{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"0d98dbc169ed","filters":{"venue":"Journal of Artificial Evolution and Applications"}},"results":[{"id":"W2066790509","doi":"10.1155/2010/568375","title":"Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology","year":2010,"lang":"en","type":"article","venue":"Journal of Artificial Evolution and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evolvability; Evolutionary algorithm; Evolutionary computation; Human-based evolutionary computation; Computer science; Modern evolutionary synthesis; Computation; Evolutionary developmental biology; Interactive evolutionary computation; Artificial intelligence; Evolutionary programming; Evolutionary biology; Biology; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.01819622157449794,"gpt":0.2936264558826595,"spread":0.2754302343081615,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005555906,0.00009039362,0.0002361604,0.0003596414,0.00007032045,0.00001065498,0.0002687223,0.00009413085,0.000009837359],"category_scores_gemma":[0.00007326055,0.00008589899,0.00003634376,0.0007940335,0.0001907389,0.0002710055,0.0001010269,0.0002409873,0.000001175446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006587373,"about_ca_system_score_gemma":0.0002373312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006078327,"about_ca_topic_score_gemma":0.00009494636,"domain_scores_codex":[0.9986092,0.00005375248,0.0008406626,0.000205263,0.0001521587,0.0001388915],"domain_scores_gemma":[0.9988734,0.0001010648,0.0004071172,0.0002108325,0.000325771,0.00008182292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000325507,0.001197102,0.06399275,0.00003200572,0.00001765767,9.184339e-7,0.0004591495,0.0001554619,0.1130876,0.7199689,0.00007928482,0.1009767],"study_design_scores_gemma":[0.0006906974,0.0001304309,0.7882175,0.00003775845,0.00001146271,0.00006278688,0.0002213934,0.02429144,0.002786263,0.1696437,0.01370704,0.0001995514],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7430484,0.0008840868,0.2505736,0.003786593,0.0002375037,0.0006568847,0.00002564071,0.00001751104,0.0007698031],"genre_scores_gemma":[0.9357553,0.0002056443,0.06393342,0.00001356581,0.0000598972,0.00001816123,0.000002943889,0.000003465415,0.00000756981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7242247,"threshold_uncertainty_score":0.3502859,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2023373595","doi":"10.1155/2008/263108","title":"Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis","year":2007,"lang":"en","type":"article","venue":"Journal of Artificial Evolution and Applications","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Reservoir simulation; Computer science; Matching (statistics); Production (economics); Workflow; Reservoir engineering; Sampling (signal processing); Genetic programming; Simulation modeling; Process (computing); Industrial engineering; Data mining; Operations research; Statistics; Petroleum engineering; Machine learning; Engineering; Database; Geology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03128015542031523,"gpt":0.2941098999529927,"spread":0.2628297445326775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007774625,0.0001010139,0.0001982391,0.0003494592,0.0001572469,0.00003661452,0.00004709543,0.00006262986,0.000002503345],"category_scores_gemma":[0.00009484845,0.0001012862,0.00007859656,0.0003343704,0.00005966674,0.0001503697,0.0000100766,0.0001294915,1.050175e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002475171,"about_ca_system_score_gemma":0.00003186417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001063106,"about_ca_topic_score_gemma":0.00003943301,"domain_scores_codex":[0.9990777,0.00002050554,0.0004869123,0.0001228621,0.0001283767,0.0001635993],"domain_scores_gemma":[0.9993158,0.000125705,0.0001575441,0.00009591338,0.0002116215,0.00009340466],"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":[0.00001852406,0.00001075122,0.001229096,0.00006899895,0.00008535874,2.54003e-7,0.0002127468,0.9604926,0.003396758,0.0008402826,0.000007665727,0.03363696],"study_design_scores_gemma":[0.0001389437,0.00002133348,0.00130184,0.00002200202,0.0001973162,0.00002454062,0.0008265597,0.993209,0.0002953795,0.001768852,0.002079437,0.0001147819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.434301,0.0004080412,0.5649536,0.00002127202,0.00009694824,0.0001803117,0.000002224429,0.00002891419,0.000007752708],"genre_scores_gemma":[0.7418089,0.00001373199,0.2578117,0.000001769226,0.000329054,0.00001066865,0.000002970258,0.00001232857,0.000008883012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3075079,"threshold_uncertainty_score":0.4130331,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}