Forms and Lability of Phosphorus in Algae and Aquatic Macrophytes Characterized by Solution 31P NMR Coupled with Enzymatic Hydrolysis
Bibliographic record
Abstract
Abstract Solution Phosphorus-31 nuclear magnetic resonance (31P NMR) spectroscopy coupled with enzymatic hydrolysis (EH) with commercially available phosphatases was used to characterize phosphorus (P) compounds in extracts of the dominant aquatic macrophytes and algae in a eutrophic lake. Total extractable organic P (Po) concentrations ranged from 504 to 1643 mg kg−1 and 2318 to 8395 mg kg−1 for aquatic macrophytes and algae, respectively. Using 31P NMR spectroscopy, 11 Po species were detected in the mono- and diester region. Additionally, orthophosphate, pyrophosphate and phosphonates were also detected. Using EH, phytate-like P was identified as the prevalent class of enzyme-labile Po, followed by labile monoester- and diester-P. Comparison of the NMR and EH data indicated that the distribution pattern of major P forms in the samples determined by the two methods was similar (r = 0.712, p < 0.05). Additional 31P NMR spectroscopic analysis of extracts following EH showed significant decreases in the monoester and pyrophosphate regions, with a corresponding increase in the orthophosphate signal, as compared to unhydrolyzed extracts. Based on these quantity and hydrolysis data, we proposed that recycling of Po in vegetative biomass residues is an important mechanism for long-term self-regulation of available P for algal blooming in eutrophic lakes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".