Effects of supplemental poultry manure applications on soil erosion and runoff water quality from a loam soil under potato production in northwestern New Brunswick
Bibliographic record
Abstract
Rees, H. W., Chow, T. L., Zebarth, B. J., Xing, Z., Toner, P., Lavoie, J. and Daigle, J.-L. 2011. Effects of supplemental poultry manure applications on soil erosion and runoff water quality from a loam soil under potato production in northwestern New Brunswick. Can. J. Soil Sci. 91: 595–613. Soil erosion is a major threat to the economic viability of potato production in northwestern New Brunswick. One option for growers to increase soil organic matter and improve soil quality in potato fields is through poultry manure application. While poultry manure may be beneficial for soil quality, there are also potential risks to surface water quality associated with manure application. This study evaluated the effects of time of poultry manure application on potato fields in northwestern New Brunswick on soil erosion and runoff water quality. Seven permanent Wischmeier-like erosion plots, established in 1982, were used. Treatments consisted of a control (Ctrl) with no manure applied, and applications of 4 Mg ha−1 of fresh poultry broiler manure in late fall (F), pre-planting (PP) and pre-hilling (PH) on 11% slope plots and a Ctrl, F and PH treatments on 8% slope plots. All poultry manured treatments increased potato total yield with a general trend of Ctrl
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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 | 0.001 | 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".