Characterization and sources of dissolved and particulate phosphorus in 10 freshwater lakes with different trophic statuses in China by solution <sup>31</sup> P nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
Abstract Information on dissolved phosphorus (DP) and particulate phosphorus (PP) is essential to evaluate the P dynamics and control eutrophication. In this work, DP and PP in 10 freshwater lakes representing various trophic statuses were analyzed by solution 31 P nuclear magnetic resonance spectroscopy. The results indicated that the predominant forms of DP and PP were orthophosphate (Ortho‐P) and monoester phosphate (Mono‐P). There was a greater concentration of Ortho‐P and Mono‐P in the water and particulate matter of medium‐eutriphic lakes than in lightly eutriphic or mesotrophic lakes. α‐Glycerophophate (2.7–32.5%), β‐glycerophosphate (1.3–23.4%), guanosine 2′Mono‐P (20.2–29.3%), inositol hexakisphosphate (IHP) (8.3–36.4%) and nicotinamide adenine dinucleotide (NAD) (17.3–35.9%) were identified as the major chemical forms of Mono‐P in water and particulate matter, which originate mainly from the degradation of labile diesters, aquatic/microbial sources and a combination of terrestrial and aquatic/microbial sources. Diester phosphate (Di‐P) was dominated by teichoic acid (0.5–14.8%) and deoxyribonucleic acid (DNA) (0.6–17.7%), which originated from aquatic/microbial sources. Moreover, correlation analysis showed that dissolved Mono‐P in water and particulate matter had a positive correlation with chlorophyll‐ a (Chl‐ a ), which indicated their potential bioavailability for algal activity. pH was a crucial parameter to control Di‐P, pyrophosphate (Pyro‐P) and polyphosphate (Poly‐P) in water. Mono‐P/PPs showed a positive correlation with Chl‐ a ( R 2 = 0.459), total phosphorus (TP) ( R 2 = 0.586) and the trophic status index ( R 2 = 0.588), which suggested that particulate Mono‐P can potentially contribute to lake eutrophication. Both Ortho‐P and Mono‐P were major contributors of P nutrients for algae, and therefore source control and new techniques are needed for reducing eutrophication.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".