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Record W4390014801 · doi:10.11648/j.sd.20231106.12

Evapotranspiration Characteristics of Forest and Grass Vegetation and Its Response to Environmental Factors on the Loess Plateau

2023· article· en· W4390014801 on OpenAlexaff
Fu Wang, Sha Xiao Yan, He Qian, Qiang Zhao, Han Fen, He Zhang

Bibliographic record

VenueScience Discovery · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicForest, Soil, and Plant Ecology in China
Canadian institutionsResponse Biomedical (Canada)
Fundersnot available
KeywordsLoess plateauEvapotranspirationVegetation (pathology)Environmental scienceLoessAgroforestryForestryHydrology (agriculture)Physical geographyGeographySoil scienceGeologyEcologyGeomorphologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

In this paper, the evapotranspiration characteristics and water consumption rules of different types of vegetation in tree forest, shrub forest and grassland in the Loess Plateau of Longzhong region were analyzed, and the response relationship between vegetation evapotranspiration and meteorological factors was established by regression analysis method. The results showed that: (1) The monthly variation of Ea values of vegetation in this region showed an obvious inverted U-shaped bimodal variation, which the lowest Ea values were found in the germination stage of vegetation in early April and the dormancy stage of leaf litter in October, and the Ea values increased greatly in May and June with the enhancement of vegetation transpiration and soil evaporation by an average increase of 74.31%. The Ea values of all types of vegetation in this region reached the maximum value in July and August and began to decline significantly from September by an average decrease of 7.42mm and an average decrease of 24.31%. In October, the Ea values of vegetation rapidly decreased to the level of early April by an average decrease of 15.67mm and a decrease of 51.52% compared with August. (2) The difference in the perspective of spatial distribution of evapotranspiration water consumption of vegetation in this region was mainly manifested in the difference between east and west but a little difference between north and south. In Pingliang City, vegetation evapotranspiration water consumption in the east was higher than that in the west, the south was higher than that in the north, and the Jinghe River basin was higher than that in the Hulu River basin. The average difference of the total evapotranspiration water consumption of the same type of vegetation during the growth period was 19.43mm from east to west, 2.2mm from north to south, and 19.43mm from basin to basin. (3) Radiation and precipitation were the main influencing factors of Ea. Among the independent variable factors affecting evapotranspiration of forest and shrub forest, the difference of significance from large to small was the average monthly temperature, average monthly precipitation, average monthly wind speed and average monthly precipitation days. The effect of monthly mean wind speed on grassland Ea was relatively small.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.136
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.024
GPT teacher head0.280
Teacher spread0.256 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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