Endophytic fungus diversity in soybean plants submitted to conditions of elevated atmospheric CO<sub>2</sub> and temperature
Bibliographic record
Abstract
Global climatic changes can have drastic impacts on plant species, including severe consequences for the agricultural species productivity. Many of these species present important mutualisms with endophytic fungi that positively influence their performance. The present study evaluated whether the increases in CO2 and temperature predicted for the year 2100 may cause changes in foliar carbon (C) and nitrogen (N) concentrations in soybean (Glycine max) and, consequently, the interactions with its endophytic fungi. The effects of elevated CO2 and temperature were evaluated in four treatments in open-top chambers: (i) control, (ii) increased temperature, (iii) increased CO2, and (iv) increased CO2 and temperature. Increased atmospheric CO2 resulted in decreased foliar N concentration, while increased temperature increased it. A total of 16 taxa of endophytic fungi were identified based on sequencing internal transcribed spacer regions of rRNA subunits. Increased atmospheric CO2 and temperature were observed to potentially modify the endophytic mycobiota of soybean plants. The results suggest that the fungi species substitution is a consequence of changes in foliar N concentration and C/N ratio. Predicted climatic changes shall affect the relationships between plant and endophytes, which in turn, will affect the performance and resistance of soybean, one of the most important crops in the world.
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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 | 0.000 |
| 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 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".