{"id":"W2293074099","doi":"","title":"What a Sunflower Can Teach a Robot? - Efficient Robot Queuing by Reverse Phyllotaxis.","year":2010,"lang":"en","type":"article","venue":"Artificial Life","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Computer science; Controller (irrigation); Queue; Queueing theory; Phyllotaxis; Simple (philosophy); Control theory (sociology); Interference (communication); Mathematical optimization; Distributed computing; Artificial intelligence; Mathematics; Control (management); Computer network","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":[0.0003435293,0.0001860155,0.0001475413,0.000137454,0.0002950547,0.0003808534,0.0004658199,0.0003331529,0.001777089],"category_scores_gemma":[0.001233437,0.0000955842,0.0001688111,0.00009688269,0.0004884248,0.0008714541,0.0003125937,0.0003365828,0.0002192582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003441208,"about_ca_system_score_gemma":0.0003044205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006270198,"about_ca_topic_score_gemma":0.0008063372,"domain_scores_codex":[0.9998989,0.00002623315,0.000004012791,0.00002811621,0.00002645645,0.00001617334],"domain_scores_gemma":[0.999593,0.0001900187,0.00007850018,0.00004717682,0.00004578752,0.00004554795],"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":[0.000850871,0.0002678945,0.005991159,0.0003819398,0.00009620066,0.0004441584,0.0007297823,0.2119871,0.2247619,0.2539545,0.006434701,0.2940998],"study_design_scores_gemma":[0.00009881723,0.0005836505,0.002948435,0.00005044616,0.00005335921,0.0003443722,0.0001368012,0.7657663,0.1166755,0.08295684,0.03032775,0.00005763347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1974033,0.000454026,0.7904884,0.0008496061,0.00009739319,0.00004532938,0.0000543379,0.001410459,0.009197176],"genre_scores_gemma":[0.8177459,0.0002258751,0.1777741,0.0002018408,0.00002368067,0.00004877751,0.00003478015,0.00005732621,0.003887764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001777089,"threshold_uncertainty_score":0.005944908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271517958026363,"score_gpt":0.2342933924876934,"score_spread":0.2215782129074298,"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."}}