The bilateral influence of plant and rhizosphere characteristics in brassicas varying in seed oil productivity
Bibliographic record
Abstract
Vessey, J. K., Fei, H., Burton, D. L., Bradley, R. L. and Smith, D. L. 2014. The bilateral influence of plant and rhizosphere characteristics in brassicas varying in seed oil productivity. Can. J. Plant Sci. 94: 1113–1116. It is important that increasing seed oil yield in species of Brassica to improve the crops as biodiesel feedstocks does not result in unforeseen increases in greenhouse gas (GHG) emissions. Studies were conducted to determine if genotypes of Brassica napus and Arabidopsis thaliana varying in seed oil content (SOC) potential had differences in plant and rhizospheric characteristics that could impact GHG emissions. Varying SOC productivity in B. napus resulted in changes in C and N partitioning within the plant, and in some cases had effects on N2O emission in the field. Although changes were observed in the composition of the rhizosphere of A. thaliana with modified SOC, there was also evidence that rhizospheric bacteria-to-plant signals could be used to improve growth and stress resistance in the plants. Project 4c in the Green Crop Network (GCN) investigated the possible ramifications of varying SOC on various plant growth, rhizospheric and agronomic characteristic of Brassica napus L. and Arabidopsis thaliana (L.) Heynh. The influence of certain bacteria-to-plant signals (i.e., lipo-chitooligosaccharides) was also investigated in these species. The rationale for these investigations was based on the fact that very little is known about how changing seed oil productivity in brassicas might affect other plants processes (e.g., C and N partitioning, root exudations, rhizospheric conditions) that might affect GHG emission from biodiesel feedstock crops designed specifically for maximized SOC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".