{"id":"W3083555321","doi":"10.1139/cjfr-2020-0156","title":"Water budget fluxes in catchments under grassland and <i>Eucalyptus</i> plantations of different ages","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Evapotranspiration; Environmental science; Interception; Streamflow; Canopy interception; Hydrology (agriculture); Eucalyptus; Transpiration; Water balance; Watershed; Agroforestry; Grassland; Canopy; Throughfall; Soil water; Drainage basin; Agronomy; Ecology; Geography; Geology; Soil science","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":[0.0001768928,0.0001469244,0.0002090489,0.0004140832,0.0002890592,0.000356996,0.0001674173,0.0001804998,0.0003561572],"category_scores_gemma":[0.0003058783,0.00009384052,0.0002199104,0.0006552625,0.0002883452,0.0002949372,0.000268513,0.0001209741,0.00003546126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064545,"about_ca_system_score_gemma":0.0004296279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1098334,"about_ca_topic_score_gemma":0.1580327,"domain_scores_codex":[0.9999002,0.00001809279,0.000006582375,0.00003632654,0.00001591437,0.00002279485],"domain_scores_gemma":[0.9998655,0.00002917137,0.00004900534,0.000007785299,0.00002152187,0.00002691869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001635137,0.00007561054,0.9749487,0.00003784082,0.00005310504,0.0001017177,0.000674427,0.001208677,0.01731588,0.0001145342,0.00008959121,0.005216414],"study_design_scores_gemma":[0.000003973825,0.00001197047,0.9979824,0.00000159729,0.000009020124,0.00001845152,0.0001607031,0.001369404,0.000325522,0.00002358629,0.00009115886,0.000002279535],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996694,0.00002699574,0.0000391066,0.000007966938,3.527344e-7,0.000002583336,0.0001100534,0.000003110403,0.0001403554],"genre_scores_gemma":[0.999542,0.00002946724,0.0001399558,0.000004903733,6.269179e-7,0.000005252134,0.0002162004,0.000001733065,0.00005995361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1098334,"threshold_uncertainty_score":0.2183883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03479249228446614,"score_gpt":0.2806262515508233,"score_spread":0.2458337592663572,"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."}}