Soil microorganisms and crop yields after cattle manure, hog manure, or fertilizer application
Bibliographic record
Abstract
Soil biological properties can be significantly impacted by land management. Cattle manure, hog manure or inorganic fertilizers were applied annually or triennially to a Gray Wooded soil at Falher, Alberta, over three years. A control treatment without manure or fertilizer was also included. Canola (Brassica napus) was grown in Year 1, hulless barley (Hordeum vulgare) in Year 2, and wheat (Triticum aestivum) in Year 3. Where effects were significant, cattle manure increased soil microbial biomass C (MBC) by 26-86%, hog manure by 31%, and inorganic fertilizers reduced MBC by 20%. Similar effects, except the reduction by inorganic fertilizers, were observed for functional diversity of soil bacteria (Shannon index, H’). Crop N uptake from hog manure was higher than that in other treatments in Year 1, when hog manure increased grain yields by 75%, cattle manure by 49%, and inorganic fertilizers had no significant effect. However, cattle manure out-yielded other treatments in Years 2 and 3 (25-50% increase over the control) even though N uptake from inorganic fertilizers was the highest in Year 2. This implies that factors other than nutrient uptake also influenced crop yields. Frequency of application usually had no effects on MBC or H’, but the triennial application rate of inorganic fertilizers reduced crop yields relative to annual applications in Year 1.
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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".