{"id":"W2317515852","doi":"10.1002/2016gl068240","title":"The dry season intensity as a key driver of NPP trends","year":2016,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Environment Research Council; Consejo Nacional de Ciencia y Tecnología; University of Exeter; Consejo Estatal de Ciencia, Tecnología e Innovación; European Commission; Australian Research Council; Sight Research UK","keywords":"Environmental science; Dry season; Primary production; Biomass (ecology); Growing season; Vegetation (pathology); Precipitation; Ecosystem; Productivity; Wet season; Carbon cycle; Climate change; Atmospheric sciences; Hydrology (agriculture); Agronomy; Ecology; Geography; Meteorology; Biology","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":[0.0002424396,0.0001195466,0.0001890536,0.0004090622,0.0001491331,0.0005304167,0.0001412246,0.0001262336,0.001945596],"category_scores_gemma":[0.0006065342,0.00006823026,0.0002175674,0.0004654777,0.0002132904,0.0003835109,0.0002540454,0.0002064664,0.0002300456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374758,"about_ca_system_score_gemma":0.0001384027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003419142,"about_ca_topic_score_gemma":0.003258921,"domain_scores_codex":[0.9999381,0.000009751991,0.000004930125,0.00002011122,0.00001136118,0.00001576953],"domain_scores_gemma":[0.9995787,0.0001295533,0.0001392081,0.00002796256,0.00005636706,0.00006831045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001346569,0.00003271467,0.9677401,0.0000376044,0.0001098339,0.0001046362,0.0000919929,0.001756752,0.02188782,0.0002105907,0.0002672133,0.007626243],"study_design_scores_gemma":[0.000001820138,0.00001421626,0.9969121,0.000001792004,0.00001608429,0.00002559039,0.00005329687,0.001873725,0.0005809102,0.00009675846,0.0004209177,0.000002778093],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99821,0.0001096171,0.0004005581,0.00003892102,0.00000324683,0.000002749621,0.0003664274,0.000009961665,0.0008586568],"genre_scores_gemma":[0.9994254,0.00003669712,0.00008112215,0.00001085368,0.000007842951,0.000002199741,0.0002673198,0.000005762775,0.0001627235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003419142,"threshold_uncertainty_score":0.006798446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508143218664827,"score_gpt":0.2639212231433867,"score_spread":0.2488397909567384,"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."}}