Soils potentially vulnerable to phosphorus losses: speciation of inorganic and organic phosphorus and estimation of leaching losses
Bibliographic record
Abstract
Abstract Eutrophication is an important threat to aquatic ecosystems world-wide, and reliable identification of areas vulnerable to phosphorus (P) losses from diffuse sources is essential for high efficiency of mitigation measures. In this three-step study we investigated (i) relationships between the agronomic (Olsen-P and P-AL) and environmental soil P tests (P-CaCl2) with molecular techniques (31P NMR and XANES) followed by (ii) rainfall simulation experiment on topsoil lysimeters and (iii) comparison to long-term field measurements of water quality. Soil samples were collected from seven sites indicated to be vulnerable to nutrient losses due to underlying geology. High P release correlated to standard agronomic P tests (Olsen P, r = 0.67; and P-AL, r = 0.74) and low P sorption capacity (r = − 0.5). High content of iron-bound P compounds indicated more labile P and higher release of dissolved P (r = 0.67). The leaching experiment showed that three out of four soils with high initial soil P status had both higher P leaching concentrations before fertilization (0.83–7.7 mg P l−1) compared to soil with low initial soil P status (0.007–0.23 mg P l−1), and higher increase in P concentrations after fertilization. Higher soil P sorption capacity reduced P leaching losses. Finally, long-term monitoring data show no significant trends in P losses in a field with low initial P content and moderate P fertilization rates whereas high and over time increasing P losses were recorded in a field with high initial soil P content and repetitively high P fertilization rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".