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Record W2048856306 · doi:10.2136/sssaj2014.01.0037

Phosphorus Transformations from Reclaimed Wastewater to Irrigated Soil: A <sup>31</sup> P NMR Study

2014· article· en· W2048856306 on OpenAlexaff
Iris Zohar, Barbara J. Cade‐Menun, Adina Paytan, Avi Shaviv

Bibliographic record

VenueSoil Science Society of America Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsAgriculture and Agri-Food Canada
FundersUnited States - Israel Binational Agricultural Research and Development Fund
KeywordsSoil waterPhosphorusLabilityWastewaterIrrigationReclaimed waterPolyphosphateFertilizerEnvironmental chemistryChemistryNutrientAgronomyEnvironmental scienceEnvironmental engineeringPhosphateSoil scienceBiology

Abstract

fetched live from OpenAlex

Irrigation of soils with reclaimed wastewater (RW) is a common practice in arid regions, but may pose an environmental threat if labile phosphorus (P) forms accumulate at the soil surface. Soil P lability can be affected by P forms in the applied RW and by P composition and distribution in the soil. Solution 31 P nuclear magnetic resonance (NMR) spectroscopy was employed to identify P forms in RW solutions, in whole soil extracts and in fractionated soil P pools in agricultural soils (maize crop, Acre, Israel) irrigated with the examined RW or with freshwater (FW) and a chemical fertilizer. The RW was rich with total P (P T ) and molybdate‐reactive P (MRP), consistent with high concentrations of MRP in the RW‐irrigated soil. Identified compounds and compound classes in the RW and in the soils include orthophosphate, polyphosphate, orthophosphate monoesters, and orthophosphate diesters. However, there was a shift in P compound classes from the RW to the RW‐irrigated soil; although the water sources were different, P forms in the soils of the different treatments were similar. The possible factors that might control this change are discussed, including biological and geochemical P recycling and crop inputs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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 designSimulation or modeling
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

Citations10
Published2014
Admission routes1
Has abstractyes

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