Temporal patterns of soil phosphorus release to runoff during a rainfall event as influenced by soil properties and its effects on estimating soil P losses
Bibliographic record
Abstract
Wang, Y. T., Zhang, T. Q., Hu, Q. C., O'Halloran, I. P., Tan, C. S. and Reid, K. 2011. Temporal patterns of soil phosphorus release to runoff during a rainfall event as influenced by soil properties and its effects on estimating soil P losses. Can. J. Soil Sci. 91: 339–347. The phosphorus (P) released in soil runoff during a rainfall event varies as labile P is depleted, and the dynamic pattern can be a function of soil P content and other soil properties. This study was conducted to determine the temporal pattern of runoff dissolved reactive P (DRP) concentration during a simulated rainfall event and the controlling soil properties. Soil samples were collected from six soil types across the province of Ontario, with 10 sites for each, to provide a wide range of soil test P (STP) levels. The instantaneous DRP concentration in surface runoff created during the rainfall event could be predicted by time t (min, since the onset of surface runoff) through a power function: DRP=αt−β, where α and β are constants representing initial potential of soil P release to runoff as DRP at the onset of surface runoff and DRP decrease rate with time, respectively. The values of α and β for a given soil could be determined by DPSM3-2 (Mehlich-3 P/Mehlich-3 Al) using the following formulas:[Formula: see text] The description of the temporal pattern of runoff DRP concentration during a rainfall event with the constants estimated using DPSM3−2 can aid in the prediction of soil runoff DRP loss.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".