effect of applying starch onto Arabidopsis thaliana on the feeding behaviour of Myzus persicae
Bibliographic record
Abstract
It is well known that plant-animal systems interact in many complex ways, and each organism must adapt and develop mechanisms to best survive in their given conditions. While much is understood about the plant Arabidopsis thaliana and the aphid Myzus persicae, additional research must be conducted to gain more knowledge about the interactions between the two species. As a defence mechanism, in response to aphid feeding, A. thaliana converts sucrose into starch. Due to a lack of sucrose, there is less feeding by M. persicae. However, it has not yet been shown if these aphids are able to detect an increase in starch and recognize this as a deterrent to feeding. To test this, varying concentrations of potato starch were applied mechanically to A. thaliana (n=36) and the effect on aphid population size and plant health was analyzed. The research team found that M. persicae do not detect higher starch levels on A. thaliana as an indicator that nutrient availability on the plant is limited. Instead, it was found that on all but one plant, high starch concentration was a factor in plant deterioration. Thus, the research team advises against using starch as an organic pesticide. The findings of this study are significant as they will contribute to a better understanding of the organisms that threaten plant health, which will prove to be useful in the maintenance of various food crops.
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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) | 0.002 | 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".