Chemical Fractions And Predictions For Long-term Releases of Phosphorus In Typical Canadian Agricultural Soils
Bibliographic record
Abstract
Phosphorus (P) pollution has been identified as the most significant agriculture-related threat to water quality impairment in Canada. One approach to reduce P pollution is to identify soils with high P loss potential and develop management strategies to minimize that risk. This thesis contributes towards greater understanding of short- and long- term P dynamics in soils to which different P sources had been applied (Chapters 3 and 4) and improvement in the P measurements for determining long-term P loss potential (Chapter 5). Chapter 3 evaluated immediate and residual effects of swine manure and fertilizer on soil P. Soils were sampled from Brookston clay loam in south-western Ontario, Canada which were treated with liquid (LM), solid (SM), composted (MC) manure and fertilizer, only in the corn phase. Soils were analyzed using a modified Hedley’s fractionation. All P sources influenced soil labile and moderately labile P in the year of application, while MC and SM showed significant residual impacts in the following year. Residual effects of MC and SM are beneficial for crops; however, there may be a P loss potential through leaching and runoff. Chapter 4 considered long-term effects of dairy manure slurry (DMS) and ammonium nitrate (AN) on soil P. Soils were sampled from south coastal region of BC, Canada, which were treated with DMS or AN at 50 or 100 kg NH4-N ha-1, and analyzed using a modified Hedley’s fractionation. DMS significantly increased labile and moderately stable P in surface soil, indicating short- and long-term impacts on P availability and loss potential. Chapter 5 analyzed a new test to predict long-term soil P loss potential. Soils were collected from four agro-ecological areas across Canada, and analyzed using Mehlich-3, Olsen, Resin strips (RMS), FeO-strips, and new procedures: various combinations of NaOH with and without EDTA, with four shaking periods. Statistically significant linear and quadratic relationships between the RMS and NaOH with EDTA-P indicated that the latter provide an efficient basis for predicting long-term soil P loss potential. A highly significant relationship between RMS-P and 0.025M NaOH with EDTA-P indicates this extractant was effective for measuring Total Releasable P.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".