Seed Quality of Chickpea and Common Bean as a Function of the Application of Ascophyllum Nodosum Doses Seaweed Extract
Bibliographic record
Abstract
The application of A. nodosum based seaweed extract can enhance seed quality. However, the positive results regarding the use of this technology remain unclear. This study investigated the impact of different doses of organomineral seaweed-based fertilizer on the seed quality of chickpea and common bean. A completely randomized design was used with four replications, and the treatments consisted of seed treatments with five doses of A. nodosum seaweed extract for chickpea (0, 50, 100, 150, and 200 mL of extract per 100 kg of seeds) and of (0, 125, 250, 375 and 500 mL of extract per 100 kg of seeds) for common bean. After harvest, the seeds were analyzed using the following tests: germination, first count, seedling length, and seedling dry mass. It was concluded that the seed quality of chickpea and common bean was influenced by the addition of A. nodosum seaweed extract. Doses of seaweed extract higher than 100 and 250 mL per 100 kg of seeds negatively affected the physiology of chickpea and bean seeds, respectively. The doses of 100 and 250 mL per 100 kg of seeds resulted in higher-quality seed lots of chickpea and common bean, offering valuable insights for future agricultural practices and the advancement of sustainable, cost-effective production.
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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 | 0.000 |
| 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".