Modelling dissolved phosphorus losses from accumulated soil phosphorus and applied fertilizer and manure for a national risk indicator
Bibliographic record
Abstract
Balancing the weighting of various components of phosphorus loss in models is a critical but often overlooked step in accurate estimation of risk of P loss under field conditions. This study compared the P loss coefficients used to predict dissolved P losses from desorption from accumulated P in the soil, and those incidental to applications of P as fertilizer or manure, with extraction coefficients determined from actual P losses reported in literature for sites in Canada, with the addition of some sites with similar soils and climate from some northern states. The extraction coefficients for dissolved P measured in runoff water were 6.5× greater in year-round edge-of-field (EoF) measurements than in runoff boxes, indicating that models using P extraction coefficients derived from runoff box experiments will be underestimating the magnitude of losses from P accumulation in soil. Differences among the measurement methods (runoff box, rainfall simulator, or EoF) were not evident for incidental losses from applied P, but current models appear to overpredict the losses of applied P. Good agreement between measured and predicted dissolved P (DP) concentrations using the equations in the Annual Phosphorous Loss Estimator model were achieved by applying coefficients of 0.275 to the fertilizer equations and 0.219 to the manure equations, implying that 72.5% of fertilizer P and 78% of manure P are not available for runoff. This study underlines the importance of considering the relative weights of the various components of P loss as new models are developed and validated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".