{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":17,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":17,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"0de77fbffe84","filters":{"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,"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":"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","authors":[{"name":"Stephen Kelly","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02192359355186942,"gpt":0.2563764809461951,"spread":0.2344528873943257,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008632347,0.0006373355,0.0005902492,0.0004474112,0.0004243392,0.0006091073,0.000986762,0.0008024956,0.001492762],"category_scores_gemma":[0.004553895,0.0004061665,0.0004474894,0.0002252803,0.0008040932,0.001037353,0.001092611,0.00113195,0.0001132504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001280016,"about_ca_system_score_gemma":0.0006962094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038288,"about_ca_topic_score_gemma":0.0137202,"domain_scores_codex":[0.9997274,0.0001024581,0.00001093688,0.00006940166,0.00003607038,0.0000536602],"domain_scores_gemma":[0.9983638,0.001200676,0.0001125449,0.00005769616,0.00009752933,0.0001678075],"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":[0.00007863471,0.0001102469,0.001161716,0.00003360403,0.00002132926,0.0001192014,0.0001167154,0.9762368,0.0008620608,0.006080222,0.0005080684,0.0146714],"study_design_scores_gemma":[0.000008932842,0.0000173936,0.0001340432,0.000002272369,0.00000171807,0.000005189188,0.0000144128,0.9953802,0.0001192619,0.004224124,0.00009063703,0.000001922603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.673693,0.0003122867,0.3190511,0.0006524448,0.00004074573,0.0001477066,0.0001040927,0.0004844424,0.005514212],"genre_scores_gemma":[0.9599646,0.00005021991,0.03750973,0.00007492577,0.000008641674,0.00009883819,0.00009998056,0.00004895646,0.002143975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01038288,"threshold_uncertainty_score":0.0206449,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"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","authors":[{"name":"Duc C. Le","is_ca":true},{"name":"Sara Khanchi","is_ca":true},{"name":"A. Nur Zincir‐Heywood","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06690114304704498,"gpt":0.2462901134470578,"spread":0.1793889704000128,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004872985,0.001129123,0.001170028,0.002799894,0.0006301548,0.001916579,0.001898922,0.002095117,0.001346674],"category_scores_gemma":[0.0159597,0.0003199189,0.0007443796,0.002196938,0.001003044,0.001530016,0.001467948,0.001675495,0.0002845722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001518792,"about_ca_system_score_gemma":0.00136551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005496836,"about_ca_topic_score_gemma":0.004718116,"domain_scores_codex":[0.9974129,0.00124045,0.0001314387,0.0003419881,0.0006571173,0.0002161536],"domain_scores_gemma":[0.9930264,0.004673404,0.0003099978,0.0006238708,0.001184274,0.0001821164],"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":[0.0001398166,0.000190298,0.004175566,0.0001043777,0.0001032362,0.000068797,0.00005410459,0.8912705,0.0006404857,0.005747837,0.001282487,0.09622246],"study_design_scores_gemma":[0.000008780249,0.00004041974,0.0004388028,0.000008061921,0.000006504425,0.00001449349,0.00001990747,0.9964336,0.0003990711,0.002281365,0.0003455649,0.000003479111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.453265,0.00493863,0.5169696,0.002630296,0.000486214,0.0003787122,0.0005262113,0.001424398,0.01938096],"genre_scores_gemma":[0.865972,0.0007195201,0.1303935,0.0002313208,0.00009487231,0.0001631825,0.0005733632,0.00009325469,0.001759022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005496836,"threshold_uncertainty_score":0.02577108,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"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","authors":[{"name":"Robert J. Smith","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0237815563799038,"gpt":0.2475599406890724,"spread":0.2237783843091686,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039415,0.000496156,0.0004557936,0.0003796901,0.0003174507,0.0008095703,0.0008963558,0.0006786324,0.002534229],"category_scores_gemma":[0.001496735,0.0002704443,0.0003551058,0.0001524867,0.0007101673,0.0004648779,0.001016908,0.0006300731,0.0002164588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009049493,"about_ca_system_score_gemma":0.0006973908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004489981,"about_ca_topic_score_gemma":0.004058,"domain_scores_codex":[0.9998695,0.00002453512,0.000004472039,0.00002855119,0.00002960432,0.00004331497],"domain_scores_gemma":[0.9994686,0.0002858525,0.00005848804,0.00004687596,0.00005505447,0.00008507964],"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":[0.0002446211,0.0002447313,0.004132478,0.0000512946,0.00004836439,0.0003904591,0.0002281688,0.9347328,0.01355842,0.0156224,0.000754291,0.02999202],"study_design_scores_gemma":[0.00001553244,0.00007540695,0.0002050579,0.000003890591,0.00000564187,0.0000203085,0.00003373488,0.9965554,0.001100736,0.00149094,0.0004883513,0.000004934386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8271931,0.00006088895,0.1608956,0.0002000154,0.00005486194,0.000152769,0.00006718217,0.0005363094,0.01083921],"genre_scores_gemma":[0.9400737,0.00003205872,0.05405686,0.0000669588,0.000003884371,0.0001184888,0.00007038839,0.00004671064,0.00553091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004489981,"threshold_uncertainty_score":0.008927703,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Class (philosophy); Active learning (machine learning); Streaming data; Artificial intelligence; Data mining","authors":[{"name":"Sara Khanchi","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true},{"name":"A. Nur Zincir‐Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04966372789149117,"gpt":0.2773607356695366,"spread":0.2276970077780454,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005896433,0.0007461318,0.001243713,0.001280577,0.0006555074,0.002172256,0.00240151,0.002086009,0.002238735],"category_scores_gemma":[0.01816422,0.0005564558,0.0008724257,0.001092605,0.001786804,0.002362565,0.002139017,0.002667576,0.0003184218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294337,"about_ca_system_score_gemma":0.001446657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005517547,"about_ca_topic_score_gemma":0.00330657,"domain_scores_codex":[0.9985569,0.0004290868,0.00007320054,0.0003128728,0.0004854444,0.0001424282],"domain_scores_gemma":[0.9928443,0.005118567,0.0005041333,0.0004731836,0.0007934201,0.000266359],"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":[0.00006509895,0.00006052712,0.001871195,0.00009579316,0.00006175447,0.000193243,0.0002453521,0.828169,0.002359545,0.1120608,0.001395801,0.05342178],"study_design_scores_gemma":[0.000006121186,0.00002031011,0.0002114921,0.000007572313,0.00000635297,0.00004448264,0.000007819401,0.9817222,0.0002173116,0.01727926,0.0004731065,0.000004053009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01647463,0.0003133983,0.980011,0.00044943,0.00002712846,0.00005608932,0.0001076689,0.0001839644,0.002376726],"genre_scores_gemma":[0.6646863,0.0007754804,0.3275464,0.0003273826,0.0001716451,0.0004393071,0.0004554358,0.0001748033,0.005423252],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005896433,"threshold_uncertainty_score":0.03118372,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"Set (abstract data type); Computer science; Constraint (computer-aided design); Mathematics; Programming language","authors":[{"name":"Dirk V. Arnold","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07974443942710047,"gpt":0.2980548241210974,"spread":0.2183103846939969,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003560018,0.0008764577,0.001008887,0.0004243906,0.0006294166,0.001957816,0.002580567,0.002167968,0.002565818],"category_scores_gemma":[0.01706078,0.0004317146,0.0006975948,0.0004111583,0.001606786,0.002842733,0.001760962,0.002873939,0.0004215084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005527828,"about_ca_system_score_gemma":0.001196043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001024778,"about_ca_topic_score_gemma":0.001014033,"domain_scores_codex":[0.9984292,0.0007346325,0.00009916139,0.0001630112,0.0004197386,0.0001541917],"domain_scores_gemma":[0.9915537,0.006364237,0.0004667864,0.0006644655,0.0006562729,0.0002944973],"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":[0.0003171829,0.0003017135,0.001554504,0.0002503968,0.0001347161,0.0006226077,0.0007273311,0.6664581,0.01516117,0.2268787,0.0016617,0.08593191],"study_design_scores_gemma":[0.00004341773,0.0001299542,0.00008311345,0.00003239688,0.00001790906,0.00006401023,0.00003563966,0.9706548,0.003119261,0.02375665,0.002044313,0.00001852492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04074026,0.0003232512,0.9508663,0.0009414495,0.00009751285,0.0001245373,0.00002043439,0.000202416,0.006683891],"genre_scores_gemma":[0.739153,0.0003771004,0.2560492,0.0004556706,0.00009996904,0.000323457,0.00004993142,0.0001883727,0.003303194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003560018,"threshold_uncertainty_score":0.01882744,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Robert J. Smith","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0344071226984597,"gpt":0.2658792075874358,"spread":0.2314720848889761,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007042626,0.0005527275,0.0006839034,0.0005335474,0.000357942,0.0008840545,0.001429673,0.0007834238,0.002214721],"category_scores_gemma":[0.003353948,0.0005629464,0.0005558663,0.0005408401,0.001140498,0.001710638,0.00156513,0.001784791,0.0004348957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008705661,"about_ca_system_score_gemma":0.001045456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002209514,"about_ca_topic_score_gemma":0.00456169,"domain_scores_codex":[0.9996778,0.00008438416,0.00001996166,0.00008124655,0.00007843686,0.00005816472],"domain_scores_gemma":[0.9987373,0.0006519108,0.0001261151,0.0002479503,0.000118443,0.0001183833],"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":[0.00006177803,0.0002011755,0.002690175,0.0001113952,0.00005553613,0.00009833576,0.0002450778,0.8701539,0.01220348,0.02386008,0.000894147,0.08942495],"study_design_scores_gemma":[0.000008990351,0.00003481921,0.0001350165,0.000006281541,0.000009584035,0.00001281806,0.00002366418,0.9848933,0.001024675,0.01329019,0.0005565765,0.000004086455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3642,0.0003616975,0.6275266,0.0005295379,0.00004251968,0.0001328182,0.00006999295,0.001396663,0.005740204],"genre_scores_gemma":[0.7077866,0.0002603563,0.2884319,0.0002091797,0.0000281722,0.0002449726,0.0001878599,0.0002131372,0.002637851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002214721,"threshold_uncertainty_score":0.007409036,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"Surrogate model; Context (archaeology); Computer science; Gaussian process; Mathematical optimization; Black box; Process (computing); Function (biology); Gaussian; Mathematics; Artificial intelligence; Physics","authors":[{"name":"Jingyun Yang","is_ca":true},{"name":"Dirk V. Arnold","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01756941332502398,"gpt":0.2230625698390649,"spread":0.205493156514041,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001588475,0.0006161427,0.000756559,0.0003332082,0.0002185184,0.0007812964,0.0009319236,0.001571004,0.001920661],"category_scores_gemma":[0.004041942,0.0002629105,0.0006553431,0.0004685174,0.0006206204,0.001046223,0.001246396,0.001004015,0.0003444315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691099,"about_ca_system_score_gemma":0.000640635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007602677,"about_ca_topic_score_gemma":0.001004105,"domain_scores_codex":[0.9994485,0.0002642707,0.00002200137,0.00006087741,0.0001618377,0.00004256946],"domain_scores_gemma":[0.9984614,0.0009952622,0.0001195028,0.0002077169,0.000164197,0.00005187274],"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":[0.00005923251,0.00004878777,0.0004180056,0.00004507203,0.00002250448,0.00007268241,0.00003736998,0.9570458,0.005357489,0.01643086,0.0003075523,0.02015453],"study_design_scores_gemma":[0.000005904403,0.00003638574,0.0000551222,0.00000311247,0.000002974705,0.00002008141,0.000002268219,0.996949,0.0008089865,0.001898824,0.0002134751,0.000003850567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05612877,0.0001360817,0.9378906,0.0002443193,0.0000350665,0.00003900495,0.000034414,0.0002232818,0.005268495],"genre_scores_gemma":[0.7122326,0.0001031033,0.2833808,0.0001656665,0.00002144927,0.00008574024,0.00008691688,0.00007046507,0.003853125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001920661,"threshold_uncertainty_score":0.008400798,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Oswin Krause","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01222331257450439,"gpt":0.2431492450212432,"spread":0.2309259324467388,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439643,0.0008912303,0.001054268,0.0004620773,0.0004499367,0.0009291609,0.00134319,0.001087037,0.001246324],"category_scores_gemma":[0.005169752,0.0004523779,0.0004939297,0.000405214,0.0008985645,0.0008751083,0.001286509,0.001047992,0.0003897758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008518919,"about_ca_system_score_gemma":0.0007208143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002227022,"about_ca_topic_score_gemma":0.001826168,"domain_scores_codex":[0.9994048,0.0002196956,0.00003276642,0.0001048489,0.0001822483,0.00005561612],"domain_scores_gemma":[0.9982697,0.001098859,0.0001506875,0.000175299,0.0002352734,0.00007023087],"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":[0.00002691242,0.00002530979,0.000290006,0.00002533353,0.0000248388,0.00004491913,0.00004068374,0.9648784,0.001610033,0.01126177,0.0003865839,0.02138532],"study_design_scores_gemma":[0.000003896152,0.0000117591,0.00003247978,0.000002035337,0.000002414948,0.000007606154,0.000002237811,0.9972511,0.000243446,0.002276639,0.000163898,0.000002506522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02076866,0.0001157777,0.9766167,0.0001081804,0.00002351936,0.00005504433,0.00001604392,0.0003601042,0.001935928],"genre_scores_gemma":[0.8125166,0.0001354525,0.1830097,0.000153558,0.00002852949,0.0002268304,0.00007735073,0.0001309095,0.003721089],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002227022,"threshold_uncertainty_score":0.007613659,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"Integer (computer science); Mathematical optimization; Integer programming; Computer science; Mathematics; Programming language","authors":[{"name":"Yuan Hong","is_ca":true},{"name":"Dirk V. Arnold","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02312400641814812,"gpt":0.2457798453234576,"spread":0.2226558389053094,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001709325,0.0009395432,0.001110867,0.000452495,0.000443668,0.001368551,0.001179343,0.001423182,0.002373661],"category_scores_gemma":[0.0045372,0.0006491851,0.000810199,0.0006709619,0.0008544055,0.001069058,0.001460538,0.00162242,0.0003277842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005592019,"about_ca_system_score_gemma":0.0007418013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001319969,"about_ca_topic_score_gemma":0.001262549,"domain_scores_codex":[0.9992162,0.0003710988,0.00003066965,0.0001091643,0.0001931738,0.00007966338],"domain_scores_gemma":[0.9976102,0.001818698,0.0001694584,0.0001393447,0.0001898329,0.00007247621],"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":[0.00003422303,0.0000372991,0.0002460825,0.00004602368,0.00002771743,0.00006989933,0.00004180748,0.9449457,0.001253149,0.02915021,0.0003884337,0.02375955],"study_design_scores_gemma":[0.00001080568,0.00001201252,0.00002450009,0.000005884537,0.00000423577,0.00001012336,0.000003549434,0.9936051,0.0002954411,0.005490495,0.000534925,0.000002954715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01775948,0.0002452651,0.9769613,0.0001642917,0.00004496617,0.00004457932,0.00003129579,0.0001407209,0.00460801],"genre_scores_gemma":[0.3771131,0.0002687413,0.6170102,0.0001909176,0.00003676565,0.0003050764,0.0001158077,0.00008745746,0.004871904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002373661,"threshold_uncertainty_score":0.009039879,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Concordia University","funders":"","keywords":"Computer science","authors":[{"name":"Mohamed Aymen Saied","is_ca":true},{"name":"Houari Sahraoui","is_ca":true},{"name":"Edouard Batot","is_ca":true},{"name":"Michalis Famelis","is_ca":true},{"name":"Pierre-Olivier Talbot","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03585907948393557,"gpt":0.2650376949175621,"spread":0.2291786154336266,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002019569,0.001323602,0.0007421224,0.002057615,0.0008089808,0.001646677,0.002135145,0.001672359,0.001398607],"category_scores_gemma":[0.01244667,0.0009390662,0.001725139,0.001813312,0.001291808,0.001923452,0.002105232,0.002565017,0.000776841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008241605,"about_ca_system_score_gemma":0.002781487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006427953,"about_ca_topic_score_gemma":0.006609899,"domain_scores_codex":[0.9975945,0.0005399918,0.0001636453,0.0006059438,0.0008871091,0.0002086562],"domain_scores_gemma":[0.9924847,0.003077346,0.001159396,0.001955972,0.001167114,0.0001554527],"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":[0.0002711437,0.00060592,0.02546224,0.0006554351,0.0002527316,0.001197911,0.001429855,0.2253139,0.04623862,0.02753526,0.009858168,0.6611787],"study_design_scores_gemma":[0.00002688612,0.00003894149,0.001410689,0.0000509108,0.00005072267,0.0003944407,0.0001831476,0.9520376,0.01803641,0.02301573,0.004724111,0.00003051548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0371647,0.0001085188,0.9548517,0.0003222498,0.00002342179,0.0001209779,0.000287341,0.005960581,0.001160582],"genre_scores_gemma":[0.1541601,0.0001308566,0.8416221,0.0001500505,0.00001572564,0.0001272037,0.001092026,0.001085374,0.001616588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006427953,"threshold_uncertainty_score":0.01278108,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"James Alexander Hughes","is_ca":true},{"name":"Mark Daley","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.079053460547292,"gpt":0.3057255928193,"spread":0.2266721322720079,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002975226,0.001103499,0.00116085,0.003313089,0.0006355912,0.001513346,0.001225252,0.001349337,0.004603231],"category_scores_gemma":[0.0230039,0.0006998629,0.001204643,0.003309002,0.001234974,0.002010419,0.001935709,0.002225689,0.00172427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005541557,"about_ca_system_score_gemma":0.001348762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774013,"about_ca_topic_score_gemma":0.003673799,"domain_scores_codex":[0.9986472,0.0006205026,0.00008229292,0.0002863021,0.0002619337,0.0001018299],"domain_scores_gemma":[0.9905254,0.007725765,0.0007551089,0.0005670494,0.0003117333,0.0001149208],"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":[0.0003373155,0.0002252279,0.0156544,0.0003862976,0.0003495226,0.001007468,0.0004678742,0.5034742,0.01259003,0.02868498,0.004854125,0.4319685],"study_design_scores_gemma":[0.000008124211,0.00002533755,0.001001637,0.00001217659,0.00001155687,0.00007292938,0.00003910059,0.9794238,0.000893895,0.01801381,0.0004833909,0.00001418026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06196005,0.0004326896,0.9334336,0.001053502,0.00002811662,0.00004891949,0.0003411337,0.001460112,0.001241783],"genre_scores_gemma":[0.6751553,0.0008035383,0.3187121,0.0002537647,0.0001188506,0.0001968768,0.001499144,0.0003069601,0.002953477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004603231,"threshold_uncertainty_score":0.01573467,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Architecture; Pareto principle; Feature (linguistics); Mathematical optimization; Mathematics","authors":[{"name":"Ritam Guha","is_ca":false},{"name":"Wei Ao","is_ca":false},{"name":"Stephen Kelly","is_ca":true},{"name":"Vishnu Naresh Boddeti","is_ca":false},{"name":"Erik D. Goodman","is_ca":false},{"name":"Wolfgang Banzhaf","is_ca":false},{"name":"Kalyanmoy Deb","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02690733289380554,"gpt":0.2699807045307969,"spread":0.2430733716369913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001715991,0.001454723,0.00139966,0.001079111,0.0005596184,0.001327405,0.002795331,0.001660889,0.007575456],"category_scores_gemma":[0.002605531,0.0007322127,0.001478352,0.0007348373,0.0009486417,0.001173186,0.002516925,0.001889416,0.001462951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000968129,"about_ca_system_score_gemma":0.001863579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004200546,"about_ca_topic_score_gemma":0.005838765,"domain_scores_codex":[0.9992121,0.0003057976,0.00003213649,0.0001154334,0.0002431537,0.00009133606],"domain_scores_gemma":[0.999295,0.0003758309,0.00007032506,0.0000737091,0.0001350584,0.00005011093],"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":[0.00006717012,0.00007351126,0.0006106559,0.0001939449,0.00007925225,0.00005266013,0.00004025174,0.8838515,0.001480119,0.03186695,0.003493792,0.07819019],"study_design_scores_gemma":[0.00001658121,0.00003622012,0.00005294105,0.00001255697,0.000008209258,0.000009732832,0.00000703831,0.9896806,0.0002885469,0.008113909,0.001767574,0.000006128932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004784875,0.0003113697,0.988512,0.0001690407,0.00003800578,0.00007286091,0.0001239323,0.001645267,0.004342558],"genre_scores_gemma":[0.2309018,0.0003644511,0.7589591,0.0004387397,0.0000807641,0.0007108438,0.000615211,0.001033534,0.006895506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007575456,"threshold_uncertainty_score":0.0253424,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Alexandru Ianta","is_ca":true},{"name":"Ryan Amaral","is_ca":true},{"name":"Caleidgh Bayer","is_ca":true},{"name":"Robert J. Smith","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0203845929356071,"gpt":0.258709105662269,"spread":0.2383245127266619,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004013534,0.0009272197,0.0005331166,0.0009376683,0.0005932075,0.0009867746,0.001314551,0.001032569,0.002426487],"category_scores_gemma":[0.02343968,0.0002155092,0.0003056029,0.0006385919,0.0009995369,0.001532242,0.001082386,0.001457332,0.0002687853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001414798,"about_ca_system_score_gemma":0.001211096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007742818,"about_ca_topic_score_gemma":0.01199671,"domain_scores_codex":[0.998123,0.0008871703,0.0001108401,0.0002516391,0.0002982605,0.0003290011],"domain_scores_gemma":[0.9673177,0.02596503,0.001565652,0.002083499,0.00154849,0.001519684],"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":[0.001861516,0.001263095,0.01182569,0.0002907728,0.0001182599,0.000129994,0.0001087029,0.9114919,0.00455171,0.006124481,0.003435387,0.05879855],"study_design_scores_gemma":[0.0001809718,0.001397302,0.004123457,0.00004956938,0.00005442823,0.00004307122,0.0001405742,0.9823889,0.004522883,0.006208568,0.0008694576,0.00002077173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829288,0.0006289054,0.006829748,0.0005195426,0.00006277929,0.00005518169,0.0003058686,0.0006843286,0.007984968],"genre_scores_gemma":[0.991602,0.00009209328,0.0070393,0.00008141765,0.00001053517,0.00003074034,0.0003466074,0.00007659977,0.0007207352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007742818,"threshold_uncertainty_score":0.02122587,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Yuchen Liu","is_ca":false},{"name":"Sun‐Yuan Kung","is_ca":false},{"name":"David Wentzlaff","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01647507186305291,"gpt":0.2156416083807964,"spread":0.1991665365177434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002086845,0.001052384,0.0006780263,0.0009948119,0.0003570151,0.0008748432,0.001106914,0.001357388,0.001487146],"category_scores_gemma":[0.008612367,0.00040017,0.0006101999,0.0005092198,0.0008762613,0.001356676,0.001330691,0.001412416,0.0003286698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111245,"about_ca_system_score_gemma":0.000827283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009428127,"about_ca_topic_score_gemma":0.001216687,"domain_scores_codex":[0.9992494,0.0002390887,0.000048593,0.0001185691,0.0002593395,0.00008502577],"domain_scores_gemma":[0.9980556,0.001140247,0.0001611683,0.0002025853,0.000377255,0.0000630825],"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":[0.00005539001,0.00007477355,0.001207532,0.00008213624,0.00003741116,0.0001522713,0.0001220912,0.8432087,0.00960298,0.0289927,0.001255448,0.1152086],"study_design_scores_gemma":[0.000009992536,0.00003678333,0.0001288198,0.00001792661,0.00001130312,0.00003618358,0.00001280494,0.987915,0.002734666,0.008180844,0.000910408,0.000005180872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09624294,0.0003813947,0.8971895,0.0002418379,0.00005006399,0.00008547058,0.00005552185,0.0007254503,0.005027816],"genre_scores_gemma":[0.6856552,0.0003502499,0.3093628,0.0002037233,0.00002981278,0.0003862791,0.00018869,0.0003424322,0.003480795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002086845,"threshold_uncertainty_score":0.01103646,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"Style (visual arts); Computer science; Telecommunications; Visual arts; Art","authors":[{"name":"Caleidgh Bayer","is_ca":true},{"name":"Robert J. Smith","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01847430744442519,"gpt":0.2825160090607089,"spread":0.2640417016162837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002665343,0.0002610947,0.0002353989,0.0002901393,0.0004842336,0.0008446786,0.0006912098,0.0005692906,0.003598053],"category_scores_gemma":[0.001524945,0.0002160462,0.0003485243,0.0001662717,0.001148793,0.001336604,0.001058401,0.0006864792,0.0004963516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007258215,"about_ca_system_score_gemma":0.0006892837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00292297,"about_ca_topic_score_gemma":0.004722327,"domain_scores_codex":[0.9998976,0.00002727609,0.000004247677,0.00003075772,0.00001502475,0.00002500199],"domain_scores_gemma":[0.9996955,0.00009025355,0.00004734564,0.00007453965,0.00003366478,0.00005876659],"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":[0.0001534053,0.00008991626,0.007594177,0.0001244883,0.00006533373,0.0005109482,0.001248569,0.6029466,0.03846228,0.2994,0.002739823,0.04666439],"study_design_scores_gemma":[0.00001283102,0.00005553018,0.0008687339,0.00001529208,0.00001137861,0.00009516974,0.0002083833,0.9122645,0.004665277,0.0780892,0.003688733,0.00002495786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3970116,0.00008236588,0.5848337,0.0004223631,0.00004215384,0.00005186098,0.0001394603,0.001020094,0.01639649],"genre_scores_gemma":[0.9329198,0.00005997324,0.06202526,0.00005655854,0.000003610023,0.00006845215,0.00009956367,0.0001585292,0.004608197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003598053,"threshold_uncertainty_score":0.01203674,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Alexander Ivanov","is_ca":true},{"name":"Wesley Willett","is_ca":true},{"name":"Christian Jacob","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01511328012465476,"gpt":0.2129976307273373,"spread":0.1978843506026825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007505453,0.0009845706,0.0005941089,0.0006061126,0.0004448621,0.001805962,0.002264273,0.001009907,0.0487832],"category_scores_gemma":[0.002271908,0.0003952244,0.0009905631,0.0003295681,0.0005738411,0.002467947,0.003847345,0.001226558,0.01007371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003664246,"about_ca_system_score_gemma":0.0004051891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001225574,"about_ca_topic_score_gemma":0.002080292,"domain_scores_codex":[0.999529,0.0001067888,0.00002322356,0.00009759623,0.0001754268,0.00006796494],"domain_scores_gemma":[0.9995442,0.0001819552,0.00001564871,0.0001193641,0.00006768002,0.0000711276],"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":[0.001666079,0.0003888864,0.003275147,0.0016507,0.0001783999,0.00127748,0.002077512,0.03632778,0.06241605,0.1492208,0.203264,0.5382571],"study_design_scores_gemma":[0.0002251844,0.0002969723,0.0012917,0.0002287844,0.00005061899,0.0009377074,0.0002330504,0.1616025,0.0219974,0.05517538,0.7578389,0.000121781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0162592,0.001032977,0.8028842,0.0004280425,0.0002474694,0.0002743241,0.003147369,0.1013735,0.07435296],"genre_scores_gemma":[0.2658233,0.001243565,0.5997361,0.001112236,0.0001222608,0.0009877018,0.01521337,0.01806536,0.09769616],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0487832,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"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,"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":"Lagrangian; Augmented Lagrangian method; Computer science; Applied mathematics; Mathematics; Algorithm","authors":[{"name":"Jeremy Porter","is_ca":true},{"name":"Dirk V. Arnold","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01818831382712729,"gpt":0.2586653392415316,"spread":0.2404770254144043,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008385233,0.001088501,0.0009447613,0.0007031765,0.0003881266,0.001507721,0.001570217,0.001333814,0.007172256],"category_scores_gemma":[0.002502918,0.0005267108,0.0006068815,0.0006742371,0.000771437,0.001078019,0.001927904,0.001092869,0.001732468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005408476,"about_ca_system_score_gemma":0.0008839049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391874,"about_ca_topic_score_gemma":0.00182751,"domain_scores_codex":[0.999464,0.0001940423,0.00002163666,0.0000680096,0.0002046991,0.00004758905],"domain_scores_gemma":[0.9991992,0.0003673249,0.00008843916,0.0001288301,0.0001748842,0.00004136273],"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":[0.00006923722,0.00008653489,0.0004069033,0.0001968794,0.0000935155,0.0001412548,0.0001285038,0.6851078,0.002980866,0.1566127,0.004218949,0.1499568],"study_design_scores_gemma":[0.00002849787,0.00005303534,0.00006388053,0.00002122153,0.00001251613,0.00004640299,0.00001044937,0.9724256,0.0006204949,0.02015173,0.006556405,0.000009857218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00665809,0.0003896924,0.9693379,0.000155865,0.0001085493,0.00009268177,0.00006229444,0.0003278918,0.02286697],"genre_scores_gemma":[0.3884017,0.0009230737,0.5697808,0.0004015422,0.0001261095,0.0007502401,0.0002797876,0.0002384167,0.0390984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007172256,"threshold_uncertainty_score":0.02399361,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}