Evaluation of phosphorus retention equations for Manitoba soils
Bibliographic record
Abstract
One of the key factors in phosphorus management is the P retention capacity (PRC) of the soil. In our previous study, we formulated several equations for estimating the phosphorus retention capacity of Manitoba soils. The objectives of the current study were to evaluate these equations using independent soil samples and to evaluate the influence of manure application on the predictive ability of these equations. Forty representative surface soil samples (20 soils with history of manure application and 20 without manure application history) were collected from across Manitoba. The P retention index (P150) and Langmuir adsorption maximum (Smax) were determined in the laboratory. The measured P retention capacities were then compared with those estimated using the formulated equations. Surprisingly, P150, which was obtained from a single measurement, was more robust than Smax that was obtained from at least 17 measurements as the equations provided a better estimate of P150 than Smax. Equations that were based on soil particle sizes (either percent clay or percent sand) provided poor estimates of soil PRC for the whole soil collection. However, when the soils were grouped on a pH basis, soil particle size worked better for soils with pH <7 than for soils with pH ≥7. The equations also worked better for soils with pH similar to those of the soils that were used to formulate the P retention equations. The accuracy of the predicted P sorption capacity improved with the manured soils due to the direct influence of manure application on soil properties that influence P retention such as Ca and Mg. Overall, the combination of CaM3, MgM3 and AlOx provided the best estimate of the PRC of Manitoba soils. Key words: Soil phosphorus, calcareous soils, phosphorus sorption, phosphorus retention, phosphorus retention capacity, estimating phosphorus retention capacity
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".