Characterization of Organic Phosphorus in Lake Sediments by Sequential Fractionation and Enzymatic Hydrolysis
Bibliographic record
Abstract
The role of sediment-bound organic phosphorus (Po) on lake eutrophication was studied using sequential extraction and enzymatic hydrolysis by collecting sediments from Dianchi Lake, China. Bioavailable Po species including labile monoester P, diester P, and phytate-like P were identified in the sequential extractions by H2O, NaHCO3, and NaOH. For the H2O-Po, 36.7% (average) was labile monoester P, 14.8% was diester P, and 69.9% was phytate-like P. In NaHCO3-Po, 19.9% was labile monoester P, 17.5% was diester P, and 58.8% was phytate-like P. For NaOH-Po, 25.6% was labile monoester P, 7.9% was diester P, and 35.9% was phytate-like P. Labile monoester P was active to support growth of algae to form blooms. Diester P mainly distributed in labile H2O and NaHCO3 fractions was readily available to cyanobacteria. Phytate-like P represents a major portion of the Po in the NaOH fractions, also in the more labile H2O and NaHCO3 fractions. Based on results of sequential extraction of Po and enzymatic hydrolysis, lability and bioavailability was in decreasing order as follows: H2O-Po > NaHCO3-Po > NaOH-Po, and bioavailable Po accounted for only 12.1-27.2% of total Po in sediments. These results suggest that the biogeochemical cycle of bioavailable Po might play an important role in maintaining the eutrophic status of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".