Friends or foes? Emerging insights from fungal interactions with plants
Bibliographic record
Abstract
Fungi interact with plants in various ways, with each interaction giving rise to different alterations in both partners. While fungal pathogens have detrimental effects on plant physiology, mutualistic fungi augment host defence responses to pathogens and/or improve plant nutrient uptake. Tropic growth towards plant roots or stomata, mediated by chemical and topographical signals, has been described for several fungi, with evidence of species-specific signals and sensing mechanisms. Fungal partners secrete bioactive molecules such as small peptide effectors, enzymes and secondary metabolites which facilitate colonization and contribute to both symbiotic and pathogenic relationships. There has been tremendous advancement in fungal molecular biology, omics sciences and microscopy in recent years, opening up new possibilities for the identification of key molecular mechanisms in plant-fungal interactions, the power of which is often borne out in their combination. Our fragmentary knowledge on the interactions between plants and fungi must be made whole to understand the potential of fungi in preventing plant diseases, improving plant productivity and understanding ecosystem stability. Here, we review innovative methods and the associated new insights into plant-fungal interactions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".