Influence of Sample Pretreatment on P Speciation in Sediments Evaluated with Sequential Fractionation and P <i>K</i>-edge XANES Spectroscopy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".