{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":22,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":22,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"fe1ac6ca7248","filters":{"venue":"Genetic and evolutionary computation"}},"results":[{"id":"W1648953748","doi":"10.1007/978-1-4419-7747-2_14","title":"Evolutionary Art Using Summed Multi-Objective Ranks","year":2010,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Outlier; Superlative; Pareto principle; Computer science; Artificial intelligence; Ranking (information retrieval); Camouflage; Multi-objective optimization; Genetic programming; Mathematics; Mathematical optimization; Machine learning; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.02290664459009226,"gpt":0.240005037729304,"spread":0.2170983931392117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001182415,0.0003875432,0.0003768697,0.000315501,0.0006918121,0.00004420959,0.0003303793,0.0006406276,0.00003706649],"category_scores_gemma":[0.00002509587,0.0003985269,0.0001173893,0.0000862244,0.0004751599,0.0001794915,0.0004214874,0.0005993444,0.00009839617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001335433,"about_ca_system_score_gemma":0.0002040456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001663262,"about_ca_topic_score_gemma":0.00002251071,"domain_scores_codex":[0.9982255,0.0000403607,0.0003898868,0.000740168,0.0003094441,0.0002946921],"domain_scores_gemma":[0.9989401,0.0001280232,0.0002414065,0.0003535299,0.0002497012,0.00008721451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002140895,0.0006084886,0.006279061,0.0004604584,0.002356912,0.0004430293,0.006131866,0.01553509,0.0009621312,0.7096772,0.06469998,0.1926317],"study_design_scores_gemma":[0.001371655,0.0002178749,0.069397,0.0001602508,0.0001803646,0.0006677681,0.00004281926,0.3572421,0.000009005926,0.4920652,0.07732871,0.00131726],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002473048,0.007251773,0.965661,0.0003857937,0.001454468,0.0005002783,0.00003039545,0.0003619162,0.02188133],"genre_scores_gemma":[0.0733065,0.0009194281,0.8546742,0.0004257045,0.0005997777,0.00004326602,0.0001629646,0.00007308683,0.06979512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.341707,"threshold_uncertainty_score":0.9998466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2914461813","doi":"10.1007/978-3-030-04735-1_3","title":"Emergent Policy Discovery for Visual Reinforcement Learning Through Tangled Program Graphs: A Tutorial","year":2019,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Constructive; Task (project management); Computer science; A priori and a posteriori; Artificial intelligence; Human–computer interaction; Theoretical computer science; Machine learning; Programming language; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01698892175237302,"gpt":0.2783094984559591,"spread":0.2613205767035861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001431369,0.0004443355,0.0003928292,0.0002770448,0.0003669481,0.000254662,0.0003363101,0.0002779633,0.00001393847],"category_scores_gemma":[0.00004177263,0.0004685286,0.0002212037,0.0001237636,0.0001023827,0.0004754616,0.0003562237,0.0002939008,0.00004621684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002597196,"about_ca_system_score_gemma":0.0005075937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002492273,"about_ca_topic_score_gemma":9.306257e-7,"domain_scores_codex":[0.9975078,0.00004239508,0.0006586681,0.0007195154,0.000631308,0.0004403864],"domain_scores_gemma":[0.9987252,0.000132364,0.0005196659,0.0002828731,0.0002457326,0.0000941806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003767365,0.00001965861,0.00003126316,0.0001099752,0.0001291061,0.000002154387,0.0002655052,0.7816744,0.000007365796,0.2020314,0.00152223,0.01416917],"study_design_scores_gemma":[0.001003498,0.001726723,0.0004617901,0.0001313698,0.00008183141,0.00001893657,0.00001811042,0.8821999,0.000002291149,0.04873,0.06502155,0.0006039963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0001359431,0.0006609578,0.9788247,0.0002005368,0.001695807,0.001818796,0.00000445728,0.0002516888,0.01640707],"genre_scores_gemma":[0.133003,0.001948506,0.3609985,0.0004851044,0.003978679,0.0004057387,0.002080097,0.0002498925,0.4968506],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6178263,"threshold_uncertainty_score":0.9997767,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Cube (algebra); Sequence (biology); Computer science; Artificial intelligence; Mathematics; Combinatorics; Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.02631860498806607,"gpt":0.2756210176767801,"spread":0.249302412688714,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0002085923,0.0002836305,0.0001959344,0.000100623,0.00149806,0.0002202311,0.000389417,0.0001945985,0.000009084428],"category_scores_gemma":[0.00001145218,0.0002473562,0.00004272139,0.00007196554,0.0003525573,0.0002833518,0.0003166707,0.0005379678,0.00002493611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004255003,"about_ca_system_score_gemma":0.0001878431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004904152,"about_ca_topic_score_gemma":0.00003331355,"domain_scores_codex":[0.9982737,0.00008327912,0.0003725665,0.0006723295,0.0003709949,0.0002271434],"domain_scores_gemma":[0.9989014,0.0001608113,0.0002226629,0.0003579831,0.0001997408,0.0001574451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005967308,0.0005072851,0.004362176,0.00005682668,0.000263887,0.00009228637,0.003596495,0.0107569,0.0001848476,0.3307362,0.001780221,0.6476032],"study_design_scores_gemma":[0.0007015617,0.00115302,0.1011468,0.00003257446,0.0000786246,0.0004585155,0.0001905194,0.48259,0.000001605271,0.3519651,0.06087181,0.0008098024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09185114,0.009472063,0.8597739,0.003538298,0.001875756,0.003841003,0.00008114074,0.0009320134,0.02863469],"genre_scores_gemma":[0.8775049,0.0007517892,0.09726296,0.0001308631,0.0008619157,0.0001171276,0.0002370731,0.00005943844,0.02307388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7856538,"threshold_uncertainty_score":0.9999979,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01964022500149743,"gpt":0.2356565986755685,"spread":0.2160163736740711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001844472,0.0003039811,0.0003224995,0.0002171071,0.000312242,0.00005824638,0.0003890802,0.0003046557,0.00001269806],"category_scores_gemma":[0.00001137355,0.0003394492,0.00008657636,0.000147097,0.0001629616,0.0001289742,0.0002341066,0.0003068156,0.00002425649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005990756,"about_ca_system_score_gemma":0.0002914743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001366074,"about_ca_topic_score_gemma":0.00003575613,"domain_scores_codex":[0.9981204,0.00004986233,0.0005511162,0.0006396429,0.0003846615,0.0002543647],"domain_scores_gemma":[0.9985116,0.0001446088,0.0003661553,0.0004185444,0.0004201868,0.0001389675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002055274,0.0004272638,0.005053456,0.0004688708,0.0004491333,0.00003787151,0.001525105,0.01765354,0.0002836834,0.1160417,0.001623191,0.8564157],"study_design_scores_gemma":[0.0003918162,0.0001943629,0.4731443,0.00008696298,0.00007820055,0.0001674837,0.000006398906,0.4519212,0.000005867379,0.06378417,0.009575062,0.0006441753],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003337345,0.005834979,0.9848046,0.0001633458,0.000314921,0.0007063888,0.00006216964,0.0002144997,0.004561709],"genre_scores_gemma":[0.1413193,0.0004300884,0.8539044,0.00003621227,0.0001868284,0.00005063043,0.0002421689,0.00004597316,0.003784439],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8557715,"threshold_uncertainty_score":0.9999058,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388134641","doi":"10.1007/978-981-99-3814-8_16","title":"Evolutionary Approaches to Explainable Machine Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; XML; Artificial intelligence; Field (mathematics); Trustworthiness; Machine learning; Data science; World Wide Web; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.09228201504910044,"gpt":0.2384105133005395,"spread":0.1461284982514391,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002665651,0.0004142937,0.0003439544,0.0005426314,0.000645455,0.000141885,0.0004972226,0.0002622355,0.00004052214],"category_scores_gemma":[0.00005217973,0.0004745688,0.0001066545,0.0002714511,0.0001107889,0.0003212121,0.0006741413,0.0003891693,0.001288597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002346459,"about_ca_system_score_gemma":0.0001868389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007712299,"about_ca_topic_score_gemma":0.00001471896,"domain_scores_codex":[0.9973726,0.00007585128,0.0005206594,0.0009920264,0.0005617731,0.0004770912],"domain_scores_gemma":[0.9988016,0.0002130719,0.000199298,0.0003713077,0.0001751204,0.0002395381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002829574,0.00004206491,0.0001589096,0.0001085983,0.00009429832,0.0001000733,0.001005238,0.3936256,0.00001700801,0.5121374,0.009520234,0.0831623],"study_design_scores_gemma":[0.00009951234,0.0002669432,0.002014631,0.00009703125,0.00002641888,0.00009178629,0.00007074422,0.6521093,0.00000943472,0.3050096,0.03961095,0.0005936906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0003167916,0.004007332,0.9279321,0.001961464,0.0007555415,0.0008145651,0.00002381102,0.0007802411,0.06340818],"genre_scores_gemma":[0.07048246,0.001188526,0.2793613,0.0004610626,0.0008808988,0.0001935097,0.0005843546,0.0002281793,0.6466197],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6485708,"threshold_uncertainty_score":0.9997706,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02034067460913992,"gpt":0.2482385159854312,"spread":0.2278978413762913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001788487,0.0003847024,0.0002762566,0.000268536,0.0006189395,0.0002986783,0.000633381,0.0002276222,0.000006170717],"category_scores_gemma":[0.000003868512,0.0003428686,0.0001382277,0.0002013968,0.0002258509,0.0006117367,0.0005330553,0.0003243448,0.00002981269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001562067,"about_ca_system_score_gemma":0.0003328208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001165418,"about_ca_topic_score_gemma":0.000003399384,"domain_scores_codex":[0.9977352,0.00002160968,0.0005695047,0.0008978656,0.0003781858,0.000397617],"domain_scores_gemma":[0.9989045,0.0002732759,0.0001845125,0.0004042446,0.0001184983,0.0001150256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000145465,0.0000591941,0.00002417324,0.00008406134,0.00007836459,0.00001357007,0.0004147412,0.03342266,0.00000276382,0.8695639,0.007308127,0.08901384],"study_design_scores_gemma":[0.0002502802,0.00004485308,0.001370375,0.0001026974,0.00002124433,0.00006736419,0.00003290528,0.6029698,4.466839e-7,0.3678739,0.02700095,0.0002651046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007727089,0.0757587,0.9016662,0.004753434,0.000645826,0.001519613,0.0002079797,0.0003251738,0.01504579],"genre_scores_gemma":[0.02036049,0.003188217,0.7873487,0.0003269183,0.001234896,0.001077361,0.001073527,0.0001911224,0.1851987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5695472,"threshold_uncertainty_score":0.9999023,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.0152799259893713,"gpt":0.2302078721210929,"spread":0.2149279461317216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001308875,0.000272397,0.0002444978,0.0001966924,0.0005993278,0.0001270573,0.0002009069,0.0001655769,0.000006223763],"category_scores_gemma":[0.0000154786,0.0003017295,0.00006919202,0.00009119044,0.0001030023,0.0001627508,0.0002936075,0.0001964246,0.00002693976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006259739,"about_ca_system_score_gemma":0.0000885478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002419838,"about_ca_topic_score_gemma":0.000004206913,"domain_scores_codex":[0.9984697,0.00002203439,0.0003482263,0.0006683008,0.0002048482,0.0002869308],"domain_scores_gemma":[0.9991757,0.0001819223,0.0001818572,0.0001883747,0.0001559324,0.0001161812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002358803,0.00006104503,0.0004148498,0.0003995711,0.0001919125,0.00002050769,0.0006412345,0.0430369,0.00002797695,0.2447814,0.003935791,0.7064652],"study_design_scores_gemma":[0.0002768347,0.0001973485,0.003912561,0.00007336962,0.00004244309,0.00009987203,0.00001708493,0.7701058,5.010848e-7,0.1600108,0.06493355,0.0003297348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002320743,0.006576213,0.988741,0.0004012625,0.000198516,0.0007878048,0.00002861995,0.000300485,0.002733974],"genre_scores_gemma":[0.01583604,0.002326566,0.8384455,0.00008728564,0.0003713396,0.0003861193,0.0003644996,0.00009877856,0.1420839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.727069,"threshold_uncertainty_score":0.9999435,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01903676189982518,"gpt":0.2339239389063061,"spread":0.2148871770064809,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000113297,0.0003284298,0.0002737524,0.0002155455,0.0004916551,0.0002764597,0.0004978769,0.000161827,0.000004327885],"category_scores_gemma":[0.00000797041,0.0002801211,0.0001464224,0.0002836017,0.0002465386,0.0001881824,0.0005042303,0.0002639894,0.00003627635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096891,"about_ca_system_score_gemma":0.0002013482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003405249,"about_ca_topic_score_gemma":0.00001487241,"domain_scores_codex":[0.9981963,0.00002953443,0.0003929795,0.0006423363,0.0003979218,0.0003409258],"domain_scores_gemma":[0.9988669,0.0001256986,0.0001981472,0.0004451782,0.000224272,0.0001397817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003817631,0.00005801131,0.00007018571,0.00009779677,0.00009179433,0.000007281023,0.0003452492,0.02546852,0.00002451795,0.9329478,0.00944238,0.03144272],"study_design_scores_gemma":[0.0002036865,0.0003101273,0.01125787,0.0001116352,0.0001547526,0.0001275325,0.00007369699,0.1412761,0.00000652697,0.7079116,0.1380179,0.0005485985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001756188,0.01172094,0.9724741,0.008109231,0.0005830624,0.001069861,0.00009482993,0.0001707336,0.004021067],"genre_scores_gemma":[0.6698847,0.001129274,0.2053416,0.0002513264,0.0008829744,0.0003080008,0.0002925754,0.0001272298,0.1217823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7671325,"threshold_uncertainty_score":0.9999651,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland; Queen's University","funders":"","keywords":"Genetic programming; Computer science; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.01464243256641924,"gpt":0.2164199084055694,"spread":0.2017774758391501,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009271463,0.0003463295,0.0002946492,0.0001708878,0.0007676611,0.0001378767,0.0003278942,0.0001843837,0.00001587969],"category_scores_gemma":[0.0000148952,0.0003759802,0.0001203505,0.0001068838,0.00009708587,0.0001924076,0.0002450793,0.0002734632,0.00003783003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001309498,"about_ca_system_score_gemma":0.0002129418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001843305,"about_ca_topic_score_gemma":0.000003597191,"domain_scores_codex":[0.9982738,0.0000165314,0.0005181103,0.0005646602,0.0003089361,0.0003179222],"domain_scores_gemma":[0.9989566,0.0001026983,0.0003089478,0.000224835,0.0002178065,0.0001891296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002106799,0.00006081352,0.0001997195,0.0004933114,0.0001659299,0.00001312777,0.001024077,0.06129017,0.000008334251,0.1566649,0.003738097,0.7763205],"study_design_scores_gemma":[0.0003309714,0.0002813669,0.001260786,0.00004488886,0.00004639723,0.00009170317,0.00002706092,0.8510703,9.541642e-7,0.06725822,0.07920465,0.0003827353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001013271,0.003709772,0.9881429,0.001610727,0.0001643279,0.001056104,0.00004384292,0.0002530162,0.004917948],"genre_scores_gemma":[0.01695271,0.0006375014,0.9648375,0.0001982223,0.000401612,0.000224702,0.0007862779,0.00005784298,0.01590365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7897801,"threshold_uncertainty_score":0.9998692,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02846351217099083,"gpt":0.2645522546837153,"spread":0.2360887425127245,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008225774,0.0004275701,0.0003343701,0.0003179151,0.0005298089,0.0001325632,0.0003880348,0.0002145054,0.000008222325],"category_scores_gemma":[0.00003479855,0.000474634,0.00007959262,0.0002519206,0.0001097706,0.0001950417,0.0002233175,0.0002068571,0.00006167081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002192686,"about_ca_system_score_gemma":0.0004559416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002149867,"about_ca_topic_score_gemma":0.000006672009,"domain_scores_codex":[0.9977615,0.00003185271,0.000476288,0.0009506798,0.0004122474,0.0003673813],"domain_scores_gemma":[0.9985925,0.0001379913,0.0002525894,0.0003332658,0.0004551884,0.0002284588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001983177,0.0002248135,0.00003457418,0.000169434,0.0001398134,0.00003151893,0.0008179612,0.3580496,0.00006740869,0.0168178,0.0004183538,0.6232089],"study_design_scores_gemma":[0.0004761556,0.0003885034,0.01754154,0.0004355013,0.0001581154,0.0005158278,0.00003677682,0.873738,0.000004513431,0.1007938,0.00468569,0.00122552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003402317,0.004075922,0.992589,0.0002550119,0.0001452141,0.001071316,0.00002215459,0.000213295,0.001287862],"genre_scores_gemma":[0.02758341,0.00009374412,0.9655688,0.0001526563,0.0003061584,0.00008189928,0.00003872496,0.00004638539,0.0061282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6219833,"threshold_uncertainty_score":0.9997705,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Evolutionary biology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01868392969207755,"gpt":0.2373948302996576,"spread":0.2187109006075801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002193155,0.0005364725,0.0004232565,0.0003577165,0.000909655,0.0001993562,0.0005802175,0.0004527404,0.00008565951],"category_scores_gemma":[0.00001120346,0.0006008471,0.0001841861,0.000194413,0.0002104481,0.0004993273,0.000397972,0.0004493603,0.0002125974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000349533,"about_ca_system_score_gemma":0.0003815986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001286872,"about_ca_topic_score_gemma":0.000005494776,"domain_scores_codex":[0.9969904,0.00005633588,0.0006161775,0.001074823,0.0007220781,0.000540139],"domain_scores_gemma":[0.9982812,0.0001579385,0.0003774465,0.0005295443,0.0003825617,0.0002712857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001100741,0.0001629256,0.0002207628,0.00005392667,0.0002127017,0.00003409889,0.0001548035,0.01682487,0.00002413485,0.8464618,0.09174815,0.04409085],"study_design_scores_gemma":[0.0005898994,0.0001874703,0.05753012,0.0001234018,0.00009951855,0.000270688,0.00004279134,0.3594179,7.068772e-7,0.4809549,0.09990401,0.0008785535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001273096,0.01838306,0.9334397,0.003540352,0.0004560282,0.0005733794,0.0001204266,0.00041941,0.04294037],"genre_scores_gemma":[0.1470933,0.005145748,0.5258928,0.002366142,0.002028002,0.0001367774,0.002244401,0.0001936365,0.3148992],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4075469,"threshold_uncertainty_score":0.9996443,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Genetic programming; Field (mathematics); Computer science; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01080787652637782,"gpt":0.2554978167080033,"spread":0.2446899401816255,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003774612,0.0003207258,0.0002285206,0.0002126421,0.0003722391,0.00025195,0.0002691699,0.0002254267,0.000008531163],"category_scores_gemma":[0.00005082046,0.0003326364,0.00006550107,0.0002134945,0.0002239774,0.0002969799,0.0004192363,0.0003073457,0.0001142611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350533,"about_ca_system_score_gemma":0.0004238526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000795451,"about_ca_topic_score_gemma":9.60643e-7,"domain_scores_codex":[0.9979271,0.0001859505,0.000394124,0.0008555983,0.0003585078,0.0002786872],"domain_scores_gemma":[0.9984829,0.0006825379,0.0001819993,0.0003085987,0.000191353,0.00015264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002023842,0.00009340327,0.00001724215,0.0002501744,0.0001855317,0.00006932826,0.0007376755,0.002438112,0.000009720214,0.3327863,0.04948546,0.6139068],"study_design_scores_gemma":[0.0001971143,0.0001513644,0.005790368,0.0001245849,0.0001713195,0.001454278,0.00005572925,0.117046,2.330893e-7,0.5955183,0.2790115,0.0004791555],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000113829,0.05814559,0.9219067,0.001480607,0.0003950983,0.0006165112,0.00002086857,0.0003020202,0.0170188],"genre_scores_gemma":[0.003123276,0.002973544,0.8929774,0.0004316745,0.0008198312,0.0001731558,0.0001476542,0.00006627119,0.09928724],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6134276,"threshold_uncertainty_score":0.9999126,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01814437794125014,"gpt":0.212850511694543,"spread":0.1947061337532929,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007158755,0.0004108296,0.0003383107,0.000141015,0.0003999088,0.000136437,0.0004237071,0.0001896404,0.00002846652],"category_scores_gemma":[0.00001712255,0.0004055791,0.00008602385,0.0001369237,0.0001711342,0.0003159989,0.0003148109,0.0002997858,0.00007560994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001617032,"about_ca_system_score_gemma":0.0004534148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001029821,"about_ca_topic_score_gemma":0.000004329066,"domain_scores_codex":[0.9978025,0.00002583034,0.0004271886,0.0009850016,0.0004901754,0.0002693115],"domain_scores_gemma":[0.9986144,0.00008539124,0.0002545039,0.0004075681,0.0004161048,0.0002220527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004754038,0.0001495504,0.00003249527,0.0002913194,0.0002338643,0.00005722152,0.0009773824,0.6493883,0.00003276392,0.2913973,0.02764545,0.02974673],"study_design_scores_gemma":[0.0003117291,0.0001424749,0.00140995,0.00009669943,0.00005546844,0.00008676788,0.000007501249,0.8163385,2.82295e-7,0.1792855,0.001851596,0.0004134737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009929301,0.003147937,0.9610981,0.001277671,0.0001140263,0.0007963234,0.0000669596,0.0003094022,0.03309024],"genre_scores_gemma":[0.05580782,0.0002403643,0.9075372,0.000525541,0.0005590477,0.0002048744,0.0004334883,0.0001038623,0.0345878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1669502,"threshold_uncertainty_score":0.9998396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1537403352","doi":"10.1002/9780470973134.ch4","title":"Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial‐Based Shape Deformation","year":2010,"lang":"en","type":"other","venue":"Genetic and evolutionary computation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Segmentation; Deformation (meteorology); Computer science; Image (mathematics); Computer vision; Genetic algorithm; Image segmentation; Algorithm; Pattern recognition (psychology); Machine learning; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01560175839712032,"gpt":0.2669453030105406,"spread":0.2513435446134203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004177314,0.000441998,0.0004391138,0.0006454061,0.0003078375,0.0001821527,0.000450249,0.000514155,0.0001243338],"category_scores_gemma":[0.00008695093,0.0004504603,0.00008558454,0.0003920389,0.0002887776,0.0005355667,0.0003075206,0.000332746,0.0000173617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001519763,"about_ca_system_score_gemma":0.0004416249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000801878,"about_ca_topic_score_gemma":0.00000532656,"domain_scores_codex":[0.9967569,0.0001828838,0.0006995318,0.0008944027,0.001009983,0.0004563031],"domain_scores_gemma":[0.9984058,0.0001741061,0.0003522585,0.000304305,0.0002681292,0.0004954443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005052061,0.0002274583,0.000009981954,0.0005004814,0.00009247186,0.00001155176,0.0006625169,0.0253483,0.000113296,0.0005210622,0.07880748,0.8936549],"study_design_scores_gemma":[0.001467581,0.0001974768,0.0001693695,0.000151391,0.00005123797,0.0001405411,0.00007322038,0.9907428,0.0000575545,0.004883762,0.001581422,0.0004836426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004742176,0.001400313,0.9923682,0.0004245382,0.0003356769,0.001977799,0.00007529925,0.0005040888,0.002866641],"genre_scores_gemma":[0.0002798119,0.0004289957,0.9955016,0.0008644041,0.0002947258,0.0006718966,0.0007687243,0.0001318513,0.00105803],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9653945,"threshold_uncertainty_score":0.9997947,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02575425611904595,"gpt":0.2476444151773596,"spread":0.2218901590583136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001214447,0.0004920693,0.0003970823,0.0002866307,0.0008825274,0.0001427319,0.0004430557,0.000233819,0.00002797829],"category_scores_gemma":[0.000009347585,0.0005354627,0.0001090305,0.0002079975,0.0002571665,0.0002479559,0.0002885104,0.0003377396,0.00009860578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002130503,"about_ca_system_score_gemma":0.000332929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002069777,"about_ca_topic_score_gemma":0.00001271086,"domain_scores_codex":[0.9970877,0.00009924944,0.0006869617,0.001187938,0.000595059,0.0003430831],"domain_scores_gemma":[0.9983055,0.0001378617,0.0003922079,0.0004936879,0.0004851601,0.0001856074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004651658,0.001393433,0.003912098,0.0001167134,0.0006570591,0.000224369,0.005289972,0.02105648,0.00003838275,0.2286962,0.01470378,0.723865],"study_design_scores_gemma":[0.001147672,0.001334875,0.4135099,0.0001439519,0.0001578129,0.000608172,0.0002901481,0.4762038,9.653526e-7,0.01963869,0.08555716,0.001406866],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003726878,0.005911079,0.9579446,0.0005227671,0.0004865234,0.002875479,0.00004352827,0.0004189144,0.02807023],"genre_scores_gemma":[0.4532425,0.002707588,0.5177299,0.000191387,0.001191874,0.0006777926,0.0006585674,0.0001239873,0.0234764],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7224582,"threshold_uncertainty_score":0.9997097,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01465560192987431,"gpt":0.2238490074227482,"spread":0.2091934054928739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003513693,0.0003998453,0.0003519566,0.000254033,0.001166357,0.0001510725,0.00031847,0.000232881,0.00001263557],"category_scores_gemma":[0.00001850002,0.0003518563,0.0001149152,0.0001908478,0.0004880383,0.0002728403,0.0005573906,0.0004472324,0.0001440377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362966,"about_ca_system_score_gemma":0.0001846308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004143586,"about_ca_topic_score_gemma":0.000002888121,"domain_scores_codex":[0.9975516,0.0001024046,0.0006484015,0.000789255,0.0005876486,0.0003206523],"domain_scores_gemma":[0.9984682,0.0004337388,0.0004095226,0.0002720945,0.0002814236,0.0001350152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002590871,0.00001810582,0.00003891958,0.00006943835,0.0001162843,0.000007307347,0.0004156703,0.3231543,0.000002683139,0.6241591,0.008087571,0.04390472],"study_design_scores_gemma":[0.0007140595,0.00008751027,0.004986458,0.00007179086,0.00005178807,0.0001168254,0.00002504748,0.6377324,1.201218e-7,0.3460229,0.009892142,0.0002989269],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008696481,0.004845541,0.9566172,0.003619357,0.0003168394,0.001187938,0.0000123539,0.0004579875,0.03285578],"genre_scores_gemma":[0.1241208,0.01258271,0.4658605,0.0008471538,0.001501076,0.0007202566,0.001884705,0.0002568475,0.392226],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4907567,"threshold_uncertainty_score":0.9998934,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.0228751579642623,"gpt":0.2598006715739276,"spread":0.2369255136096653,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0001766721,0.0004065467,0.0002997704,0.0004132892,0.001606832,0.0001308369,0.0003596642,0.0001375373,0.00008288305],"category_scores_gemma":[0.000008013525,0.0004286183,0.0001023298,0.000344933,0.0001155584,0.000261182,0.0005767288,0.0004261254,0.00004436806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002786406,"about_ca_system_score_gemma":0.0002433481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004874558,"about_ca_topic_score_gemma":0.00001454326,"domain_scores_codex":[0.9974509,0.00005406377,0.000507141,0.0008976264,0.0006101209,0.000480143],"domain_scores_gemma":[0.9988642,0.00008020551,0.0003050504,0.0002922409,0.0002239177,0.0002343962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001430897,0.0001654803,0.0001378531,0.00004996784,0.0001255694,0.000012457,0.00049404,0.848611,0.00002178533,0.08446577,0.001935276,0.06396651],"study_design_scores_gemma":[0.0006828904,0.001414434,0.006170846,0.00007930621,0.00007416327,0.000135571,0.0000559644,0.8670252,7.055608e-7,0.02220475,0.1013985,0.0007577245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000487421,0.001078304,0.9901428,0.0004664609,0.0001356882,0.001902025,0.00002036561,0.0004601783,0.005306782],"genre_scores_gemma":[0.2823584,0.0006043072,0.6636645,0.0003213533,0.000300898,0.002330184,0.002294016,0.0001609298,0.04796541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3264782,"threshold_uncertainty_score":0.9998165,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152360548","doi":"10.1007/978-1-4419-1626-6_8","title":"Algorithmic Trading with Developmental and Linear Genetic Programming","year":2009,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Profit (economics); Stock market; Pairs trade; Stock trading; Trading strategy; Stock (firearms); Linear programming; Economics; Algorithmic trading; Financial economics; Econometrics; Computer science; Business; Microeconomics; Alternative trading system; Artificial intelligence; Biology; Algorithm; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06206718783974972,"gpt":0.3173230143692484,"spread":0.2552558265294986,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006376035,0.0003445713,0.0003827984,0.0003973334,0.0003471463,0.0001319396,0.000170386,0.0001953754,0.00005726335],"category_scores_gemma":[0.0001203854,0.0002927357,0.00005266098,0.0001372984,0.0002333034,0.00009230922,0.00009945379,0.000216562,0.00001971392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000102624,"about_ca_system_score_gemma":0.0001927438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006904165,"about_ca_topic_score_gemma":0.000005915773,"domain_scores_codex":[0.9973199,0.00009856556,0.0006233004,0.0007966912,0.0009006599,0.0002608369],"domain_scores_gemma":[0.9985533,0.000582537,0.0003383085,0.0001471647,0.0002192463,0.0001594069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003466837,0.000008279995,0.000831518,0.00001124991,0.00003232195,0.00003115926,0.0002105057,0.0009367476,0.000004014367,0.000181795,0.0003905661,0.9973271],"study_design_scores_gemma":[0.001191374,0.0009728345,0.3910793,0.0003681566,0.0001988099,0.004523262,0.0001664958,0.2974576,0.000002865085,0.2572582,0.04550301,0.001278048],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06648842,0.009316362,0.8588181,0.0002748872,0.0004869348,0.001741882,0.00003123252,0.0002123627,0.06262985],"genre_scores_gemma":[0.03117753,0.0001024448,0.9457232,0.00005686329,0.0002512109,0.00001195823,0.00002907519,0.00004172984,0.02260595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9960491,"threshold_uncertainty_score":0.9999525,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Artificial intelligence; Data science; Human–computer interaction","retraction":null,"screen_n_in":null,"score":{"opus":0.05950827733421983,"gpt":0.3158990664913205,"spread":0.2563907891571007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001923859,0.0002666338,0.0002390649,0.0002329637,0.0003279119,0.0001443361,0.0008742596,0.0002899871,0.00004434537],"category_scores_gemma":[0.00003741539,0.0002821272,0.00004784795,0.0002600609,0.0001470729,0.000222514,0.0006805121,0.0002142337,0.00001382731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003553998,"about_ca_system_score_gemma":0.000341049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001487229,"about_ca_topic_score_gemma":0.00004047053,"domain_scores_codex":[0.9978967,0.00004677228,0.0003284007,0.0008705474,0.0005182259,0.0003393769],"domain_scores_gemma":[0.9988732,0.0001056239,0.0002154938,0.0005257707,0.00008230602,0.0001975802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007126787,0.0002898185,0.0006156705,0.0002536034,0.0001512471,0.00001589186,0.0001538278,0.003055983,0.00005747469,0.003383718,0.07427702,0.9177386],"study_design_scores_gemma":[0.0002822601,0.00005260987,0.003020664,0.00008218738,0.00003767776,0.000115237,0.00002310789,0.815423,7.35983e-7,0.001835264,0.1788214,0.0003059199],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001047822,0.004231979,0.9921206,0.000349666,0.0007068644,0.0007038779,0.00005410117,0.0002917938,0.0004932714],"genre_scores_gemma":[0.002231034,0.0004435339,0.9928358,0.00005468338,0.001433619,0.00018109,0.0003415589,0.0001306351,0.002348102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9174327,"threshold_uncertainty_score":0.9999631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408012843","doi":"10.1007/978-981-96-0077-9_5","title":"Evolving Many-Model Agents with Vector and Matrix Operations in Tangled Program Graphs","year":2025,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Matrix (chemical analysis); Vector (molecular biology); Computer science; Mathematics; Biology; Materials science; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.01344726544251667,"gpt":0.2528431059099153,"spread":0.2393958404673986,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007849006,0.0002553466,0.0002248686,0.0003517859,0.0002075652,0.0001728829,0.0001899785,0.0001495531,0.000008494327],"category_scores_gemma":[0.00001192839,0.0002576516,0.00003272882,0.0001255271,0.00009099217,0.0002135023,0.0002029429,0.0002117558,0.000005224015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007238,"about_ca_system_score_gemma":0.0001980595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001241123,"about_ca_topic_score_gemma":0.00001173641,"domain_scores_codex":[0.9986487,0.00002707722,0.0003325976,0.0004939462,0.00029268,0.0002049664],"domain_scores_gemma":[0.9994168,0.00006080854,0.0001032014,0.0002082864,0.0001408358,0.00007009314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006434852,0.00001579372,0.0004474648,0.00006588725,0.00003675857,0.00001053382,0.0001765307,0.943974,0.000001932448,0.04686469,0.0006642644,0.007735678],"study_design_scores_gemma":[0.0003986685,0.0001712361,0.01846027,0.0002008357,0.00003399787,0.00002450147,0.000006016594,0.9717559,2.09907e-7,0.00838914,0.0003040994,0.0002550867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001109204,0.001516664,0.9845021,0.0001906451,0.0001195006,0.0008117981,0.00000504155,0.0001331672,0.0116119],"genre_scores_gemma":[0.2179898,0.0006009319,0.7206598,0.0001253669,0.00005012479,0.00008449049,0.000172209,0.00003829187,0.06027896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2638423,"threshold_uncertainty_score":0.9999875,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Genetic programming; Sampling (signal processing); Computer science; Symbolic regression; Statistics; Econometrics; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.0228414497904075,"gpt":0.2448681306979612,"spread":0.2220266809075537,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001301279,0.0002849085,0.0003737961,0.0002289207,0.0002184344,0.0000475183,0.0002146318,0.0002796978,0.000007751623],"category_scores_gemma":[0.00000938318,0.0003272206,0.0000498449,0.0001101818,0.0002664409,0.0001862367,0.0002234098,0.0003326281,0.000004681522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006624849,"about_ca_system_score_gemma":0.000145995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004949242,"about_ca_topic_score_gemma":0.0000325449,"domain_scores_codex":[0.9979907,0.00002972508,0.0006319203,0.0007224368,0.0003523534,0.0002728147],"domain_scores_gemma":[0.9988785,0.0001604828,0.0003053517,0.0003116176,0.0002021583,0.0001419188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002997727,0.0002656179,0.003994498,0.0003666899,0.0001104643,0.00005456166,0.00063137,0.03250913,0.0007962436,0.5069426,0.00005813802,0.4542407],"study_design_scores_gemma":[0.0002258851,0.00008172974,0.1013997,0.00006753263,0.00003402672,0.0001984765,0.000004552712,0.4175623,0.000004725147,0.4798545,0.0002736285,0.0002929615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01867821,0.003037129,0.9768125,0.0001314012,0.0001000492,0.0005569591,0.00002868062,0.00005837864,0.0005966262],"genre_scores_gemma":[0.2857829,0.0006689474,0.7129502,0.0000264593,0.0001026274,0.00003194613,0.00009563631,0.0000233627,0.0003179228],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4539477,"threshold_uncertainty_score":0.999918,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02750249213461001,"gpt":0.2428350913914425,"spread":0.2153325992568325,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000142551,0.0004204609,0.0002981196,0.0003851939,0.0005428212,0.00017509,0.0004773251,0.0003063242,0.00007977356],"category_scores_gemma":[0.000008548034,0.0004533571,0.0001760623,0.0001882718,0.0002485061,0.0002634708,0.0005578342,0.0004184722,0.001151424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963093,"about_ca_system_score_gemma":0.0002579576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002005043,"about_ca_topic_score_gemma":0.000004581589,"domain_scores_codex":[0.997584,0.00002052768,0.0005706121,0.0009752813,0.0004772953,0.0003723214],"domain_scores_gemma":[0.998755,0.0001692524,0.0001780826,0.0004677009,0.0002428283,0.0001871782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002601771,0.00002713089,0.000002177163,0.00004154678,0.00007559823,0.00001524517,0.0001066592,0.007012967,0.000006765764,0.9046084,0.0175718,0.07052909],"study_design_scores_gemma":[0.00006108546,0.00006772942,0.0007026928,0.00007871244,0.00005888635,0.0001671628,0.000009535876,0.3386745,0.000001327152,0.5338543,0.125945,0.0003790385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00001345544,0.01438342,0.884703,0.002599746,0.0009628272,0.0004040849,0.0000902024,0.0004140878,0.09642912],"genre_scores_gemma":[0.03525501,0.00569577,0.4711265,0.0006055564,0.00178534,0.000210764,0.0006731509,0.0001619104,0.484486],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4135765,"threshold_uncertainty_score":0.9997918,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}