{"id":"W4281255639","doi":"10.1111/geb.13531","title":"Water table depth modulates productivity and biomass across Amazonian forests","year":2022,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Environment Research Council; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Instituto Nacional de Pesquisas da Amazônia; Newton Fund; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Sight Research UK; ASCRS Research Foundation","keywords":"Edaphic; Water table; Environmental science; Biomass (ecology); Amazonian; Productivity; Hydrology (agriculture); Ecology; Water use; Groundwater; Amazon rainforest; Soil water; Agroforestry; Soil science; Geology; 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":[0.0002073251,0.0001309651,0.0001526826,0.0003139198,0.0002025261,0.000569353,0.000233358,0.000163155,0.001391388],"category_scores_gemma":[0.0008607325,0.0001510166,0.000194338,0.0004763862,0.0003728637,0.0004046751,0.0003468559,0.0001365729,0.00008823886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00045874,"about_ca_system_score_gemma":0.0002325039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01892067,"about_ca_topic_score_gemma":0.02072595,"domain_scores_codex":[0.9998931,0.00002348557,0.000006411296,0.00003510918,0.00001245695,0.00002936757],"domain_scores_gemma":[0.999536,0.0001477878,0.0001637005,0.00003061575,0.00004888027,0.00007302084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009035206,0.00002540532,0.9852267,0.0000395302,0.00009196563,0.00008824021,0.0002902308,0.002202186,0.007575749,0.000259158,0.00009290827,0.004017635],"study_design_scores_gemma":[0.00000371328,0.00001428347,0.9971597,0.00000407071,0.00001218419,0.00002535411,0.0001515502,0.00219182,0.0001658591,0.0001307451,0.0001379302,0.000002856647],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994509,0.00006627951,0.00011526,0.00002321222,6.364422e-7,0.000001269512,0.0001219441,0.000005095237,0.0002153635],"genre_scores_gemma":[0.999871,0.00001845132,0.00002871691,0.000002552799,6.494969e-7,7.485544e-7,0.00004242927,9.115751e-7,0.0000345831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01892067,"threshold_uncertainty_score":0.03762108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003932740766690052,"score_gpt":0.1974960249107308,"score_spread":0.1935632841440407,"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."}}