Manitoba Livestock and Manure Management Initiative
Bibliographic record
Abstract
Continuous application of manure P above crop removal results in a buildup of soil phosphorus (P), which increases the risk of P runoff loss from agricultural land, leading to environmental problems such as eutrophication of surface waters. There is little or no information about the forms of manure P that are correlated with risk of P loss after manure interacts with soil, especially for prairie soils. The objectives of this study were to quantify and compare P losses from liquid swine- and solid cattle- manure treated soils after incubating for 6 weeks, and to relate P losses to manure P forms and soil test P after incubation. Amount of P in different fractions of manure samples were quantified using the modified Hedley fractionation. Phosphorus runoff and leaching losses in ten fertility treatments (4 sources of solid cattle manure, 4 sources of liquid swine manure, monoammonium phosphate (MAP) and check) were compared in two soils (Lone Sand and Newdale Clay Loam) with two replicates for each fertility treatment by conducting a rainfall simulation runoff study and a column leaching study. Manure or fertilizer was applied to soil at the rate of 50 mg P kg-1 soil (≈100 kg of P ha-1), mixed, moistened to 90 % field capacity and
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.011 |
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".