{"id":"W2806071795","doi":"10.5558/tfc2018-025","title":"Quantifying the effect of non-spatial and spatial forest stand structure on rainfall partitioning in mountain forests, Southern China","year":2018,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Throughfall; Interception; Stemflow; Environmental science; Leaf area index; Precipitation; Spatial distribution; Canopy; Canopy interception; Hydrology (agriculture); Atmospheric sciences; Soil science; Geography; Ecology; Meteorology; Soil water; Geology; Remote sensing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006299092,0.000265182,0.000193865,0.0007549321,0.0002357556,0.0003807235,0.0002057585,0.0001324641,0.0003027046],"category_scores_gemma":[0.0007600203,0.0001341927,0.0003184264,0.0007705924,0.0002294603,0.0003216015,0.0002902812,0.00008702021,0.00004586273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004574026,"about_ca_system_score_gemma":0.0004115926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01988864,"about_ca_topic_score_gemma":0.04870367,"domain_scores_codex":[0.9997045,0.00006693645,0.00003093451,0.00008467487,0.00005761468,0.00005528105],"domain_scores_gemma":[0.9995468,0.0001320503,0.0001435193,0.00004280358,0.00006706166,0.00006777993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002840065,0.000009304417,0.9929248,0.0000132336,0.00005325011,0.00006637026,0.0001735939,0.0008128459,0.002152731,0.00003284009,0.00001959758,0.003712978],"study_design_scores_gemma":[5.608642e-7,0.000009066032,0.9991902,8.697868e-7,0.000006357664,0.00001381806,0.00005209087,0.0006277232,0.00005924331,0.00001164769,0.00002741456,0.000001206531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997509,0.00003644321,0.00009755071,0.000003120759,2.889876e-7,0.000001311838,0.00003760603,0.000002173204,0.00007064033],"genre_scores_gemma":[0.9997335,0.00001865801,0.00009282262,0.000001792152,9.356464e-7,0.000002212434,0.0001072137,7.835144e-7,0.00004212357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01988864,"threshold_uncertainty_score":0.03954577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005683649079228202,"score_gpt":0.2208787270140175,"score_spread":0.2151950779347893,"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."}}