{"id":"W2550243899","doi":"","title":"Automatic rule generation for procedural modeling of a sketched tree using genetic programming and particle swarm optimization","year":2013,"lang":"en","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Tree (set theory); Artificial intelligence; Set (abstract data type); Genetic programming; Particle swarm optimization; Machine learning; Data mining; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004318867,0.0007348077,0.0007627418,0.000735855,0.0003707703,0.0008002978,0.001088828,0.0008663243,0.002523042],"category_scores_gemma":[0.001165598,0.0004986234,0.001156082,0.0004712957,0.0005147885,0.0005272743,0.0006731363,0.0007441899,0.0004294636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799305,"about_ca_system_score_gemma":0.0008235297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003746146,"about_ca_topic_score_gemma":0.004247789,"domain_scores_codex":[0.9997634,0.00004058952,0.00001837381,0.00006236771,0.00009351259,0.00002167991],"domain_scores_gemma":[0.9995689,0.0002351552,0.00004030892,0.00006017334,0.00007386717,0.0000216282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004907009,0.00008040157,0.000944618,0.000145619,0.00004889613,0.0002398853,0.0001627563,0.8244086,0.02172108,0.009290396,0.001136921,0.1417717],"study_design_scores_gemma":[0.000005105284,0.00001092563,0.00005285839,0.00000470748,0.000007024996,0.00002678503,0.000007863504,0.9960935,0.001738762,0.001400232,0.0006481434,0.000004084792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008513155,0.00003995504,0.9893289,0.00003037828,0.00001523748,0.00007848851,0.00003687098,0.0007161877,0.001240847],"genre_scores_gemma":[0.1480924,0.0001107443,0.8495433,0.0000378261,0.00000978325,0.0002614026,0.0002225218,0.000168945,0.001553165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003746146,"threshold_uncertainty_score":0.008440375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03606921941729299,"score_gpt":0.2303493151252269,"score_spread":0.1942800957079339,"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."}}