{"meta":{"query_hash":"0de77fbffe84","filters":{"venue":"Proceedings of the Genetic and Evolutionary Computation Conference"},"cohort_total":17,"direct_labels_cover":0,"predictions_cover":17,"exported":17,"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/0de77fbffe84","api":"https://metacan.xera.ac/api/v1/cohort?venue=Proceedings+of+the+Genetic+and+Evolutionary+Computation+Conference"},"results":[{"id":"W2724228633","doi":"10.1145/3071178.3071303","title":"Multi-task learning in Atari video games with emergent tangled program graphs","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Reinforcement Learning in Robotics","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Observability; Task (project management); Video game; Artificial intelligence; Variety (cybernetics); State (computer science); Matching (statistics); Human–computer interaction; Machine learning; Multimedia; Programming language","score_opus":0.021923593551869416,"score_gpt":0.25637648094619514,"score_spread":0.23445288739432574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724228633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.673693,0.0003122867,0.3190511,0.0006524448,0.000040745734,0.00014770664,0.00010409273,0.0004844424,0.005514212],"genre_scores_gemma":[0.95996463,0.000050219907,0.03750973,0.00007492577,0.000008641674,0.000098838194,0.00009998056,0.000048956455,0.002143975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997274,0.000102458056,0.000010936877,0.00006940166,0.000036070378,0.0000536602],"domain_scores_gemma":[0.9983638,0.0012006762,0.00011254488,0.00005769616,0.00009752933,0.00016780752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086323475,0.00063733547,0.0005902492,0.00044741118,0.00042433917,0.00060910726,0.000986762,0.00080249563,0.001492762],"category_scores_gemma":[0.0045538954,0.00040616654,0.0004474894,0.00022528032,0.0008040932,0.0010373534,0.0010926115,0.0011319498,0.00011325036],"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":[0.00007863471,0.00011024694,0.0011617162,0.00003360403,0.000021329259,0.00011920139,0.000116715375,0.97623676,0.0008620608,0.006080222,0.00050806836,0.014671401],"study_design_scores_gemma":[0.000008932842,0.000017393599,0.00013404319,0.000002272369,0.0000017180704,0.0000051891875,0.000014412801,0.99538016,0.00011926188,0.004224124,0.000090637026,0.0000019226031],"about_ca_topic_score_codex":0.010382884,"about_ca_topic_score_gemma":0.013720197,"teacher_disagreement_score":0.010382884,"about_ca_system_score_codex":0.0012800163,"about_ca_system_score_gemma":0.00069620943,"threshold_uncertainty_score":0.020644903},"labels":[],"label_agreement":null},{"id":"W2725874489","doi":"10.1145/3071178.3071213","title":"Properties of a GP active learning framework for streaming data with class imbalance","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Data Stream Mining Techniques","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":"Dalhousie University","funders":"","keywords":"Computer science; Class (philosophy); Active learning (machine learning); Streaming data; Artificial intelligence; Data mining","score_opus":0.04966372789149117,"score_gpt":0.2773607356695366,"score_spread":0.22769700777804544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2725874489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01647463,0.00031339834,0.980011,0.00044943002,0.000027128457,0.00005608932,0.00010766886,0.00018396437,0.0023767264],"genre_scores_gemma":[0.6646863,0.0007754804,0.3275464,0.00032738256,0.0001716451,0.00043930707,0.00045543577,0.0001748033,0.005423252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985569,0.0004290868,0.00007320054,0.00031287278,0.00048544438,0.00014242818],"domain_scores_gemma":[0.9928443,0.005118567,0.0005041333,0.00047318355,0.0007934201,0.00026635898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005896433,0.00074613176,0.0012437132,0.0012805766,0.0006555074,0.0021722557,0.0024015105,0.002086009,0.0022387349],"category_scores_gemma":[0.018164221,0.00055645575,0.00087242573,0.0010926052,0.0017868043,0.0023625651,0.0021390172,0.0026675758,0.00031842184],"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":[0.000065098946,0.000060527116,0.001871195,0.00009579316,0.00006175447,0.00019324305,0.0002453521,0.82816905,0.0023595453,0.11206082,0.0013958008,0.053421777],"study_design_scores_gemma":[0.000006121186,0.000020310106,0.0002114921,0.0000075723133,0.00000635297,0.00004448264,0.000007819401,0.9817222,0.00021731158,0.017279256,0.0004731065,0.000004053009],"about_ca_topic_score_codex":0.0055175466,"about_ca_topic_score_gemma":0.00330657,"teacher_disagreement_score":0.005896433,"about_ca_system_score_codex":0.0012943372,"about_ca_system_score_gemma":0.0014466571,"threshold_uncertainty_score":0.03118372},"labels":[],"label_agreement":null},{"id":"W2727779323","doi":"10.1145/3071178.3071294","title":"Reconsidering constraint release for active-set evolution strategies","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Set (abstract data type); Computer science; Constraint (computer-aided design); Mathematics; Programming language","score_opus":0.07974443942710047,"score_gpt":0.2980548241210974,"score_spread":0.21831038469399694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727779323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040740263,0.00032325115,0.9508663,0.0009414495,0.00009751285,0.00012453733,0.000020434385,0.00020241603,0.006683891],"genre_scores_gemma":[0.73915297,0.00037710043,0.25604922,0.00045567058,0.00009996904,0.00032345703,0.000049931416,0.00018837272,0.003303194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842924,0.0007346325,0.000099161385,0.00016301122,0.00041973862,0.00015419173],"domain_scores_gemma":[0.9915537,0.0063642366,0.00046678635,0.0006644655,0.00065627287,0.00029449732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035600183,0.00087645766,0.0010088874,0.00042439057,0.00062941655,0.0019578163,0.0025805668,0.0021679683,0.0025658181],"category_scores_gemma":[0.017060783,0.00043171464,0.00069759483,0.00041115828,0.0016067863,0.0028427327,0.0017609624,0.0028739392,0.00042150842],"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":[0.00031718292,0.0003017135,0.0015545039,0.00025039684,0.00013471606,0.0006226077,0.0007273311,0.6664581,0.01516117,0.2268787,0.0016617001,0.08593191],"study_design_scores_gemma":[0.00004341773,0.00012995423,0.00008311345,0.00003239688,0.000017909062,0.00006401023,0.000035639663,0.97065485,0.0031192615,0.023756646,0.002044313,0.000018524916],"about_ca_topic_score_codex":0.0010247784,"about_ca_topic_score_gemma":0.0010140332,"teacher_disagreement_score":0.0035600183,"about_ca_system_score_codex":0.00055278285,"about_ca_system_score_gemma":0.0011960433,"threshold_uncertainty_score":0.018827438},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36419997,0.00036169746,0.6275266,0.00052953785,0.00004251968,0.00013281821,0.00006999295,0.0013966633,0.005740204],"genre_scores_gemma":[0.70778656,0.00026035635,0.28843185,0.00020917968,0.000028172197,0.0002449726,0.00018785987,0.0002131372,0.002637851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996778,0.00008438416,0.000019961657,0.00008124655,0.00007843686,0.00005816472],"domain_scores_gemma":[0.9987373,0.00065191084,0.00012611513,0.0002479503,0.00011844302,0.00011838328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070426264,0.0005527275,0.0006839034,0.00053354737,0.00035794202,0.0008840545,0.0014296733,0.0007834238,0.002214721],"category_scores_gemma":[0.0033539475,0.0005629464,0.0005558663,0.00054084015,0.0011404982,0.0017106378,0.00156513,0.0017847907,0.00043489574],"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":[0.00006177803,0.0002011755,0.002690175,0.00011139524,0.000055536133,0.00009833576,0.00024507783,0.8701539,0.012203476,0.023860082,0.000894147,0.08942495],"study_design_scores_gemma":[0.0000089903515,0.000034819208,0.00013501647,0.0000062815407,0.000009584035,0.000012818058,0.000023664183,0.98489326,0.0010246753,0.013290195,0.00055657653,0.0000040864547],"about_ca_topic_score_codex":0.0022095144,"about_ca_topic_score_gemma":0.00456169,"teacher_disagreement_score":0.002214721,"about_ca_system_score_codex":0.0008705661,"about_ca_system_score_gemma":0.0010454556,"threshold_uncertainty_score":0.007409036},"labels":[],"label_agreement":null},{"id":"W2729697425","doi":"10.1145/3071178.3071209","title":"Searching for nonlinear relationships in fMRI data with symbolic regression","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Neural Networks 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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Functional magnetic resonance imaging; Nonlinear system; Computer science; Artificial intelligence; Linear model; Machine learning; Cognitive science; Neuroscience; Psychology; Physics","score_opus":0.079053460547292,"score_gpt":0.30572559281929995,"score_spread":0.22667213227200794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729697425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061960045,0.00043268965,0.9334336,0.0010535021,0.000028116625,0.000048919494,0.0003411337,0.0014601118,0.0012417829],"genre_scores_gemma":[0.6751553,0.0008035383,0.3187121,0.00025376465,0.00011885059,0.00019687683,0.0014991441,0.0003069601,0.0029534767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864715,0.0006205026,0.00008229292,0.0002863021,0.00026193372,0.000101829886],"domain_scores_gemma":[0.99052536,0.0077257655,0.0007551089,0.0005670494,0.00031173325,0.0001149208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029752264,0.0011034986,0.0011608503,0.0033130886,0.0006355912,0.0015133461,0.0012252523,0.0013493373,0.0046032313],"category_scores_gemma":[0.0230039,0.0006998629,0.0012046426,0.003309002,0.0012349738,0.0020104195,0.0019357088,0.002225689,0.0017242702],"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":[0.00033731546,0.00022522788,0.015654396,0.00038629764,0.00034952257,0.0010074679,0.0004678742,0.5034742,0.012590034,0.028684977,0.0048541245,0.43196854],"study_design_scores_gemma":[0.000008124211,0.000025337547,0.0010016368,0.000012176592,0.000011556867,0.00007292938,0.00003910059,0.97942376,0.00089389505,0.018013809,0.00048339088,0.000014180256],"about_ca_topic_score_codex":0.0027740125,"about_ca_topic_score_gemma":0.0036737989,"teacher_disagreement_score":0.0046032313,"about_ca_system_score_codex":0.00055415573,"about_ca_system_score_gemma":0.0013487622,"threshold_uncertainty_score":0.015734673},"labels":[],"label_agreement":null},{"id":"W2886334640","doi":"10.1145/3205455.3205612","title":"Benchmarking evolutionary computation approaches to insider threat detection","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Data Stream Mining Techniques","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Benchmarking; Genetic programming; Insider threat; Adaptation (eye); Context (archaeology); Set (abstract data type); Class (philosophy); Machine learning; Insider; Evolutionary computation; Artificial intelligence; Business","score_opus":0.06690114304704498,"score_gpt":0.24629011344705778,"score_spread":0.1793889704000128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886334640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45326498,0.00493863,0.5169696,0.0026302962,0.000486214,0.00037871223,0.0005262113,0.0014243982,0.019380955],"genre_scores_gemma":[0.865972,0.00071952015,0.13039349,0.0002313208,0.00009487231,0.00016318253,0.00057336316,0.00009325469,0.0017590217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741286,0.00124045,0.00013143866,0.00034198805,0.0006571173,0.00021615361],"domain_scores_gemma":[0.9930264,0.0046734037,0.00030999782,0.0006238708,0.0011842736,0.00018211636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048729847,0.0011291229,0.0011700284,0.0027998937,0.0006301548,0.0019165785,0.0018989218,0.002095117,0.0013466739],"category_scores_gemma":[0.015959699,0.0003199189,0.00074437965,0.0021969383,0.0010030436,0.0015300164,0.0014679484,0.001675495,0.0002845722],"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":[0.00013981659,0.00019029803,0.0041755657,0.00010437769,0.00010323625,0.000068796995,0.000054104592,0.8912705,0.0006404857,0.0057478375,0.0012824872,0.09622246],"study_design_scores_gemma":[0.000008780249,0.00004041974,0.0004388028,0.000008061921,0.0000065044246,0.000014493493,0.000019907468,0.9964336,0.00039907105,0.0022813645,0.00034556488,0.0000034791115],"about_ca_topic_score_codex":0.0054968363,"about_ca_topic_score_gemma":0.004718116,"teacher_disagreement_score":0.0054968363,"about_ca_system_score_codex":0.0015187922,"about_ca_system_score_gemma":0.0013655104,"threshold_uncertainty_score":0.025771081},"labels":[],"label_agreement":null},{"id":"W2887081063","doi":"10.1145/3205455.3205622","title":"Towards the automated recovery of complex temporal API-usage patterns","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Software Engineering Research","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":"Université de Montréal; Concordia University","funders":"","keywords":"Computer science","score_opus":0.035859079483935574,"score_gpt":0.2650376949175621,"score_spread":0.22917861543362655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887081063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037164703,0.00010851878,0.9548517,0.00032224978,0.00002342179,0.00012097789,0.00028734095,0.0059605814,0.0011605823],"genre_scores_gemma":[0.15416014,0.00013085664,0.84162205,0.00015005049,0.000015725644,0.0001272037,0.0010920264,0.0010853742,0.0016165876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759454,0.0005399918,0.00016364532,0.00060594385,0.00088710914,0.00020865623],"domain_scores_gemma":[0.99248475,0.003077346,0.0011593957,0.001955972,0.0011671135,0.0001554527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020195693,0.0013236016,0.00074212236,0.0020576154,0.0008089808,0.0016466767,0.002135145,0.0016723593,0.001398607],"category_scores_gemma":[0.012446669,0.0009390662,0.001725139,0.0018133118,0.0012918076,0.0019234519,0.0021052323,0.0025650165,0.00077684096],"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":[0.0002711437,0.00060592004,0.025462238,0.00065543514,0.00025273155,0.0011979108,0.0014298554,0.22531395,0.046238624,0.027535256,0.009858168,0.6611787],"study_design_scores_gemma":[0.000026886119,0.000038941485,0.0014106887,0.0000509108,0.00005072267,0.0003944407,0.00018314757,0.9520376,0.018036412,0.023015734,0.0047241114,0.000030515484],"about_ca_topic_score_codex":0.0064279535,"about_ca_topic_score_gemma":0.0066098985,"teacher_disagreement_score":0.0064279535,"about_ca_system_score_codex":0.0008241605,"about_ca_system_score_gemma":0.002781487,"threshold_uncertainty_score":0.012781084},"labels":[],"label_agreement":null},{"id":"W2953979082","doi":"10.1145/3321707.3321728","title":"A surrogate model assisted (1+1)-ES with increased exploitation of the model","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"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":"Surrogate model; Context (archaeology); Computer science; Gaussian process; Mathematical optimization; Black box; Process (computing); Function (biology); Gaussian; Mathematics; Artificial intelligence; Physics","score_opus":0.017569413325023982,"score_gpt":0.22306256983906494,"score_spread":0.20549315651404096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953979082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05612877,0.00013608171,0.9378906,0.0002443193,0.000035066496,0.00003900495,0.000034414003,0.00022328182,0.0052684946],"genre_scores_gemma":[0.71223265,0.00010310331,0.28338084,0.00016566647,0.000021449274,0.00008574024,0.00008691688,0.00007046507,0.0038531248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994485,0.00026427073,0.000022001368,0.00006087741,0.00016183768,0.000042569456],"domain_scores_gemma":[0.99846137,0.0009952622,0.00011950277,0.00020771689,0.00016419696,0.000051872743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015884746,0.0006161427,0.000756559,0.00033320824,0.00021851837,0.0007812964,0.0009319236,0.0015710038,0.0019206611],"category_scores_gemma":[0.004041942,0.0002629105,0.00065534306,0.00046851742,0.00062062044,0.0010462228,0.0012463958,0.0010040154,0.00034443146],"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":[0.000059232505,0.000048787773,0.00041800557,0.00004507203,0.000022504475,0.000072682415,0.00003736998,0.9570458,0.0053574895,0.016430857,0.00030755234,0.020154528],"study_design_scores_gemma":[0.0000059044028,0.00003638574,0.0000551222,0.00000311247,0.0000029747046,0.000020081412,0.0000022682193,0.996949,0.00080898654,0.0018988238,0.00021347511,0.0000038505673],"about_ca_topic_score_codex":0.0007602677,"about_ca_topic_score_gemma":0.0010041054,"teacher_disagreement_score":0.0019206611,"about_ca_system_score_codex":0.0003691099,"about_ca_system_score_gemma":0.00064063503,"threshold_uncertainty_score":0.008400798},"labels":[],"label_agreement":null},{"id":"W2954089099","doi":"10.1145/3321707.3321866","title":"Evolving dota 2 shadow fiend bots using genetic programming with external memory","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Artificial Intelligence in Games","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Observability; Context (archaeology); Genetic programming; Shadow (psychology); Artificial intelligence; Reinforcement learning; Set (abstract data type); Task (project management); Human–computer interaction; Programming language; Psychology; Engineering; Mathematics","score_opus":0.0237815563799038,"score_gpt":0.2475599406890724,"score_spread":0.22377838430916858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954089099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82719314,0.00006088895,0.16089565,0.00020001536,0.00005486194,0.00015276897,0.00006718217,0.00053630944,0.01083921],"genre_scores_gemma":[0.9400737,0.00003205872,0.054056864,0.000066958804,0.000003884371,0.00011848878,0.000070388385,0.000046710644,0.00553091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986947,0.000024535117,0.000004472039,0.000028551192,0.000029604318,0.00004331497],"domain_scores_gemma":[0.9994686,0.0002858525,0.00005848804,0.000046875957,0.000055054472,0.000085079635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039415003,0.00049615605,0.00045579358,0.00037969011,0.00031745067,0.0008095703,0.0008963558,0.0006786324,0.0025342288],"category_scores_gemma":[0.0014967354,0.00027044435,0.00035510585,0.00015248673,0.00071016734,0.00046487793,0.001016908,0.00063007313,0.0002164588],"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":[0.0002446211,0.00024473126,0.0041324785,0.000051294595,0.000048364385,0.00039045914,0.00022816879,0.9347328,0.0135584185,0.015622395,0.00075429096,0.029992018],"study_design_scores_gemma":[0.000015532443,0.00007540695,0.00020505789,0.000003890591,0.0000056418703,0.000020308496,0.00003373488,0.99655545,0.0011007363,0.0014909398,0.0004883513,0.0000049343857],"about_ca_topic_score_codex":0.0044899806,"about_ca_topic_score_gemma":0.0040579997,"teacher_disagreement_score":0.0044899806,"about_ca_system_score_codex":0.00090494927,"about_ca_system_score_gemma":0.0006973908,"threshold_uncertainty_score":0.008927703},"labels":[],"label_agreement":null},{"id":"W2955270668","doi":"10.1145/3321707.3321724","title":"Large-scale noise-resilient evolution-strategies","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"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":"Fondation Pour La Conservation Du Saumon Atlantique","keywords":"Computer science; Noise (video); Ranking (information retrieval); Weighting; Mathematical optimization; Stochastic gradient descent; Algorithm; Bounded function; Curse of dimensionality; Reinforcement learning; Mathematics; Artificial intelligence; Artificial neural network","score_opus":0.012223312574504394,"score_gpt":0.24314924502124322,"score_spread":0.2309259324467388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955270668","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020768657,0.000115777715,0.9766167,0.000108180386,0.000023519358,0.000055044333,0.000016043918,0.00036010417,0.001935928],"genre_scores_gemma":[0.81251657,0.00013545253,0.18300967,0.00015355795,0.00002852949,0.00022683037,0.00007735073,0.00013090951,0.003721089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994048,0.00021969558,0.000032766417,0.000104848936,0.00018224835,0.000055616118],"domain_scores_gemma":[0.9982697,0.0010988587,0.00015068748,0.00017529904,0.00023527344,0.00007023087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014396426,0.0008912303,0.001054268,0.00046207733,0.0004499367,0.0009291609,0.00134319,0.0010870369,0.0012463239],"category_scores_gemma":[0.0051697516,0.00045237795,0.0004939297,0.000405214,0.0008985645,0.0008751083,0.0012865087,0.0010479924,0.00038977584],"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":[0.000026912423,0.000025309793,0.000290006,0.000025333531,0.000024838797,0.00004491913,0.000040683735,0.9648784,0.0016100325,0.01126177,0.00038658394,0.021385318],"study_design_scores_gemma":[0.000003896152,0.000011759102,0.00003247978,0.0000020353366,0.0000024149483,0.0000076061538,0.0000022378115,0.9972511,0.00024344602,0.0022766395,0.00016389802,0.0000025065217],"about_ca_topic_score_codex":0.0022270223,"about_ca_topic_score_gemma":0.001826168,"teacher_disagreement_score":0.0022270223,"about_ca_system_score_codex":0.0008518919,"about_ca_system_score_gemma":0.00072081434,"threshold_uncertainty_score":0.007613659},"labels":[],"label_agreement":null},{"id":"W3176022961","doi":"10.1145/3449639.3459348","title":"On the impact of tangled program graph marking schemes under the atari reinforcement learning benchmark","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Reinforcement Learning in Robotics","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; Reinforcement learning; Benchmark (surveying); Graph; Adaptation (eye); Heuristic; Scheme (mathematics); Theoretical computer science; Modularity (biology); Artificial intelligence; Node (physics); Machine learning; Engineering; Mathematics","score_opus":0.0203845929356071,"score_gpt":0.25870910566226896,"score_spread":0.23832451272666186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176022961","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98292875,0.00062890537,0.006829748,0.0005195426,0.00006277929,0.000055181692,0.00030586863,0.0006843286,0.007984968],"genre_scores_gemma":[0.991602,0.00009209328,0.0070392997,0.000081417646,0.000010535166,0.00003074034,0.0003466074,0.00007659977,0.0007207352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998123,0.0008871703,0.00011084009,0.00025163905,0.00029826048,0.00032900114],"domain_scores_gemma":[0.9673177,0.025965033,0.0015656516,0.0020834992,0.0015484898,0.0015196837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004013534,0.0009272197,0.0005331166,0.0009376683,0.00059320754,0.0009867746,0.0013145505,0.0010325693,0.002426487],"category_scores_gemma":[0.023439683,0.00021550924,0.00030560288,0.00063859194,0.000999537,0.0015322419,0.0010823858,0.0014573323,0.00026878534],"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":[0.0018615164,0.0012630954,0.011825692,0.00029077276,0.0001182599,0.000129994,0.00010870286,0.9114919,0.00455171,0.006124481,0.0034353866,0.05879855],"study_design_scores_gemma":[0.00018097175,0.0013973017,0.0041234572,0.00004956938,0.000054428234,0.000043071224,0.00014057421,0.9823889,0.0045228833,0.0062085683,0.0008694576,0.000020771728],"about_ca_topic_score_codex":0.007742818,"about_ca_topic_score_gemma":0.011996714,"teacher_disagreement_score":0.007742818,"about_ca_system_score_codex":0.0014147979,"about_ca_system_score_gemma":0.001211096,"threshold_uncertainty_score":0.02122587},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09624294,0.00038139473,0.8971895,0.00024183786,0.00005006399,0.00008547058,0.00005552185,0.0007254503,0.005027816],"genre_scores_gemma":[0.6856552,0.00035024993,0.3093628,0.00020372328,0.000029812782,0.0003862791,0.00018868997,0.00034243218,0.0034807953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992494,0.0002390887,0.000048593,0.00011856909,0.00025933952,0.00008502577],"domain_scores_gemma":[0.9980556,0.0011402472,0.00016116825,0.00020258527,0.000377255,0.0000630825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002086845,0.0010523845,0.0006780263,0.0009948119,0.00035701506,0.00087484316,0.0011069139,0.0013573884,0.0014871464],"category_scores_gemma":[0.008612367,0.00040017004,0.00061019993,0.0005092198,0.0008762613,0.0013566758,0.0013306913,0.0014124163,0.0003286698],"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":[0.000055390014,0.000074773554,0.0012075318,0.00008213624,0.000037411155,0.00015227134,0.0001220912,0.84320873,0.00960298,0.028992698,0.0012554476,0.115208566],"study_design_scores_gemma":[0.0000099925355,0.00003678333,0.00012881975,0.000017926612,0.000011303121,0.00003618358,0.000012804939,0.98791504,0.0027346658,0.008180844,0.00091040804,0.0000051808715],"about_ca_topic_score_codex":0.00094281265,"about_ca_topic_score_gemma":0.0012166875,"teacher_disagreement_score":0.002086845,"about_ca_system_score_codex":0.0011112447,"about_ca_system_score_gemma":0.00082728296,"threshold_uncertainty_score":0.011036456},"labels":[],"label_agreement":null},{"id":"W4285734700","doi":"10.1145/3512290.3528722","title":"EvoIsland","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Music Technology and Sound Studies","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":"Computer science; Scalability; Range (aeronautics); Interface (matter); Grid; Human–computer interaction; Hexagonal crystal system; Distributed computing; Geography; Database; Engineering; Chemistry; Parallel computing","score_opus":0.015113280124654759,"score_gpt":0.21299763072733727,"score_spread":0.1978843506026825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285734700","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016259199,0.001032977,0.8028842,0.00042804252,0.00024746943,0.00027432406,0.0031473686,0.101373464,0.074352965],"genre_scores_gemma":[0.26582327,0.0012435645,0.5997361,0.001112236,0.00012226075,0.0009877018,0.015213367,0.01806536,0.097696155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999529,0.00010678883,0.000023223556,0.00009759623,0.00017542677,0.00006796494],"domain_scores_gemma":[0.9995442,0.00018195524,0.000015648715,0.00011936411,0.00006768002,0.0000711276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007505453,0.0009845706,0.0005941089,0.00060611265,0.0004448621,0.0018059622,0.0022642727,0.0010099068,0.0487832],"category_scores_gemma":[0.0022719079,0.00039522443,0.0009905631,0.0003295681,0.0005738411,0.0024679466,0.0038473455,0.0012265578,0.010073711],"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":[0.0016660787,0.0003888864,0.003275147,0.0016506998,0.00017839989,0.0012774797,0.0020775122,0.036327776,0.062416047,0.14922084,0.20326397,0.5382571],"study_design_scores_gemma":[0.0002251844,0.00029697226,0.0012916997,0.00022878444,0.00005061899,0.00093770743,0.0002330504,0.16160248,0.0219974,0.055175383,0.7578389,0.00012178096],"about_ca_topic_score_codex":0.0012255738,"about_ca_topic_score_gemma":0.0020802924,"teacher_disagreement_score":0.0487832,"about_ca_system_score_codex":0.00036642462,"about_ca_system_score_gemma":0.00040518906,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4384024880","doi":"10.1145/3583131.3590391","title":"MOAZ: A Multi-Objective AutoML-Zero Framework","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Machine Learning and Data Classification","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":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Architecture; Pareto principle; Feature (linguistics); Mathematical optimization; Mathematics","score_opus":0.026907332893805543,"score_gpt":0.26998070453079687,"score_spread":0.24307337163699133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384024880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047848746,0.00031136975,0.98851204,0.00016904066,0.00003800578,0.00007286091,0.00012393232,0.0016452667,0.004342558],"genre_scores_gemma":[0.23090182,0.0003644511,0.7589591,0.00043873972,0.000080764104,0.0007108438,0.000615211,0.0010335341,0.006895506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921215,0.0003057976,0.000032136486,0.000115433395,0.00024315367,0.000091336056],"domain_scores_gemma":[0.999295,0.00037583092,0.00007032506,0.0000737091,0.00013505844,0.000050110928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017159906,0.0014547234,0.0013996603,0.0010791112,0.0005596184,0.0013274052,0.0027953307,0.0016608895,0.0075754556],"category_scores_gemma":[0.0026055314,0.0007322127,0.001478352,0.00073483726,0.0009486417,0.0011731861,0.002516925,0.0018894164,0.0014629512],"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":[0.00006717012,0.00007351126,0.0006106559,0.00019394493,0.00007925225,0.000052660125,0.000040251736,0.88385147,0.0014801194,0.03186695,0.0034937917,0.078190185],"study_design_scores_gemma":[0.00001658121,0.00003622012,0.000052941046,0.000012556973,0.000008209258,0.000009732832,0.0000070383103,0.9896806,0.0002885469,0.008113909,0.0017675742,0.000006128932],"about_ca_topic_score_codex":0.004200546,"about_ca_topic_score_gemma":0.005838765,"teacher_disagreement_score":0.0075754556,"about_ca_system_score_codex":0.000968129,"about_ca_system_score_gemma":0.0018635793,"threshold_uncertainty_score":0.025342405},"labels":[],"label_agreement":null},{"id":"W4391912930","doi":"10.1145/3583131.3590467","title":"Evolutionary Mixed-Integer Optimization with Explicit Constraints","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"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":"Integer (computer science); Mathematical optimization; Integer programming; Computer science; Mathematics; Programming language","score_opus":0.023124006418148123,"score_gpt":0.24577984532345756,"score_spread":0.22265583890530943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391912930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017759481,0.0002452651,0.9769613,0.00016429173,0.000044966167,0.00004457932,0.00003129579,0.0001407209,0.0046080104],"genre_scores_gemma":[0.37711313,0.0002687413,0.61701024,0.00019091756,0.000036765647,0.00030507636,0.000115807736,0.00008745746,0.0048719044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992162,0.00037109875,0.00003066965,0.000109164335,0.00019317381,0.00007966338],"domain_scores_gemma":[0.9976102,0.0018186985,0.00016945836,0.00013934467,0.00018983289,0.00007247621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017093249,0.0009395432,0.0011108674,0.00045249503,0.00044366802,0.001368551,0.0011793431,0.0014231821,0.0023736614],"category_scores_gemma":[0.0045371996,0.0006491851,0.00081019895,0.0006709619,0.00085440546,0.0010690581,0.0014605375,0.0016224202,0.00032778425],"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":[0.000034223034,0.000037299098,0.0002460825,0.000046023677,0.000027717435,0.00006989933,0.000041807478,0.9449457,0.001253149,0.029150208,0.00038843372,0.023759548],"study_design_scores_gemma":[0.000010805683,0.000012012517,0.000024500087,0.000005884537,0.0000042357697,0.000010123361,0.0000035494338,0.9936051,0.00029544113,0.005490495,0.000534925,0.000002954715],"about_ca_topic_score_codex":0.0013199691,"about_ca_topic_score_gemma":0.0012625494,"teacher_disagreement_score":0.0023736614,"about_ca_system_score_codex":0.00055920193,"about_ca_system_score_gemma":0.00074180134,"threshold_uncertainty_score":0.009039879},"labels":[],"label_agreement":null},{"id":"W4400412426","doi":"10.1145/3638529.3654012","title":"Direct Augmented Lagrangian Evolution Strategies","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lagrangian; Augmented Lagrangian method; Computer science; Applied mathematics; Mathematics; Algorithm","score_opus":0.01818831382712729,"score_gpt":0.25866533924153157,"score_spread":0.24047702541440427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400412426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00665809,0.00038969243,0.96933794,0.000155865,0.00010854929,0.000092681774,0.000062294435,0.0003278918,0.022866972],"genre_scores_gemma":[0.38840172,0.00092307373,0.56978077,0.0004015422,0.0001261095,0.00075024005,0.0002797876,0.00023841669,0.039098404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999464,0.00019404234,0.000021636657,0.0000680096,0.00020469914,0.00004758905],"domain_scores_gemma":[0.9991992,0.00036732494,0.000088439156,0.0001288301,0.00017488422,0.000041362735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008385233,0.0010885011,0.0009447613,0.00070317654,0.0003881266,0.0015077213,0.0015702171,0.0013338139,0.007172256],"category_scores_gemma":[0.0025029178,0.00052671076,0.00060688145,0.0006742371,0.00077143696,0.0010780186,0.0019279041,0.0010928685,0.001732468],"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":[0.00006923722,0.00008653489,0.0004069033,0.00019687935,0.0000935155,0.00014125483,0.00012850377,0.6851078,0.0029808665,0.1566127,0.004218949,0.1499568],"study_design_scores_gemma":[0.000028497872,0.00005303534,0.00006388053,0.00002122153,0.0000125161305,0.00004640299,0.000010449374,0.9724256,0.0006204949,0.020151727,0.0065564048,0.000009857218],"about_ca_topic_score_codex":0.0013918736,"about_ca_topic_score_gemma":0.0018275098,"teacher_disagreement_score":0.007172256,"about_ca_system_score_codex":0.0005408476,"about_ca_system_score_gemma":0.0008839049,"threshold_uncertainty_score":0.023993611},"labels":[],"label_agreement":null},{"id":"W4412106329","doi":"10.1145/3712256.3726331","title":"Emergent Braitenberg-style Behaviours for Navigating the ViZDoom 'My Way Home' Labyrinth","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Data Visualization and Analytics","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":"Style (visual arts); Computer science; Telecommunications; Visual arts; Art","score_opus":0.01847430744442519,"score_gpt":0.2825160090607089,"score_spread":0.2640417016162837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412106329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39701158,0.000082365885,0.5848337,0.0004223631,0.00004215384,0.000051860985,0.00013946027,0.0010200944,0.016396485],"genre_scores_gemma":[0.9329198,0.000059973245,0.062025264,0.00005655854,0.0000036100225,0.00006845215,0.00009956367,0.00015852919,0.0046081967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998976,0.00002727609,0.0000042476768,0.000030757725,0.000015024752,0.000025001988],"domain_scores_gemma":[0.99969554,0.000090253554,0.00004734564,0.000074539654,0.00003366478,0.00005876659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026653428,0.0002610947,0.00023539894,0.0002901393,0.00048423363,0.0008446786,0.0006912098,0.0005692906,0.0035980532],"category_scores_gemma":[0.0015249453,0.00021604622,0.00034852434,0.00016627168,0.0011487927,0.0013366045,0.0010584011,0.00068647915,0.0004963516],"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":[0.00015340526,0.000089916255,0.0075941766,0.00012448826,0.000065333734,0.00051094824,0.0012485688,0.60294664,0.03846228,0.29939997,0.002739823,0.046664394],"study_design_scores_gemma":[0.000012831018,0.00005553018,0.00086873386,0.000015292078,0.000011378607,0.000095169744,0.00020838331,0.9122645,0.0046652774,0.0780892,0.003688733,0.000024957859],"about_ca_topic_score_codex":0.0029229703,"about_ca_topic_score_gemma":0.0047223275,"teacher_disagreement_score":0.0035980532,"about_ca_system_score_codex":0.0007258215,"about_ca_system_score_gemma":0.00068928365,"threshold_uncertainty_score":0.012036741},"labels":[],"label_agreement":null}]}