{"id":"W1603156618","doi":"10.1007/978-3-642-01181-8_26","title":"Beneficial Preadaptation in the Evolution of a 2D Agent Control System with Genetic Programming","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Genetic programming; Computer science; Task (project management); Genetic algorithm; Control (management); Artificial intelligence; Simple (philosophy); Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006199106,0.0002634016,0.0002964892,0.0004193861,0.0001851593,0.0001689082,0.001674572,0.0001349325,7.265357e-7],"category_scores_gemma":[0.00001128898,0.0001884592,0.00006390794,0.0007747173,0.0002745708,0.0002573665,0.0001177581,0.0003323662,0.000003008741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004967979,"about_ca_system_score_gemma":0.0004817566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001333958,"about_ca_topic_score_gemma":0.0002760187,"domain_scores_codex":[0.9975784,0.00005064144,0.0004583207,0.0007359643,0.0008340798,0.0003425275],"domain_scores_gemma":[0.9984698,0.0001811961,0.0003248507,0.0007509615,0.0002244322,0.00004874922],"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":[0.000007743924,0.00006915059,0.00008358797,0.00004267977,0.000005995536,0.00002217513,0.001096541,0.2770298,0.00003775882,0.1092746,0.000001310317,0.6123287],"study_design_scores_gemma":[0.0003407887,0.0003361408,0.005290859,0.0004120218,0.00001286321,0.0001004823,0.000004143538,0.9735035,0.00001890869,0.01957736,0.0001396962,0.0002632292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004173213,0.0004642815,0.9970541,0.0004678355,0.0001473087,0.0009486667,0.000003780258,0.00005581137,0.0004409002],"genre_scores_gemma":[0.7004236,0.00000460392,0.2992926,0.00007742023,0.0001368663,0.00004122735,0.000002238378,0.000007322507,0.00001415576],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7000062,"threshold_uncertainty_score":0.7685145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009963257023095237,"score_gpt":0.2168744367676328,"score_spread":0.2069111797445375,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}