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Record W2942876432 · doi:10.5539/jas.v11n6p209

Water Balance in a Tropical Eucalyptus plantations in the Doce River Basin, Eastern Brazil

2019· article· en· W2942876432 on OpenAlexvenueno aff
André Quintão de Almeida, A.C. Ribeiro, Fernando Palha Leite, Rodolfo Souza, Maria Isidória Silva Gonzaga, Weslei Almeida Santos

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEvapotranspirationWater balanceEucalyptusEnvironmental sciencePrecipitationHydrology (agriculture)Structural basinSoil waterDrainage basinWater useSurface waterGroundwaterEcologyGeographySoil scienceGeologyBiologyEnvironmental engineering

Abstract

fetched live from OpenAlex

The rapid expansion of Eucalyptus plantations in Doce river basin, eastern Brazil, by changing the grassland and the natural surface cover in the savanna ecosystem, can potentially cause significant changes to water resources of the region. Especially for the higher amount of water transpired by the trees. The objective of this work was to model the water balance in an area cultivated with clonal E. grandis × urophylla in the Doce river basin, state of Minas Gerais, Brazil. Between October 2007 and September 2010, the water balance model estimated the daily variation of available soil water as a function of the water loss via evapotranspiration. Components of the evapotranspiration process were estimated by modifying the stomatal resistance of the Penman-Monteith equation. Evapotranspiration (ET) and precipitation (P), during the three years, were 3,467 mm and 3,439 mm, respectively. The evapotranspiration/precipitation ratio (ET/P) was of 1.01. Precipitation input was approximately balanced by water losses to evapotranspiration, without significant changes to water stored in soil and groundwater.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.199
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural Science→Same topicPlant Water Relations and Carbon Dynamics→French-language works237,207→