Potential for Preferential Pathways of Phosphorus Transport
Bibliographic record
Abstract
Abstract This paper briefly reviews the existing literature and uses evidence from three studies to demonstrate the occurrence of preferential pathways of P transport through soil. Studies conducted in the St. Lawrence lowlands, Canada, indicated that particulate P (PP‐i.e., >0.45 µm) the main fraction of total P (TP) in tile‐drainage water generated by storm events after periods of low rainfall. In the remainder of the year, the concentration of TP and P forms were related to soil texture, primary tillage intensity and frequency, and showed wide seasonal variations. For a study conducted in the UK under grassland, higher TP concentrations were found in near‐surface runoff (0–30 cm) compared with concentrations measured in drainflow. Water passing through the artificial drainage system had a higher proportion of PP (43%) than water passing close to (<30 cm) or over the soil surface (31%). Installation of tile drainage in a poorly draining soil reduces P transfer by improving the infiltration capacity, thereby reducing overland flow volume and allowing P to be retained/sorbed by the soil matrix. Because of the absence of tillage, permanent grasslands accumulate P near the surface. We hypothesize that, if the soil P store is coincident with preferential flow pathways (either artificial mole drainage channels or natural macropores), permanent grassland will be vulnerable to transfer large amounts of P through subsurface pathways. Phosphorus transfer through preferential flow pathways may be particularly important after storm events that rapidly follow periods of drought and/or surface P inputs as inorganic fertilizer or manure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".