Application of seed-row potash to spring wheat grown on soils with high available potassium levels
Bibliographic record
Abstract
Karamanos, R., Flore, N. A. and Harapiak, J. T. 2013. Application of seed-row potash to spring wheat grown on soils with high available potassium levels. Can. J. Plant Sci. 93: 271-277. Two experiments were conducted at numerous locations across western Canada from 1990 to 1994 to ascertain the response of hard red spring (CWRS) wheat (Triticum aestivum L.) to seed-row applied K fertilizers. Soil test K levels at all sites (location×year combinations) exceeded the critical level for western Canada of 125 mg NH4OAc-extractable K kg-1, the concentration below which the probability of a K deficiency is high. In the first experiment, days to maturity and yield for wheat were statistically similar whether or not KCl was applied in the seed row. When the cultivar Roblin was grown significant (P<0.05) yield reductions as a result of seedrow placing low rates of potassium fertilizers were obtained. The second experiment showed that maturity and yield did not respond to treatments including KCl, K2SO4, or CaCl2 fertilizer applied in seed row. Furthermore, spring wheat responses for any of the preceding treatments were not different relative to the control (no K fertilizer applied). These findings bring into question the benefit of seed-row K fertilizer application to hard red spring (CWRS) wheat production on K-sufficient soils in western Canada.
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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".