Optimization of Sargassum bovianum Extraction Techniques for Germination of Wheat, Canola, and Corn Under Different Salinity Stress
Bibliographic record
Abstract
Seaweeds are a cheap, eco-friendly, and rich source of plant growth stimulators that can mitigate the adverse effects of salinity stress. This study examined the impact of Sargassum bovianum extracts obtained through different techniques using pressure, heat, and microwave radiations on the germination and growth of wheat, corn, and canola seeds under varying salinity levels (500, 3500, and 6500 µS cm−1). The findings showed that pressure, microwave, and acidic extraction methods were the most effective in extracting polysaccharides, alginate, and nutrients from S. bovianum. Seaweed extract significantly improved the mean germination time (MGT) and germination index (GI) of wheat under high salinity stress and had a positive effect on wheat plumule length (PL) and germination percentage (GP). However, seaweed extract had no significant impact on canola seeds in salinity stress, except for improved canola PL. The PL and seedling vigor index (SVI) of corn were enhanced in low salinity levels, but most treatments reduced PL and SVI in high salinity. This study suggests that using heat, pressure, and microwave techniques for seaweed extraction results in higher polysaccharides and alginate content, leading to improved germination and plant growth, particularly in wheat and canola. These findings can help growers optimize the germination and growth of these important crops.
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) | 0.000 | 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 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".