{"id":"W2168085447","doi":"10.1007/s10710-007-9027-9","title":"Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings","year":2007,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Probabilistic logic; Genetic programming; Set (abstract data type); Implementation; Encoding (memory); Grammatical evolution; Fitness function; Coevolution; Theoretical computer science; Genetic algorithm; Artificial intelligence; Machine learning; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009389669,0.000495961,0.0003901562,0.0006441652,0.0003621159,0.0007680136,0.001726734,0.001007344,0.002094939],"category_scores_gemma":[0.003508685,0.0004576268,0.0005684128,0.0007106287,0.001212061,0.001129145,0.001958324,0.001192317,0.0003295113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006253971,"about_ca_system_score_gemma":0.0006289086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006439206,"about_ca_topic_score_gemma":0.0007808722,"domain_scores_codex":[0.9994541,0.0001903184,0.00002049595,0.0001072211,0.0001863046,0.00004149673],"domain_scores_gemma":[0.9993162,0.0003877109,0.00006125124,0.00009156248,0.0001096904,0.00003358692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000277197,0.00003586806,0.0005438827,0.0000834457,0.00003290607,0.0001486764,0.0001622455,0.2902733,0.007776427,0.6283454,0.0007275946,0.07184248],"study_design_scores_gemma":[0.00001151076,0.00003580143,0.0001157636,0.00002122417,0.00001670298,0.0001451561,0.0000178615,0.8488884,0.003001207,0.142608,0.005121373,0.00001699879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007845954,0.0001128742,0.9877824,0.0001014732,0.00002967739,0.00001334207,0.000008766329,0.0001007291,0.004004805],"genre_scores_gemma":[0.2930322,0.0003208233,0.7001842,0.0001176719,0.00003625779,0.000135659,0.00004435277,0.0001130768,0.006015882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002094939,"threshold_uncertainty_score":0.007008195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077993330042553,"score_gpt":0.2169474614803382,"score_spread":0.2061675281799127,"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."}}