{"id":"W4409037224","doi":"10.22541/essoar.174349993.30198378/v1","title":"Inter–annual Variability of Hydrological Parameters Improves Simulation of Annual Gross Primary Production","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"International Max Planck Research School for global Biogeochemical Cycles; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Swedish National Space Agency","keywords":"Production (economics); Environmental science; Primary (astronomy); Climatology; Economics; Geology; 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":[0.0009236139,0.0004744756,0.0004032241,0.0003164849,0.0002993411,0.0006847846,0.000554197,0.0005586498,0.0005380323],"category_scores_gemma":[0.001827118,0.000289786,0.0006924239,0.0004000255,0.0003113846,0.0007976943,0.0005734086,0.0007737709,0.00008763168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004982632,"about_ca_system_score_gemma":0.0007718969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.012486,"about_ca_topic_score_gemma":0.009949652,"domain_scores_codex":[0.9997056,0.00009021411,0.00002246065,0.0001026806,0.00003498269,0.00004403801],"domain_scores_gemma":[0.9993302,0.0003007949,0.0001112144,0.0001377347,0.00006512365,0.00005495798],"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":[0.00003740435,0.00006045435,0.007874683,0.000009153026,0.0000396686,0.00001431719,0.00001948529,0.9867046,0.00157759,0.0002851292,0.00009727261,0.003280164],"study_design_scores_gemma":[0.000006237598,0.00001608246,0.002963322,0.00000153246,0.000009367674,0.000002953254,0.000007222172,0.9960685,0.0006115812,0.0002009448,0.0001069902,0.000005194713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733692,0.00007171193,0.02434692,0.0001061201,0.00001830826,0.00001267384,0.0003024326,0.0002528824,0.001519762],"genre_scores_gemma":[0.9965894,0.00001973287,0.003098358,0.00001023425,0.00000364164,0.000009105406,0.0001468672,0.00002182778,0.0001007817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.012486,"threshold_uncertainty_score":0.02482659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186056200088715,"score_gpt":0.2724008546780864,"score_spread":0.2405402926771993,"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."}}