Testing the Effects of Nitrogen on the Interaction of M. persicae and A. thaliana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it