{"id":"W2905077346","doi":"10.1109/ictai.2018.00097","title":"Inferring Stochastic L-Systems Using a Hybrid Greedy Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Rewriting; Computer science; A priori and a posteriori; Algorithm; Context (archaeology); Theoretical computer science; Sequence (biology); Formal grammar; Greedy algorithm; Set (abstract data type); Software system; Process (computing); Software; Programming language; Artificial intelligence; Rule-based machine translation","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":[0.002205439,0.001326579,0.001215269,0.002319747,0.001129251,0.001340993,0.002131787,0.001857562,0.003027407],"category_scores_gemma":[0.008825204,0.001157507,0.001907813,0.001084986,0.001404375,0.001436226,0.002005704,0.001283507,0.0008351544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001865302,"about_ca_system_score_gemma":0.003619396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009297438,"about_ca_topic_score_gemma":0.01451759,"domain_scores_codex":[0.9983026,0.0005600578,0.0001079332,0.0005052065,0.0003417199,0.0001824749],"domain_scores_gemma":[0.9954647,0.003445148,0.0002325461,0.0003315204,0.0003996202,0.0001263648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000239265,0.0001691918,0.004971565,0.0001730974,0.0001665249,0.0003550821,0.000303566,0.7700575,0.008069178,0.02467323,0.001891917,0.1889299],"study_design_scores_gemma":[0.00001675085,0.00001879889,0.0001001152,0.000006952672,0.00001243096,0.0000313821,0.00001852425,0.9913453,0.0009010814,0.00722886,0.0003113816,0.000008384741],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02758061,0.00008445,0.968917,0.000106503,0.00001531658,0.00009771672,0.0000946861,0.002140131,0.0009635446],"genre_scores_gemma":[0.2151173,0.00005627035,0.7819895,0.0001633551,0.00002028501,0.0002417951,0.0006309091,0.0003700257,0.00141067],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009297438,"threshold_uncertainty_score":0.01848662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834496786877944,"score_gpt":0.2697280212393079,"score_spread":0.2413830533705285,"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."}}