Phosphorus fractions and profile distribution in newly formed wetland soils along a salinity gradient in the Yellow River Delta in China
Bibliographic record
Abstract
Abstract Soil P availability has been identified as one of the key factors controlling wetland productivity, structure, and function. Soil P fractions at different depths in newly formed wetlands along a salinity gradient in Yellow River Delta (China) were studied using a modified Hedley fraction method. The total P (Pt) content ranged from 471.1 to 694.9 mg kg–1, and diluted HCl‐extractable inorganic P (Dil‐HCl‐Pi) ranged from 324 to 524.2 mg kg–1. The Dil‐HCl‐Pi is the predominant P form in all profiles, with on average 70% of the Pt extracted as Pi. Organic P (Po) comprised (4.2 ± 2.0)% (mean ± SD) of the Pt, due to low organic‐matter content in coastal salt marsh ecosystems. The labile P (resin‐P, NaHCO3‐Pi, and NaHCO3‐Po) and moderately labile P (NaOH‐Pi and NaOH‐Po) concentrations were both low, ranged from 11.6 to 38.1 and 2.8 to 21.3 mg kg–1, respectively, constituting (3.7 ± 1.1)% and (2.0 ± 0.7)%, respectively, of Pt, suggesting low availability of P to plants in these soils. Our results suggested that vegetation cover significantly influenced soil P dynamics and availability. In particular, the labile P content under Tamarix chinensis increased significantly by 23.2%–145.5% compared with adjacent soils. These findings have important implications for wetland conservation or restoration and long‐term sustainable management of newly formed wetland ecosystems in the Yellow River Delta.
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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 | 0.000 | 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".