{"id":"W4411330187","doi":"10.1007/s11047-025-10024-x","title":"Inductive inference of Lindenmayer systems: algorithms and computational complexity","year":2025,"lang":"en","type":"article","venue":"Natural Computing","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Theory of computation; Computer science; Algorithm; Inference; Computational complexity theory; Theoretical computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002043275,0.0001189292,0.0001847899,0.00004644238,0.000113248,0.00002691683,0.0001378641,0.0001191454,9.97942e-7],"category_scores_gemma":[0.0001625887,0.00009708813,0.00004384269,0.0001386305,0.0001585765,0.000003144871,0.000319312,0.0001773614,5.998384e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001055814,"about_ca_system_score_gemma":0.00004199333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003419771,"about_ca_topic_score_gemma":0.000002391399,"domain_scores_codex":[0.9991356,0.00008646806,0.0002454381,0.0002797301,0.00009445861,0.0001583449],"domain_scores_gemma":[0.9994481,0.0001083126,0.0001125487,0.00009440584,0.0002069798,0.00002961528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004364143,0.0004246796,0.2417518,0.000811698,0.00090135,0.00001448376,0.0005464057,0.02728263,0.3727562,0.0761744,0.001409057,0.2774909],"study_design_scores_gemma":[0.002557402,0.000723726,0.4265232,0.0005848329,0.00006667987,0.0000470347,0.0007919646,0.5303654,0.02555134,0.008646535,0.003161877,0.0009800062],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725614,0.001426158,0.02470979,0.00006188647,0.0003182237,0.0001266835,0.000005673724,0.00001661486,0.0007735608],"genre_scores_gemma":[0.9926212,0.000005538058,0.007007459,0.0001359798,0.0001152705,7.950125e-7,0.00003982741,0.000003561865,0.000070393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5030828,"threshold_uncertainty_score":0.3959139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763257918964659,"score_gpt":0.3179028382026179,"score_spread":0.2902702590129713,"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."}}