{"id":"W2347121625","doi":"10.1007/978-3-319-30668-1_14","title":"Modelling Evolvability in Genetic Programming","year":2016,"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":"Memorial University of Newfoundland","funders":"","keywords":"Evolvability; Computer science; Genetic programming; A priori and a posteriori; Fitness landscape; Artificial intelligence; Genetic algorithm; Tree (set theory); Theoretical computer science; Machine learning; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006382362,0.0003591834,0.0003401431,0.0005422263,0.0001979118,0.0002540282,0.002494314,0.0002266905,0.00001296009],"category_scores_gemma":[0.00002201444,0.0003057587,0.00009464239,0.0006090151,0.0005209002,0.0005039645,0.0009007098,0.0005118346,0.00004113651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004229636,"about_ca_system_score_gemma":0.0004690966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003068971,"about_ca_topic_score_gemma":0.0000475093,"domain_scores_codex":[0.9966766,0.00002540924,0.0005201024,0.001513401,0.0006466113,0.0006178907],"domain_scores_gemma":[0.9979317,0.0002633771,0.0001709775,0.001313473,0.0001856348,0.0001347904],"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":[0.000001289313,0.0000337757,0.00006832856,0.00001444523,0.00000205242,0.00001827329,0.0002173511,0.2671588,0.00001718131,0.02604624,0.000001776021,0.7064205],"study_design_scores_gemma":[0.0001000895,0.00003291807,0.0001192918,0.0001655484,0.00000135865,0.00001778349,4.13899e-8,0.6965327,0.00003229758,0.3018697,0.0008427757,0.0002855684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001369304,0.0004417065,0.9961761,0.0009675532,0.0004075975,0.0004844289,0.000003032839,0.0001207435,0.001261965],"genre_scores_gemma":[0.08848947,0.00004117655,0.9107462,0.0001566357,0.0002712484,0.00003917983,0.000001332397,0.00001937822,0.0002353913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7061349,"threshold_uncertainty_score":0.9999394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979057997037511,"score_gpt":0.2436607517646063,"score_spread":0.2238701717942312,"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."}}