Irrigated crop rotation and phosphorus fertilizer-manure addition impacts on soil water repellency of a clay loam soil in southern Alberta
Bibliographic record
Abstract
Soil water repellency (SWR) was measured for a 28 yr field study under irrigation on a clay loam Dark Brown soil in southern Alberta. The objectives were to study the effect of legume–cereal crop rotations, feedlot manure, and phosphorus (P) fertilizer application on soil hydrophobicity (SH) and soil water repellency index (RI) under irrigation. Mean SH and RI were similar (P > 0.05) for a legume–cereal and cereal rotation and were unaffected by P fertilization. However, P fertilization shifted the RI classification from slight to subcritical. In contrast, SH was significantly greater for manured than nonmanured treatments, while RI was unaffected. Soil organic carbon (SOC) concentration was significantly (P ≤ 0.05) correlated with SH (r = 0.74), but not with RI (r = −0.17). This suggested a closer association between the quantity of SOC and quantity of hydrophobic compounds (SH method) compared with the hydrophobic coatings inhibiting infiltration of water (RI method). No significant correlation between SH and RI (r = −0.09) suggests that SH is not a good predictor of SWR using the RI method. Overall, manure application increased SH and P fertilization shifted the RI classification from slight to subcritical. In contrast, legume–cereal rotations had no influence on SH and SWR using RI method compared with continuous cereal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".