Bibliographic record
Abstract
The issue of regulating the circulation of genetically modified (GM) crops and their products is extremely important for Ukraine. This is confirmed by climate change, which indicates the need for rapid adaptation of existing varieties while maintaining the yield level; increasing pest resistance to pesticides; international competitiveness of GM products; the need to comply with regulations on genetically modified organisms (GMOs) for European integration and the presence of genetically modified seeds in the country’s crops. So, the purpose of the work was to consider the problems, prospects and potential of GM plants in Ukraine. Qualitative analytical methods were used in the market analysis. Information was obtained from official data sources and market surveys. The results of four-year screenings in Dnipropetrovsk Region were also summarized. PCR diagnostics was used as verification method. In the course of the work, it has been established the presence on the Ukrainian market of more than two dozen GM soybean varieties, four transgenic sunflower hybrids, and ten transgenic corn hybrids from the world’s leading producers of Canada (Bramhill seeds, Sertis Holding S.A., Hyland Seeds, Sevita Int., Prograin), the USA (Asgrow & Monsanto), France (R.A.G.T.), Austria (Saatbau Linz). During 2018–2021, the distribution of transgenic products among domestic products in Dnipropetrovsk Region has been recorded. Thus, real-time PCR revealed that GMOs were present in 42.8% of the analyzed soybean samples; 87.5% of mixed fodder; 15.0% of sunflower samples. It has been established that the circulation of falsified GM products on the country’s market ranged from 25 to 50% (inconsistency in marking, certificate, holograms, and QR code), which indicates the imperfection of legal regulation and creates prerequisites for their illegal use
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".