{"id":"W1529894968","doi":"10.1109/cec.2015.7257022","title":"Flow of control in linear genetic programming","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Alternator; Computer science; Control flow; Flow (mathematics); Genetic programming; Flow control (data); Domain (mathematical analysis); Task (project management); Linear programming; String (physics); Control engineering; Artificial intelligence; Algorithm; Programming language; Mathematics; 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":[0.00009996609,0.00003001995,0.00005349088,0.00003149293,0.00001059971,0.000007515437,0.0002094577,0.0000150188,0.000002635941],"category_scores_gemma":[0.000008929438,0.00002581963,0.000013585,0.0002082265,0.00001530226,0.00007546227,0.00003558371,0.00002697488,0.00001666586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001028893,"about_ca_system_score_gemma":0.00004797271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000501402,"about_ca_topic_score_gemma":0.00001109586,"domain_scores_codex":[0.9996153,0.00001164016,0.0001099383,0.00009620634,0.00008617214,0.00008072069],"domain_scores_gemma":[0.9997012,0.0000163758,0.00002046774,0.000165898,0.00005315933,0.00004292243],"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":[0.000008345705,0.0008854201,0.03167277,0.00001930984,0.00001964145,0.00001424794,0.001502432,0.06785367,0.0006707825,0.2028962,0.002983639,0.6914735],"study_design_scores_gemma":[0.0003248962,0.00003396695,0.00500104,0.00000231173,6.716372e-7,0.0000030373,0.0000173215,0.9877085,0.00005205814,0.002606382,0.004212998,0.00003680168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005363069,0.00008520495,0.9929895,0.0006394458,0.00003925161,0.0001104273,4.947565e-7,0.00003268713,0.0007398536],"genre_scores_gemma":[0.4365568,9.797609e-7,0.5632639,0.00004663627,0.00001695583,0.00001743995,3.285049e-7,0.000001073838,0.000095863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9198548,"threshold_uncertainty_score":0.1052894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849450196504734,"score_gpt":0.2539878208490026,"score_spread":0.2354933188839553,"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."}}