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Record W2794850770 · doi:10.1139/cjss-2017-0111

Feldspathic sandstone addition and its impact on hydraulic properties of sandy soil

2018· article· en· W2794850770 on OpenAlexvenueno aff
Junchao Jia, Pingping Zhang, Xiaofeng Yang, Xingchang Zhang

Bibliographic record

VenueCanadian Journal of Soil Science · 2018
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsHydraulic conductivitySoil scienceSoil waterInfiltration (HVAC)Field capacityWater retentionLoessLoess plateauEnvironmental scienceHydrology (agriculture)GeologyGeotechnical engineeringMaterials scienceGeomorphology

Abstract

fetched live from OpenAlex

Feldspathic sandstone could be used as an effective conditioner to improve the physical quality of sandy soil, and increase the crop yield there. To determine the effects of feldspathic sandstone content on soil hydraulic properties in a sandy soil, the present study added 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (no sandy soil) of feldspathic sandstone to sandy soil. Changes in hydraulic parameters were investigated and the results showed addition of feldspathic sandstone increased saturated water content by 37%–61% and field capacity by 29%–44%, and decreased saturated hydraulic conductivity from 10.19 to 0.58 cm h −1 of the sandy soil. Further data analysis demonstrated that with increasing content of feldspathic sandstone, the parameter n of soil water retention curve in Van Genuchten model dropped from 1.807 to 1.333. The same decreasing trend is detected in parameter a of infiltration rate (3.841–0.703) in Kostiakov formula (i = at − b ) and parameter a 1 of wetting front (6.901–1.174) in the empirical equation (X = a 1 t b 1 ). In terms of hydraulic parameters, 40% feldspathic sandstone and 60% sandy soil, optimally matching indices of loess soil, were the best mixing ratio for sandy land restoration.

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 score0.199
Threshold uncertainty score0.371

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)0.0000.000

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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