{"id":"W1996503485","doi":"10.1002/cav.26","title":"Physically‐based simulation of plant leaf growth","year":2004,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Plant growth; Process (computing); Computer science; Growth rate; Computer simulation; Set (abstract data type); Compressibility; Animation; Biological system; Mechanics; Simulation; Mathematics; Geometry; Botany; Physics; Biology; Computer graphics (images)","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":[0.000185213,0.0003450431,0.0004517344,0.0002922899,0.0003666523,0.0006777399,0.0007659905,0.001079227,0.002840983],"category_scores_gemma":[0.0005555991,0.0002476006,0.0004365218,0.00027507,0.0005476223,0.0003931344,0.0006038322,0.0006269416,0.0002564295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000629066,"about_ca_system_score_gemma":0.0004904227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004479407,"about_ca_topic_score_gemma":0.002030027,"domain_scores_codex":[0.9999294,0.00001932003,0.000003193804,0.00001000389,0.0000263941,0.0000116742],"domain_scores_gemma":[0.9997537,0.0001397564,0.00002175764,0.00002219293,0.00002947025,0.00003313855],"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":[0.00002585245,0.00003605272,0.0003626306,0.00002301515,0.000007763254,0.00006800025,0.00004481282,0.9911186,0.004378119,0.002358805,0.0001717054,0.00140463],"study_design_scores_gemma":[0.00001218475,0.00001762499,0.0001937213,0.000003346396,0.000002325088,0.00001364043,0.00000717191,0.9981081,0.0006214465,0.000470018,0.0005454824,0.000004843491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.764827,0.0004769431,0.1979347,0.0006887786,0.0001568428,0.0001602529,0.0006773749,0.0009771986,0.0341009],"genre_scores_gemma":[0.9775434,0.0001890957,0.01740953,0.00005988254,0.00001325561,0.000143968,0.0002998648,0.00004850602,0.004292415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004479407,"threshold_uncertainty_score":0.00950408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461254720692339,"score_gpt":0.2155773819349752,"score_spread":0.2009648347280518,"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."}}