Testing the Effects of Nitrogen on the Interaction of M. persicae and A. thaliana
Bibliographic record
Abstract
Natural elements serve as the building blocks of ecosystems, and cycle through the biosphere. One of the most important elements is nitrogen, which is beneficial for plant growth. To further increase plant growth, nitrogen is artificially added to ecosystems as fertilizer, though it may put nearby organisms at risk. The impact of fertilizer runoff affects many environments and the organisms that inhabit them. For these reasons, it is important to understand the effects of increased amounts of nitrogen on plant-animal interactions. To do so, we studied the effect of varying ammonium nitrate (AN) concentrations, a compound commonly found in fertilizer, on the interaction between Arabidopsis thaliana and Myzus persicae. The control group A. thaliana plants were treated with distilled water, while low and high dose groups were treated with 60 ppm and 300 ppm aqueous solutions of AN respectively. We counted the number of M. persicae present on each A. thaliana plant throughout the study period. The low dose group begins to plateau after the sixth day, while the control and high dose groups grew. These results suggest that soil nitrogen content affects plant-animal interactions. The optimal treatment was a low dose of AN, as population growth of M. persicae plateaued, limiting herbivory and potentially benefiting A. thaliana.
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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) | 0.001 | 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".