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Record W2090820048 · doi:10.2136/sssaj2000.6451669x

Ammonium Adsorption and Desorption in Sandy Soils

2000· article· en· W2090820048 on OpenAlexaff
F. L. Wang, A. K. Alva

Bibliographic record

VenueSoil Science Society of America Journal · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Saskatchewan
FundersU.S. Department of Agriculture
KeywordsSoil waterLoamDesorptionAdsorptionLeaching (pedology)ChemistryEnvironmental chemistrySiltAmmoniumFertilizerSoil scienceMineralogyEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Leaching of fertilizer N in sandy soils is both an agricultural and environmental concern and depends, in part, on the N holding capacity of the soils in the vadose zone. We examined NH 4 adsorption and desorption in surface (0–30 cm) and subsurface (30–60 cm) samples of Wabasso (sandy, siliceous, hyperthermic Alfic Alaquod) and Candler (uncoated hyperthermic, Typic Quartzipsamment) sands using a batch technique. Samples of a 90‐ to 100‐cm depth horizon of the Wabasso sand were also used in the study. The NH 4 quantity–intensity (Q/I) relations showed that the potential buffering capacity (PBC) of the soils ranged from 0.26 (Wabasso, 30–60 cm depth) to 3.9 (Wabasso, 90–100 cm depth) cmol c kg −1 M −1/2 Labile NH 4 , as determined from the Q/I, was 4.9 × 10 −3 (Candler, subsurface) to 13.8 × 10 −3 (Wabasso, surface) cmol c kg −1 Positive linear relationships were observed between organic C content and Q/I plot parameters (potential NH 4 buffering capacity and labile NH 4 ) of all soil samples except in the Wabasso 90‐ to 100‐cm depth horizon. Although NH 4 adsorption capacity of the surface soils was greater than that of the subsurface soils, desorption was greater from the former soils than that from the latter. This study clearly demonstrated that the potential NH 4 buffering capacity and labile NH 4 for the sandy soils studied were much lower than those for clay and silt loam soils.

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 score0.303
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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

Citations103
Published2000
Admission routes1
Has abstractyes

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