Biological Response of Triticum aestivum L. to the Abiotic Stress Induced by Winemaking Waste
Bibliographic record
Abstract
The winemaking waste (grape marc) can be beneficial if it is used in food, pharmaceutical industry, and medicine. However, studies reported that some concentrations of grape marc extracts may induce negative effects on animals. The present study was conducted in order to research if the grape marc induces abiotic stress with serious negative implications on plants. For this purpose, wheat grains were treated for 48 h with 0.025%, 0.05%, 0.1% and 0.2% aqueous extracts of Merlot and Sauvignon blanc grape marc. Grains germination rate and cytogenetic parameters were investigated. The germination rate decreased moderately compared to the control in all treatments. The investigated cytogenetic parameters were: mitotic index (MI) and genetic abnormalities (bridges, fragments, associations between bridges and fragments, multipolar ana-telophases, micronuclei). As the grape marc concentration increases, the germination rate and mitotic index decrease moderately, while the percent of cells with chromosomal aberrations and micronuclei increases. Treatments with Merlot grape marc extract induced a higher percent of genetic abnormalities. The results prove from a genetic point of view that the winemaking waste induces abiotic stress on wheat (and probably, on other plants) and it should be depleted in polyphenols before storing on fields. Possible use of unprocessed grape marc could be as bio-herbicide.
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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) | 0.000 | 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 | 0.001 |
| 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".