{"id":"W4401616322","doi":"10.3390/f15081437","title":"Improved Branch Volume Prediction of Multi-Stemmed Shrubs: Implications in Shrub Volume Inventory and Fuel Characterization","year":2024,"lang":"en","type":"article","venue":"Forests","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Shrub; Volume (thermodynamics); Characterization (materials science); Environmental science; Materials science; Botany; Biology; Nanotechnology; Thermodynamics; Physics","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":[0.001342414,0.0009001315,0.0006355346,0.001646738,0.0002166289,0.001059257,0.0008241048,0.0003672981,0.0006631574],"category_scores_gemma":[0.002294717,0.0003087046,0.0005853086,0.00125878,0.0001660276,0.001213585,0.0005023034,0.0004075387,0.000392264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003438547,"about_ca_system_score_gemma":0.0003828233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00559499,"about_ca_topic_score_gemma":0.01099185,"domain_scores_codex":[0.9996074,0.00009414543,0.00001965496,0.0001553314,0.00008757186,0.00003592821],"domain_scores_gemma":[0.9987429,0.0005588653,0.0001969209,0.0001352743,0.0002984949,0.00006764472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003113628,0.0001933913,0.396374,0.0002694761,0.0002919994,0.0002006597,0.0002738963,0.3186519,0.0347097,0.001220034,0.001870648,0.2456329],"study_design_scores_gemma":[0.00001242162,0.00005720228,0.09034488,0.0000360337,0.00005858041,0.00009581425,0.00006473533,0.8993821,0.007428068,0.001230462,0.001240881,0.00004874509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7265307,0.001573599,0.2636879,0.0001170396,0.00005758451,0.00005002166,0.002684683,0.002722332,0.002576086],"genre_scores_gemma":[0.9102186,0.0002711898,0.08662673,0.00004049005,0.00002919812,0.00003973917,0.002208719,0.0001735438,0.00039182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00559499,"threshold_uncertainty_score":0.01112485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065222833504195,"score_gpt":0.221900047485829,"score_spread":0.2112478191507871,"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."}}