Analyzing the effect of mycorrhizal fungi on plant communication of nutrients
Bibliographic record
Abstract
The goal of our experiment was to determine if phosphate transfer occurred between plants, using mycorrhizal fungi. We hypothesized that a “communication” network existed between plants when the mycorrhizal fungi were present and that there would be no transfer of nutrients if the mycorrhizal fungi were not present. This study extended the analysis of Suzanne Simard who studied Paper Birch, Douglas Fir, and Western Red Cedar in Canada. She was able to confirm that there is a massive underground communication where different plants cooperate with resources using mycorrhizal fungi. The purpose of our experiment was to see if plants with nutrients would transfer their excess levels of phosphate to the plant that had a limited amount of nutrients. Overall, no definitive conclusion could be drawn from the treatments conducted. The measurements greatly deviated from the hypothesized results and demonstrated a more complicated relationship than originally thought. Further study will need to be conducted to determine if there are further conditions that must be met, such as a minimum amount of phosphate concentration or a minimum differential between the resources (in terms of this study, phosphate concentration) of the two plants, for this transfer to occur. The results of this study, if replicated and shown to be conclusive at long distances, could be used to aid forest management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".