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Record W1984031987 · doi:10.2136/sssaj2003.8890

Adsorption and Recovery of Dissolved Organic Phosphorus and Nitrogen by Mixed‐Bed Ion‐Exchange Resin

2003· article· en· W1984031987 on OpenAlexafffund
Jacques L. Langlois, Dale W. Johnson, G. R. Mehuys

Bibliographic record

VenueSoil Science Society of America Journal · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdsorptionChemistryLeaching (pedology)Ion-exchange resinAmine gas treatingLeachateIon exchangeNitrogenAbsorption (acoustics)PhosphorusFire retardantChromatographyNuclear chemistryIonEnvironmental chemistryInorganic chemistryOrganic chemistryMaterials scienceSoil waterGeology

Abstract

fetched live from OpenAlex

A laboratory study was conducted to investigate the efficacy of using various mixed‐bed anion‐cation exchange resins in absorbing dissolved organic and inorganic N (DON and DIN) and P (DOP and DIP). Leachate from foliage of aspen ( Populus grandidentata ) was passed through columns containing three brands of mixed‐bed resin contained in nylon bags, with and without pretreatment of the resins by rinsing with KCl. After leaching, the resins were extracted with either 2 M KCl or 2 M HCl, and recoveries of DIN, DON, DIP, and DOP were calculated. The results showed that all brands of resin adsorbed more DIP (91–98.5%) than DOP (55–70%) and more NO 3 (87–100%) than NH 4 (0–14%) and DON (18–49%). In general, pretreating the resin significantly decreased absorption. The recovery of DIP and DOP was influenced by the pretreatment and the extracting solution whereas the recovery of DIN and DON was problematic because of the release of amine groups. Overall, the use of mixed‐bed resin seems adequate for P studies but not for N studies on a short‐time scale. Longer exposure periods are needed for studies of N so that the signal/noise (blank) ratio is higher than in this study.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.792

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 communication0.0000.000
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations41
Published2003
Admission routes2
Has abstractyes

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