A new solution<sup>31</sup>P NMR sample preparation scheme for marine sediments
Bibliographic record
Abstract
Abstract A new approach for the preparation of marine sediment samples for solution 31 P nuclear magnetic resonance spectroscopy ( 31 P NMR) has been developed and tested. This approach addresses important aspects associated with sample pretreatments for marine sediments, including the effects of sample pretreatment on sedimentary P composition. The method increases the signals of low abundance P species in 31 P NMR spectra by quantitatively and precisely removing up to 80% of inorganic P (orthophosphate) from sediment samples while causing minimal alteration of the chemical structure of organic P compounds. This method uses a reductive step to solubilize P bound to iron oxyhydroxides, followed by a low pH digestion to extract P from authigenic and biogenic apatite, as well as P bound to calcium carbonate. These P forms combined represent the most abundant inorganic P reservoir in marine sediments. The sample residue is then extracted in an alkaline solvent, 0.25 M NaOH with 0.05 M Na 2 EDTA, and processed for 31 P NMR spectroscopy. The method was tested on natural marine sediment samples from different localities with high inorganic P content (>85% molybdate reactive P), and allowed for the detection of orthophosphate monoesters and pyrophosphate in samples for which only an orthophosphate signal could be resolved with an NaOH‐EDTA extraction alone. This new approach will allow the use of 31 P NMR on samples for which low organic P concentrations previously hindered the use of this tool, and will help answer longstanding question regarding the fate of organic P in marine sediments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".