Agronomic performance of barley cultivars in response to varying rates of swine slurry
Bibliographic record
Abstract
Buckley, K. E., Mohr, R. M. and Therrien, M. C. 2011. Agronomic performance of barley cultivars in response to varying rates of swine slurry. Can. J. Plant Sci. 91: 69-79. Selection of crop variety may address concerns of potential adverse effects of preplant manure slurry application on crop yield and quality due to nutrient availability and lack of precision in application rate. An experiment was conducted in two field locations in southern Manitoba to assess the impact of slurry rate on growth, yield and quality of three barley (Hordeum vulgare L.) cultivars (Harrington, Rosser, Stander). Treatments included three rates of swine slurry, an unfertilized check and an inorganic fertilizer treatment at the recommended N rate based on preseeding soil nutrient tests. While the current study demonstrated no significant difference in the grain yield response of barley cultivars to rates of slurry application, higher rates of swine slurry may have a negative effect on milling quality (percentage of plump kernels) depending on cultivar, but had little effect on other quality parameters such as test weight and 1000-kernel weight. The absence of cultivar×slurry interaction for grain and biomass yield at each field location in each year indicated that all cultivars responded similarly to increasing rates of manure slurry for these traits. Grain protein concentration for all cultivars was unaffected by slurry amendment except at the highest application rate.
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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 | 0.001 |
| 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".