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Record W4223957131 · doi:10.1080/00103624.2022.2063317

Influence of Sample Pretreatment on P Speciation in Sediments Evaluated with Sequential Fractionation and P <i>K</i>-edge XANES Spectroscopy

2022· article· en· W4223957131 on OpenAlexaff
Julia Prüter, Yongfeng Hu, Peter Leinweber

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of SaskatchewanCanadian Light Source (Canada)
Fundersnot available
KeywordsFractionationXANESGenetic algorithmChemistrySpectroscopyPhosphorusSample preparationExtraction (chemistry)Environmental chemistryAtomic absorption spectroscopyAnalytical Chemistry (journal)MineralogyChromatographyEcologyBiology

Abstract

fetched live from OpenAlex

Sequential phosphorus (P) fractionation procedures are one of the most widely used wet chemical methods for characterizing P pools in soils and sediments, but have also been criticized repeatedly for their lack of accuracy to measure chemically specified phosphate fractions. In the recent investigation, sediments from two different sample locations with the same pretreatments were analyzed with sequential P fractionation. To verify traditional assignments of P fractionation results, P K-edge X-ray absorption near edge structure (XANES) spectroscopy was applied on the sediments and especially on the residues after the sequential extraction steps. Results of both methods indicated that the influence of sample pretreatment on the distribution of P pools was much lower compared to the effects of different sample origins. Kettle hole sediments were dominated by moderately labile iron (Fe) and aluminum (Al) associated P species, whereas Bodden sediments contained more stable calcium (Ca)–P species. Sample pretreatment of sediments can be similar to traditional soil sample pretreatment without causing fundamental changes in P speciation. The P K-edge XANES spectroscopy confirmed most assumptions of sequential P fractionation.

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 score0.033
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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