{"id":"W4302283952","doi":"10.5194/egusphere-2022-835","title":"Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Biome; Environmental science; Carbon cycle; Biomass (ecology); Temperate climate; Vegetation (pathology); Atmospheric sciences; Taiga; Temperate rainforest; Temperate forest; Ecology; Ecosystem; Climatology; Biology; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004332522,0.000884295,0.0006532905,0.0003457638,0.0003062635,0.0006506978,0.001158428,0.000900668,0.001820414],"category_scores_gemma":[0.000634276,0.0004620216,0.0008917396,0.0004694535,0.0003279155,0.0004944272,0.0004653359,0.0009436687,0.0004097703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007406863,"about_ca_system_score_gemma":0.001056871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02568555,"about_ca_topic_score_gemma":0.01720441,"domain_scores_codex":[0.999916,0.00002418633,0.000004117403,0.00001984098,0.0000187877,0.00001694526],"domain_scores_gemma":[0.9998385,0.00005779758,0.00001574189,0.00001724704,0.00004677843,0.00002396885],"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":[0.00001682203,0.00002908682,0.001246566,0.00002418932,0.00004340393,0.00002080775,0.00001036599,0.9927692,0.001104469,0.0006515816,0.0008215833,0.003261995],"study_design_scores_gemma":[0.000007315248,0.000003217336,0.0002427054,0.000002025032,0.00000377176,0.00000242644,0.000002145664,0.9990921,0.0001238428,0.00020735,0.000310334,0.000002902039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5705994,0.0008596329,0.3993303,0.0007970011,0.0003712465,0.0002070874,0.01013542,0.004469656,0.01323035],"genre_scores_gemma":[0.9236867,0.0002104611,0.06843942,0.0001452138,0.00004354959,0.0002715202,0.004740582,0.0003203293,0.002142135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02568555,"threshold_uncertainty_score":0.05107206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009073709117692993,"score_gpt":0.2292504714532084,"score_spread":0.2201767623355154,"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."}}