Potassium and nitrogen poising: Physiological changes and biomass gains in rice and barley
Bibliographic record
Abstract
Britto, D. T., Balkos, K. D., Becker, A., Coskun, D., Huynh, W. Q. and Kronzucker, H. J. 2014. Potassium and nitrogen poising: Physiological changes and biomass gains in rice and barley. Can. J. Plant Sci. 94: 1085–1089. Soil nitrogen, potassium, and water are three of the most important factors influencing, often interdependently, the growth of plants. Maximizing plant growth is not simply a matter of maximizing the availability of these and other nutrients; indeed, excess supply can be deleterious to plant performance. Rather, optimal performance may come about by adjusting the supply of each of the disparate factors required for plant growth, not only individually, but in relation to one another. In our work investigating the nutritional maximization of plant growth, we have found that altering the ratios of N and K provided to seedlings of cereal grasses can result in very substantial increases in vegetative biomass accrual, e.g., >220% of low-K+controls, in short-term studies with rice, the world's most important cereal grain, and even greater gains in grain yield, in the longer term. Hence, the findings in our laboratory are of direct relevance to the aim of NSERC's Green Crop Network, which was to contribute to the amelioration of climate change by improvement of carbon capture and sequestration in crop plants. In addition, these findings may help to increase the world's food supply, the security of which is sometimes at odds with proposed means to thwart climate change. Our work in this area has also led to a potential breakthrough of a more fundamental sort in plant nutritional biology, which may in itself have important practical implications: evidence that aquaporin-type transport proteins conduct rapid NH3fluxes into roots at toxic levels of external ammonia/ammonium.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".