Bibliographic record
Abstract
Plant genetic manipulation has led to the development of genetically modified plants (GMPs) expressing various traits. Since their first commercial use in 1996, GMPs have been increasingly used, reaching a global cultivating production area of 114.3 million hectares in 2007. The rapid development of agricultural biotechnology and release of GMPs have provided many agronomic and economic benefits, but has also raised concerns over the potential impact these plants might have on the environment. Among these environmental concerns, the unintentional impact that GMPs might have on soil-associated microbes, especially rhizosphere-inhabiting bacteria or rhizobacteria, represents one of the least studied and understood areas. As rhizobacteria are responsible for numerous key functions including nutrient cycling and decomposition, they have been defined as good indicator organisms to assess the general impact that GMPs might have on the soil environment. This minireview summarizes the results of various experiments that have been conducted to date on the impact of GMPs on rhizobacteria. Both biological and technical parameters are discussed and an attempt is made to determine if specific rhizobacterial responses exist for the different categories of GMPs developed to date.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".