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Record W4284881742 · doi:10.3390/w14142159

Soil Water Behavior of Sandy Soils under Semiarid Conditions in the Shendong Mining Area (China)

2022· article· en· W4284881742 on OpenAlexaboutno aff
Ying Liu, Yangnan Guo, Linli Long, Shaogang Lei

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsInfiltration (HVAC)Soil waterWater contentEnvironmental scienceMacroporeSoil sciencePermeameterField capacityHydrology (agriculture)Land reclamationHydraulic conductivityGeologyGeotechnical engineeringChemistryEcologyMaterials science

Abstract

fetched live from OpenAlex

The soil water behavior of sandy soils was studied under semiarid conditions in the Shendong mining area (China). The soil water content (θ) was measured under different depths and topographies using an HH2 moisture meter. The infiltration process was studied using a Guelph soil permeameter. A set of hydrodynamic variables was calculated in the laboratory. The θ of the first 20 cm was the lowest and increased with depth. The content of soil water increased from the top slope to the bottom slope. The infiltration experiments showed that the steady state infiltration rate was >40 mm h−1 in most cases. Owing to the higher contents of sand and soil macropores at the top of the slope and the top 0–20 cm of surface soil, the initial infiltration rate and steady infiltration rate were higher. The average available water capacity was 18.28%, which was consistent with the predominance of a sandy textural fraction. The results of a soil water retention curve and a rainfall simulation experiment showed that there was a low soil water retention capacity throughout the whole profile. This study contributes to the understanding of several aspects of the soil water behavior of sandy soils and provides key information for environmental management and land reclamation under semiarid conditions in the Shendong mining area.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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 designBench or experimental
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

Citations15
Published2022
Admission routes1
Has abstractyes

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