Transformation and recovery of fertilizer phosphorus applied to five Quebec Humaquepts
Bibliographic record
Abstract
Improving phosphorus (P) fertilizer efficiency while minimizing environmental impacts requires better understanding of the dynamics of applied P in soils. This study assessed the fate of fertilizer P applied in Quebec Humaquepts. A pot experiment with five textural Humaquepts, each receiving 0 (P0), 10 (P10), 20 (P20) and 40 (P40) mg P kg−1 soil was conducted under barley (Hordeum vulgare L.)-soybean (Glycine max L.) rotations. A modified Hedley procedure was used for soil P fractionation. The clayey soils reached a plateau of dry matter at less P applied than the coarser-textured soils. Plant P uptake, soil labile inorganic P (resin-P + NaHCO3-Pi) and moderately labile inorganic P (NaOH-Pi) increased proportionally with P rate. The coarser-textured soils had lower contents of labile and moderately labile Pi, but a larger increase in labile Pi than the finer-textured soils after receiving P additions. The applied P was retained primarily as soil labile Pi, accounting for 43–69% of total soil recovery of applied P, compared to 20–30% recovered as moderately labile Pi, and 7–29% assumed to be sparingly soluble P (HCl-P + H2SO4-P). The labile Pi recovery of applied P was linearly depressed with clay content, compared to a quadratic relation for the moderately labile Pi recovery. The results suggest the importance of accounting for soil texture along with soil P adsorption capacity when assessing the efficiency of applied P, P accumulation in soils and subsequently P nutrient management.
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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 | 0.000 |
| 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) | 0.001 | 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".