Current state of genetically modified plant impact on target and non-target fungi
Bibliographic record
Abstract
For two decades, genetic engineering has made it possible to develop crops and trees designed for yield improvement and simplified culture management. This, combined with field deployment of monocultures over large areas, can result in environmental stress and unwanted potential side effects. The commercial production of genetically modified (GM) crops and the recent development of GM trees raise concerns about their potential impact on the environment, in general, and on the biodiversity of non-target organisms, in particular. Fungi are spread worldwide and play key roles in ecosystems. They have been closely associated with plants since they emerged from the oceans. This review critically examines research monitoring the potential effects of GM crops and GM trees on target and non-target fungi. Parsing public databases for peer-reviewed publications about GM plant impacts on fungi yielded 149 studies, a relatively modest number considering the diversity of crops and ecosystems studied. Analysis of these publications showed that the effects of GM plants expressing herbicide and insect tolerance on fungi are understudied while they dominate the GM area worldwide. Experiments monitoring the impact of GM crops and GM trees with enhanced antifungal activity towards target fungi showed, for the most part, significant decreases in disease severity caused by fungal pathogens. Significant changes, expressed as an increase or decrease in fungal development, abundance, and diversity of non-target fungi, were observed in 18 out of 60 studies and all of them involved GM plants expressing traits that were unexpected to affect fungi. The remaining 42 studies did not identify a significant impact on fungal populations. Therefore, in spite of the fact that GM plants have been commercialized since 1996, no clear generalized trend can be identified and it appears that a case-by-case approach is the safest.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".