Management of Risks for Wheat Contamination with Fusarium graminearum
Bibliographic record
Abstract
The topic is particularly important because toxins cause mycotoxins in plants and animals and remain in food products obtained from infected organisms and they are mutagenic, teratogenic and estrogenic effects in animal and human bodies. Also, may be a serious threat to human health. This study presents the mineral nutrition status of winter wheat in connection with the risks of wheat contamination by Fusarium toxins in the soil conditions at INCDA Fundulea. The plants selected for testing were ten wheat cultivars, identified as susceptible to infection with Fusarium graminearum. The soil from the experiment was Cambic Chernozem. Two types of parcels were included in the experiment: one with healthy plants and another with artificially infected plants. In order to quantify the mineral nutrition status of plant with macro and micronutrients, the plant analyses were being carried out in the ear emergence-flowering phase. The obtained results it was interpreted in connection with the optimum limits of mineral contents in dry matter, mentioned in the specialty literature. The N and K ratios between healthy plant and artificially infected plants it was processed based on analytical data.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".