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Record W2290458533 · doi:10.2166/ws.2015.045

Soil water movement under a drip irrigation double-point source

2015· article· en· W2290458533 on OpenAlexaff
Lizhu Hou, Jie Shang, Jiangtao Liu, Haiyuan Lu, Zhiming Qi

Bibliographic record

VenueWater Science & Technology Water Supply · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsDrip irrigationWater contentEnvironmental scienceLoamIrrigationField capacityWater potentialInfiltration (HVAC)Surface irrigationSurface runoffSoil waterLeaching modelHydrology (agriculture)Soil scienceAgronomySoil fertilityMaterials scienceGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Particularly in dry regions, the scarcity of high-quality fresh water has heightened the importance of urban runoff water re-use, leading as well to the improvement of water use efficiency through the surface drip irrigation method. Given the limited research on wetting front migration under a surface drip irrigation emitter, soil water movement under a double-point-source irrigation emitter was investigated. An experimental soil bin was designed and filled with silt loam soil, and time domain reflectometry and tensiometers were used to measure soil moisture contents and soil water potential, respectively. The results show that under the conditions of 6 hours of irrigation with two drippers each delivering 1.05 L hr−1 and spaced at 45 cm, the soil moisture content of the 0–30 cm layer increased rapidly and reached 0.29 cm3 cm−3, and was greater than that in the 30–60 cm layer as irrigation proceeded. After 6 hours the irrigation was stopped, such that in the redistribution phase, soil moisture of the top layer gradually decreased, while that of the sublayer gradually increased. The results indicate that 6 hours of irrigation under given emitter flow conditions produced adequate soil moisture down to 30 cm for most shallow-rooted crops.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.105
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.022
GPT teacher head0.225
Teacher spread0.202 · 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.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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