Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
Bibliographic record
Abstract
Phosphorus (P) is a macronutrient that often limits the productivity and growth of terrestrial ecosystems, but it is also one of the main causes of eutrophication in aquatic systems at both local and global levels. P content in soils can vary largely, but usually, only a small fraction is plant-available or in an organic form for biological utilization because it is bound in incompletely weathered mineral particles or adsorbed on mineral surfaces. Furthermore, in agricultural ecosystems, plant-available P content in topsoil is mainly controlled by fertilization and land management. To understand, model, and predict P dynamics at the landscape level, the availability of detailed observation-based P data is extremely valuable. We used more than 388,000 topsoil plant-available P samples from the period 2005 to 2021 to study spatial and temporal variability and land-use effect on soil P. We developed a mapping approach based on existing databases of soil, land-use, and fragmentary soil P measurements by land-use classes to provide spatially explicit high-resolution estimates of topsoil P at the national level. The modeled spatially detailed (1:10,000 scale) GIS dataset of topsoil P is useful for precision farming to optimize nutrient application and to increase productivity; it can also be used as input for biogeochemical models and to assess P load in inland waters and sea.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".