The Effects of Salinity and Activated Charcoal on the Herbivory of Arabidopsis thaliana by Myzus persicae
Bibliographic record
Abstract
Road salt is commonly applied in the winter and inevitably percolates into surrounding areas where it is absorbed by plants. The detrimental effects of salinity on plants has been studied extensively, with recent research papers investigating potential mitigative methods, including the application of biochar. Building upon previous findings, this study serves to explore the use of activated carbon, charcoal with increased adsorptive ability, as a remediation technique for salt stress. Using Arabidopsis thaliana (thale cress) and Myzus persicae (green peach aphids) as our model organisms, the aim was to determine the individual and combined effects of salinity and activated charcoal on plant performance and aphid populations using a factorial design. The overall findings presented a statistically significant effect (p=0.0201) on M. persicae herbivory between 25 mM salt and activated charcoal treatment groups. Although changes in plant biomass were not observed, there were a greater number of aphids occupying the plants without activated charcoal than on plants with activated charcoal for 25 mM salt treatments. Therefore, activated charcoal presents the opportunity for an accessible method of treatment for salt-stressed plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".