374,0.000041815198,0.000065839624,0.0006272759,0.000082628605,0.000045042918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057929567,0.00015655214,0.00014097674,0.00011742016,0.00012527218,0.00005243156,0.00051559566,0.0001530374,0.0002094073],"category_scores_gemma":[0.0000018261039,0.00014731447,0.00009865554,0.00005410577,0.000029363378,0.00015618735,0.00027327525,0.00018283019,0.00046904746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.6082085e-7,0.000007699799,4.532735e-7,0.000008564734,0.000011779919,0.0000012763496,0.0000024985202,0.0000025607953,0.0000033643548,0.85786784,0.09696386,0.04512983],"study_design_scores_gemma":[0.000054035565,0.00001306125,0.00006556262,0.00002477372,0.000008954412,0.0000056172266,2.5303427e-7,0.0021334186,0.0000040893588,0.19268166,0.80486256,0.00014599312],"about_ca_topic_score_codex":0.0000068383906,"about_ca_topic_score_gemma":0.0000025865816,"teacher_disagreement_score":0.70789874,"about_ca_system_score_codex":0.00008296023,"about_ca_system_score_gemma":0.00015731445,"threshold_uncertainty_score":0.60288125},"labels":[],"label_agreement":null},{"id":"W7096493495","doi":"","title":"Recommended C Style and Coding Standards","year":2000,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coding (social sciences); Style (visual arts); Scope (computer science); Miller; Set (abstract data type)","score_opus":0.011820763784610253,"score_gpt":0.2591402804207473,"score_spread":0.24731951663613708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096493495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0103723295,0.00011421575,0.844327,0.009298617,0.000038763705,0.00009694152,0.000009778122,0.0002297123,0.13551265],"genre_scores_gemma":[0.6687018,0.00030758735,0.31889716,0.00097506086,0.000060371316,0.00002213202,0.0000027438116,0.000004794848,0.011028326],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996294,0.000008615069,0.000060316106,0.00013054488,0.000089318295,0.00008184189],"domain_scores_gemma":[0.99975747,0.000021305987,0.000007807505,0.00014901676,0.00002275361,0.000041622414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010923867,0.000035072793,0.000037011818,0.000014321482,0.00012558675,0.000054612654,0.00013464136,0.000012874008,0.0005147401],"category_scores_gemma":[0.00000224279,0.00003061847,0.000009451439,0.000103543476,0.000011397928,0.00018878879,0.000035963334,0.00003119637,0.000026158807],"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":[0.0000011020276,0.00003212226,0.00007920837,0.0000019711188,0.000004136239,0.0000010120721,0.000161636,0.000010839902,0.00009773928,0.41002598,0.020205481,0.56937873],"study_design_scores_gemma":[0.0003923211,0.000053810007,0.004445134,0.0000075702164,0.000002194573,0.000033595446,0.000042278054,0.18761541,0.00040537745,0.0251526,0.7816586,0.00019112225],"about_ca_topic_score_codex":0.000011945322,"about_ca_topic_score_gemma":0.0000025759527,"teacher_disagreement_score":0.7614531,"about_ca_system_score_codex":0.000015351929,"about_ca_system_score_gemma":0.000018836721,"threshold_uncertainty_score":0.56360435},"labels":[],"label_agreement":null},{"id":"W7100583189","doi":"","title":"Economic evaluation of Avonex (interferon beta-la) in patients following a single demyelinating event,” Multiple Sclerosis","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multiple sclerosis; Christian ministry; Economic evaluation; Cost–benefit analysis; Quality-adjusted life year; Multivariate analysis; Outcome (game theory); Economic analysis; Cost effectiveness","score_opus":0.04628665552023966,"score_gpt":0.27189510585764637,"score_spread":0.2256084503374067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100583189","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9004735,0.000041337855,0.09750133,0.00031569842,0.00006825057,0.00025137002,0.0000023747248,0.000035613968,0.0013105541],"genre_scores_gemma":[0.9447362,0.0000015692807,0.055121675,0.000025359896,0.000028925095,0.000045585923,0.0000066833436,0.000005534318,0.000028461653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888235,0.000091118025,0.00036471745,0.00026399794,0.00024735185,0.00015046529],"domain_scores_gemma":[0.9994115,0.00011915108,0.000119500924,0.00024343049,0.000071607436,0.000034785626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076090446,0.000084923704,0.00011808742,0.0001082841,0.00006712115,0.000033045668,0.00030524185,0.00004096901,0.000024812574],"category_scores_gemma":[0.000040466795,0.00008872695,0.00007691861,0.00015386232,0.0000142613635,0.0005946651,0.000126239,0.000058768906,0.00003321175],"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":[0.000010683208,0.0018059926,0.15129107,0.000013044981,0.000056111563,1.6124932e-7,0.0014999323,0.10179469,0.006808893,0.009820211,0.00039179582,0.7265074],"study_design_scores_gemma":[0.0010193748,0.000055752425,0.17037791,0.000033714416,0.00000898236,1.8007056e-7,0.000024552062,0.8250374,0.0028528674,0.00041957074,0.00007151492,0.00009817559],"about_ca_topic_score_codex":0.000074464646,"about_ca_topic_score_gemma":0.00015607876,"teacher_disagreement_score":0.72640926,"about_ca_system_score_codex":0.00030335836,"about_ca_system_score_gemma":0.000055737783,"threshold_uncertainty_score":0.36181802},"labels":[],"label_agreement":null},{"id":"W7100933117","doi":"","title":"Evolvable View Environment","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"IBM; General partnership; Architecture; Work (physics); Information system","score_opus":0.011182285998384393,"score_gpt":0.22786989263902174,"score_spread":0.21668760664063735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100933117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033358904,0.00017374406,0.9546736,0.0010691384,0.000032272455,0.00005342536,1.8044862e-7,0.00007635896,0.04358771],"genre_scores_gemma":[0.13466707,0.00006453418,0.85625666,0.00065750605,0.000064102765,0.000013247641,0.0000014897408,0.0000032772098,0.008272087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995536,0.0000033264516,0.000082240666,0.00013624017,0.00009833366,0.00012621249],"domain_scores_gemma":[0.9996349,0.000021627642,0.000013662601,0.0002716571,0.0000068882046,0.000051247196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016918313,0.000036703215,0.000032370303,0.000018736493,0.00007719329,0.000016637901,0.0002602021,0.000014097492,0.00017869723],"category_scores_gemma":[0.0000010630009,0.000031259224,0.000018139388,0.000101083315,0.000013012848,0.00014227898,0.00008481709,0.00002847559,0.00077345525],"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":[2.073137e-7,0.00008393647,0.00016903314,0.0000013579868,0.0000028414786,0.0000022104555,0.000032881315,0.00007442334,0.00048279043,0.9011442,0.0049086576,0.09309743],"study_design_scores_gemma":[0.00010733736,0.000026667818,0.016456762,0.0000023137022,0.0000014185462,0.000013132659,0.0000134085685,0.03439393,0.0016932561,0.021090593,0.92606694,0.00013426205],"about_ca_topic_score_codex":0.000010213138,"about_ca_topic_score_gemma":0.0000011474191,"teacher_disagreement_score":0.92115825,"about_ca_system_score_codex":0.000023080529,"about_ca_system_score_gemma":0.000008095056,"threshold_uncertainty_score":0.99414605},"labels":[],"label_agreement":null},{"id":"W7113380925","doi":"","title":"A Possible Canvasback x Ruddy Duck Hybrid","year":2025,"lang":"","type":"article","venue":"Digital Commons - University of South Florida (University of South Florida)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"","score_opus":0.013988400727369963,"score_gpt":0.19176470921194777,"score_spread":0.17777630848457782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113380925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6392161,0.00059769966,0.30454487,0.0024336658,0.0014413641,0.0011545506,0.0051315646,0.0003510481,0.045129094],"genre_scores_gemma":[0.9784998,0.000104830026,0.005477393,0.00004236854,0.0001153366,2.8463353e-7,0.00016787527,0.0000318903,0.015560183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99539286,0.00014701141,0.0006523649,0.0016102747,0.0010890752,0.0011084359],"domain_scores_gemma":[0.9946493,0.0002696877,0.0009879088,0.0022703651,0.0011576307,0.0006651248],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00037452223,0.0007944079,0.0013266549,0.0012348017,0.002216614,0.00024929803,0.004708437,0.00040726928,0.0003084413],"category_scores_gemma":[0.00006089159,0.0012044058,0.0010729076,0.003423812,0.0023506808,0.0028560814,0.0034168747,0.00090056343,0.00033364072],"study_design_candidate":"qualitative","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":[0.0052039693,0.0070466413,0.24541382,0.0039620856,0.010784295,0.0023010918,0.22660646,0.013750533,0.0006107895,0.33959928,0.057939786,0.08678124],"study_design_scores_gemma":[0.027790675,0.0029057167,0.22162531,0.0042896345,0.0045516593,0.00014065055,0.3814254,0.20140691,0.0009502287,0.014855605,0.131363,0.008695199],"about_ca_topic_score_codex":0.0014139079,"about_ca_topic_score_gemma":0.00023034445,"teacher_disagreement_score":0.3392837,"about_ca_system_score_codex":0.00044968713,"about_ca_system_score_gemma":0.0016255423,"threshold_uncertainty_score":0.9990824},"labels":[],"label_agreement":null},{"id":"W72577311","doi":"10.4018/978-1-60566-717-1.ch009","title":"An Integrated Framework for Fuzzy Classification and Analysis of Gene Expression Data","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Classifier (UML); Gene selection; Fuzzy logic; Correlation; Artificial intelligence; Computer science; Fuzzy rule; Gene; Feature selection; Machine learning; Data mining; Pattern recognition (psychology); Mathematics; Fuzzy set; Biology; Gene expression; Genetics","score_opus":0.04428926122515509,"score_gpt":0.3067072035141715,"score_spread":0.26241794228901644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W72577311","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009851839,0.00018887554,0.96861047,0.00008182571,0.000114685325,0.00032848134,0.0019975044,0.00008195709,0.028497664],"genre_scores_gemma":[0.02420169,0.000016259704,0.9735311,0.00007237622,0.00014480852,0.000034867942,0.0007395667,0.000015645031,0.0012436748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985272,0.000013927954,0.00034616143,0.00075168966,0.00021978059,0.00014118323],"domain_scores_gemma":[0.9970935,0.00008259755,0.00034028216,0.0021413036,0.00022407474,0.00011824698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016490229,0.00020940152,0.0003362091,0.00012849862,0.0001410718,0.000084372856,0.0013849954,0.00042699833,0.0000050870553],"category_scores_gemma":[0.000022056876,0.00019203208,0.00009596142,0.00009735797,0.000112505506,0.00016333177,0.0002458729,0.00022453928,0.00000264022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057227394,0.000015698246,0.000026558755,0.0000071386326,0.00011825989,4.0251064e-7,0.00002289953,0.0000073655924,0.001576218,0.97790605,0.00010308953,0.020210624],"study_design_scores_gemma":[0.0001367728,0.00007197394,0.0012464662,0.000069427035,0.0004852668,0.0000046563932,0.000009518394,0.08180842,0.0003849719,0.89956415,0.01587772,0.00034068566],"about_ca_topic_score_codex":0.000026129968,"about_ca_topic_score_gemma":0.000033504293,"teacher_disagreement_score":0.08180106,"about_ca_system_score_codex":0.000036715308,"about_ca_system_score_gemma":0.00012482819,"threshold_uncertainty_score":0.78308403},"labels":[],"label_agreement":null},{"id":"W75137543","doi":"","title":"THE UPHILL BATTLE OF ANT PROGRAMMING VS. GENETIC PROGRAMMING","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic programming; Symbolic regression; Ant colony optimization algorithms; Computer science; Swarm intelligence; Artificial intelligence; Ant colony; Machine learning; Particle swarm optimization","score_opus":0.010657418470338466,"score_gpt":0.23518321485049173,"score_spread":0.22452579638015327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75137543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074838162,0.00022654116,0.98492604,0.006391949,0.00011848568,0.00025040895,9.611933e-7,0.0001231568,0.0004786462],"genre_scores_gemma":[0.5526123,0.000057627934,0.4463276,0.000030421701,0.000066152854,0.00010578181,3.2196328e-7,0.000005411634,0.00079440576],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923927,0.000018530778,0.00017973504,0.00019053124,0.00015955075,0.00021236291],"domain_scores_gemma":[0.99916714,0.00019557739,0.00006593879,0.0004404312,0.00008160642,0.000049331375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015757159,0.00006426065,0.00006279656,0.000021834578,0.0002005316,0.000050787396,0.00058064365,0.000021233445,0.000007779829],"category_scores_gemma":[0.000027512564,0.00003088264,0.000043801527,0.00022760338,0.00010380083,0.00013308143,0.00015526425,0.000029254825,0.000030752766],"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":[6.40383e-7,0.000042330335,0.00076918246,0.0000023913167,0.0000056329513,5.980495e-7,0.000038914844,0.0000020577966,0.00045454493,0.1339692,0.0003022891,0.8644122],"study_design_scores_gemma":[0.0005276111,0.00029348553,0.048404295,0.00005696526,0.0000109573975,0.000060988838,0.000083985266,0.012503608,0.0051617045,0.017920101,0.9146185,0.0003578132],"about_ca_topic_score_codex":0.000020655589,"about_ca_topic_score_gemma":0.000009123821,"teacher_disagreement_score":0.9143162,"about_ca_system_score_codex":0.000015949967,"about_ca_system_score_gemma":0.00004138414,"threshold_uncertainty_score":0.15423472},"labels":[],"label_agreement":null},{"id":"W76294416","doi":"","title":"Influence of clustering pre-processing on genetically generated fuzzy knowledge bases","year":2005,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Cluster analysis; Computer science; Fuzzy clustering; Data mining; Outlier; CURE data clustering algorithm; Data stream clustering; Artificial intelligence; Correlation clustering; Canopy clustering algorithm; Noise (video); Fuzzy logic; Machine learning; Process (computing); Pattern recognition (psychology)","score_opus":0.01094977893640601,"score_gpt":0.24728039377079425,"score_spread":0.23633061483438825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W76294416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27830938,0.0008704802,0.71761364,0.0018291586,0.00001909951,0.00028066183,0.000009797888,0.00039618168,0.0006716156],"genre_scores_gemma":[0.67341787,0.00006587706,0.32528278,0.00069960475,0.00009995792,0.00018293782,0.0000042759234,0.000018436096,0.00022825481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818754,0.00007614723,0.0004904623,0.00049552886,0.00028093872,0.00046937686],"domain_scores_gemma":[0.9984099,0.00009305318,0.00019173777,0.00084605365,0.00024513504,0.00021410958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032218316,0.00024461056,0.0002396815,0.00027771166,0.00027368037,0.00012443737,0.0010087413,0.00014469516,0.000005907792],"category_scores_gemma":[0.00006651917,0.0002445536,0.000086805754,0.0008169385,0.00009646724,0.00053161336,0.00035567352,0.00024186922,0.000022838569],"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":[0.000026491924,0.00069791416,0.0017976001,0.000051596693,0.000021469508,0.0000073125266,0.00044724834,0.6488139,0.054054625,0.0721641,0.00069821026,0.22121954],"study_design_scores_gemma":[0.00022416975,0.00011764898,0.046108007,0.00007807606,0.000008301477,0.000037550544,0.0000068414774,0.9239598,0.025993245,0.0011199419,0.0020740386,0.00027237865],"about_ca_topic_score_codex":0.0004451957,"about_ca_topic_score_gemma":0.0005558312,"teacher_disagreement_score":0.3951085,"about_ca_system_score_codex":0.0002063279,"about_ca_system_score_gemma":0.00028241996,"threshold_uncertainty_score":0.99726075},"labels":[],"label_agreement":null},{"id":"W86880533","doi":"10.1007/978-3-642-37192-9_44","title":"CodeMonkey; a GUI Driven Platform for Swift Synthesis of Evolutionary Algorithms in Java","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Java; Swift; Eclipse; Programming language; Plug-in; Evolutionary algorithm; Architecture; Software engineering; Code (set theory); Simple (philosophy); Operating system; Software; Source code; Artificial intelligence; Set (abstract data type)","score_opus":0.01862146126745619,"score_gpt":0.245575392731098,"score_spread":0.2269539314636418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W86880533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008986468,0.0004291244,0.99495596,0.0009220618,0.000530051,0.0010371581,0.000053905525,0.00008528804,0.0018965631],"genre_scores_gemma":[0.03709775,0.00008325886,0.9616051,0.00026968258,0.00030677844,0.00023242368,0.000015141144,0.000034287474,0.00035559718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636394,0.000018930099,0.00083207875,0.0013580874,0.0007765968,0.0006503767],"domain_scores_gemma":[0.9965527,0.0012099956,0.00041687436,0.001252589,0.00041478509,0.00015302618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049786014,0.0004677295,0.0006497763,0.0009226225,0.00023477421,0.00012799683,0.0031317812,0.0003657872,0.000029347973],"category_scores_gemma":[0.00012424657,0.00044512062,0.00019706915,0.00075772847,0.0006815227,0.0007870048,0.0009402452,0.0004762882,0.000037133173],"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":[0.000008567984,0.00017739863,0.00013301983,0.00010802323,0.000026605723,0.000015560128,0.00048618912,0.04680672,0.00018466526,0.1611338,0.00035386172,0.7905656],"study_design_scores_gemma":[0.00022922909,0.00010656734,0.00069557293,0.00032824927,0.000007517775,0.000029666277,4.4168135e-7,0.797232,0.00046063968,0.19841239,0.0020599598,0.000437769],"about_ca_topic_score_codex":0.00009943001,"about_ca_topic_score_gemma":0.000071379254,"teacher_disagreement_score":0.7901278,"about_ca_system_score_codex":0.00038772743,"about_ca_system_score_gemma":0.000697356,"threshold_uncertainty_score":0.9998},"labels":[],"label_agreement":null},{"id":"W93303570","doi":"10.1007/978-3-642-37207-0_10","title":"On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Evolvability; Computer science; Representation (politics); Genetic programming; Rank (graph theory); Formicoidea; Ant colony; Pheromone; Ant colony optimization algorithms; Theoretical computer science; Artificial intelligence; Mathematics; Biology; Ecology; Evolutionary biology; Aculeata; Combinatorics","score_opus":0.03587807863154491,"score_gpt":0.2736483658721608,"score_spread":0.23777028724061589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W93303570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008894966,0.00025219517,0.99435353,0.002404374,0.00043685446,0.00087295636,0.0000054752363,0.00006966546,0.0007154707],"genre_scores_gemma":[0.13465919,0.000014376144,0.864331,0.0006457711,0.00014989455,0.00007633078,0.0000019582917,0.000015182381,0.00010627683],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970964,0.0001317487,0.0005179551,0.0011415752,0.00063573295,0.0004765765],"domain_scores_gemma":[0.99526495,0.0022764783,0.00033684727,0.0017164227,0.00029839866,0.00010687923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012476828,0.00034137815,0.00043078,0.00028742908,0.0002943257,0.00015732944,0.003062032,0.0001281653,0.000044557433],"category_scores_gemma":[0.00020741805,0.00024125817,0.00013888738,0.000489071,0.0012717424,0.00017441652,0.00082806917,0.0005888339,0.000040525774],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029779906,0.00009095918,0.00003901456,0.00002901149,0.000015025364,0.000015573718,0.00035379652,0.014024342,0.00016474452,0.22889179,0.0000849637,0.7562878],"study_design_scores_gemma":[0.00008967267,0.00027086266,0.00067124364,0.00009869084,0.000007075862,0.000067086985,2.2449775e-7,0.48326066,0.0007455297,0.51341254,0.0010894835,0.00028694017],"about_ca_topic_score_codex":0.000056865316,"about_ca_topic_score_gemma":0.00001773264,"teacher_disagreement_score":0.7560009,"about_ca_system_score_codex":0.0001663566,"about_ca_system_score_gemma":0.00042853382,"threshold_uncertainty_score":0.9838222},"labels":[],"label_agreement":null},{"id":"W94609200","doi":"10.1007/978-0-387-87623-8_4","title":"Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study","year":2008,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","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":"Dalhousie University","funders":"","keywords":"Benchmarking; Genetic programming; Benchmark (surveying); Computer science; Decomposition; Mathematical optimization; Domain (mathematical analysis); Pareto principle; Coevolution; Genetic algorithm; Operator (biology); Artificial intelligence; Multi-objective optimization; Machine learning; Mathematics; Economics; Ecology; Chemistry; Biology","score_opus":0.025754256119045946,"score_gpt":0.24764441517735955,"score_spread":0.2218901590583136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94609200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037268782,0.005911079,0.9579446,0.00052276714,0.00048652344,0.0028754787,0.00004352827,0.00041891437,0.02807023],"genre_scores_gemma":[0.4532425,0.0027075876,0.5177299,0.00019138696,0.0011918738,0.00067779265,0.00065856735,0.0001239873,0.0234764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970877,0.00009924944,0.0006869617,0.001187938,0.00059505895,0.00034308314],"domain_scores_gemma":[0.9983055,0.0001378617,0.00039220793,0.00049368787,0.00048516007,0.00018560742],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000121444675,0.0004920693,0.00039708227,0.0002866307,0.0008825274,0.00014273186,0.00044305567,0.00023381901,0.00002797829],"category_scores_gemma":[0.000009347585,0.0005354627,0.00010903048,0.00020799748,0.00025716654,0.0002479559,0.0002885104,0.00033773965,0.00009860578],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046516583,0.0013934327,0.003912098,0.00011671342,0.00065705914,0.00022436901,0.0052899723,0.021056479,0.000038382754,0.22869617,0.014703779,0.72386503],"study_design_scores_gemma":[0.0011476724,0.0013348752,0.4135099,0.00014395193,0.00015781286,0.00060817203,0.0002901481,0.47620377,9.653526e-7,0.019638693,0.08555716,0.0014068664],"about_ca_topic_score_codex":0.00002069777,"about_ca_topic_score_gemma":0.000012710863,"teacher_disagreement_score":0.7224582,"about_ca_system_score_codex":0.00021305033,"about_ca_system_score_gemma":0.00033292896,"threshold_uncertainty_score":0.99970967},"labels":[],"label_agreement":null},{"id":"W94828587","doi":"","title":"Swarms of robots based on evolutionary game theory","year":2007,"lang":"en","type":"article","venue":"International Conference on Control Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robot; Evolutionary robotics; Computer science; Artificial intelligence; Reinforcement learning; Set (abstract data type); Process (computing); Personality; Swarm robotics; Machine learning; Human–computer interaction; Psychology; Social psychology","score_opus":0.018669401211987152,"score_gpt":0.2860211424759233,"score_spread":0.2673517412639361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94828587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019548192,0.000029846495,0.93631774,0.006586722,0.00015668418,0.00051606185,0.00008620704,0.00014120186,0.05597003],"genre_scores_gemma":[0.9744723,0.000010759491,0.02318337,0.0011922894,0.00016934423,0.00044434113,0.00005304426,0.000011342637,0.00046321997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813473,0.000053892087,0.00046282745,0.00049567857,0.00060078356,0.00025211106],"domain_scores_gemma":[0.99764,0.0006401815,0.00025199555,0.0007480364,0.00059097883,0.0001288198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054108107,0.00018631389,0.0001795372,0.00031159978,0.00014348539,0.000051717387,0.0013656891,0.00008874244,0.00013320475],"category_scores_gemma":[0.000052035066,0.0001791334,0.00012894788,0.00038863308,0.00014311905,0.00020045368,0.00006489496,0.00021093806,0.00022712578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053390944,0.00046955765,0.00017570384,0.000002640319,0.00002805873,0.0000013174464,0.00002553706,0.005891186,0.0010356082,0.97151834,0.000247933,0.020550745],"study_design_scores_gemma":[0.0015405556,0.00021218741,0.027414296,0.00005022511,0.000016872222,0.000008636861,0.00004418773,0.70803714,0.0008968195,0.24583325,0.015597069,0.0003487627],"about_ca_topic_score_codex":0.00001012584,"about_ca_topic_score_gemma":0.000004336995,"teacher_disagreement_score":0.9742768,"about_ca_system_score_codex":0.00012473935,"about_ca_system_score_gemma":0.00018763491,"threshold_uncertainty_score":0.73048484},"labels":[],"label_agreement":null},{"id":"W961119122","doi":"10.1007/978-3-642-33902-8_13","title":"Mechanisms for Complex Systems Engineering Through Artificial Development","year":2012,"lang":"en","type":"book-chapter","venue":"Understanding complex systems","topic":"Evolutionary Algorithms and Applications","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":"Memorial University of Newfoundland","funders":"","keywords":"Evolvability; Computer science; Function (biology); Sensitivity (control systems); Artificial intelligence; Development (topology); Space (punctuation); Control engineering; Engineering; Mathematics","score_opus":0.20738270474169884,"score_gpt":0.2742188261621337,"score_spread":0.06683612142043488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W961119122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.743964e-7,0.0010802271,0.9646808,0.00017532513,0.0024550154,0.0018888417,0.00012417491,0.00057176687,0.029023157],"genre_scores_gemma":[0.25881705,0.00008482104,0.64635813,0.00016458647,0.0039677615,0.0015154611,0.0014535219,0.0005461186,0.087092556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966015,0.000028071221,0.0010596401,0.000836581,0.0007088189,0.00076539133],"domain_scores_gemma":[0.99795645,0.00023787365,0.00055849575,0.00085372024,0.00017468433,0.00021876788],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054639555,0.00064492016,0.0008090997,0.0002724783,0.00084059517,0.0005481682,0.0010599782,0.00035801425,0.000033748704],"category_scores_gemma":[0.000008309576,0.0006823873,0.00021816591,0.0001246655,0.00006273224,0.00036420158,0.0002887106,0.00027949404,0.00014592372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029536961,0.000026930333,1.9643443e-7,0.00033017318,0.00015417508,0.0000027411668,0.00033597264,0.0022243788,0.00072229747,0.9917769,0.0043148506,0.00010847669],"study_design_scores_gemma":[0.0002695518,0.000055251898,0.0000055243468,0.00046430313,0.000048512262,0.00008988645,0.00022623444,0.33571076,0.0000113741835,0.078361146,0.58380103,0.00095644925],"about_ca_topic_score_codex":0.000038850987,"about_ca_topic_score_gemma":0.000006523706,"teacher_disagreement_score":0.91341573,"about_ca_system_score_codex":0.0016750671,"about_ca_system_score_gemma":0.00018709502,"threshold_uncertainty_score":0.99956274},"labels":[],"label_agreement":null}]}