Physical properties of an Orthic Black Chernozem after 5 years of liquid and solid pig manure application to annual and perennial crops
Bibliographic record
Abstract
Pig (Sus scrofa) manure is added to the soil to supply nutrients and improve soil properties. To our knowledge, no direct comparison has been made on the effect of liquid pig manure (LPM) and solid pig manure (SPM) on the physical properties of a prairie soil. This study was established in 2009 at the University of Manitoba’s Ian Morrison Research Station in Carman, Manitoba. The treatment design was a split-plot structure with cropping system as the main plot and manure treatments as subplots. Five years after the study was initiated, soil samples were collected from the 0–10 cm and 10–20 cm depth intervals for determination of bulk density, saturated hydraulic conductivity (Ksat), and water retention at field capacity and permanent wilting point (PWP). For wet aggregate stability, samples were collected from the 0–5 cm layer. Land application of SPM significantly decreased bulk density by 14%, significantly increased Ksat by 110% in the 0–10 cm layer, and resulted in a 30% increase in wet aggregate stability (P < 0.05). In perennial plots, SPM increased water retention at field capacity, PWP, and available water in the 0–10 cm compared with annual plots. This was not the case for LPM-amended soils. We conclude that SPM has the potential as an organic amendment to improve the physical properties of the topsoil.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".