Seed Germination and Initial Growth of Quinoa Seedlings Under Water and Salt Stress
Bibliographic record
Abstract
Excessive amounts of salts and soil water deficiency interfere on seed germination and the full development of several crops. The objective of this research was to evaluate the effect of water stress and salinity on the germination process and initial growth of quinoa (Chenopodium quinoa Willd.) seedlings. In the first experiment, two quinoa seed lots with different physiological conditions were distributed on paper soaked in aqueous solution containing polyethylene glycol PEG-6000 in osmotic potentials corresponding to 0.0; -0.1; -0.2; -0.3 and -0.4 MPa and held at 20 °C under 8 hours of light exposition. In the second experiment, solutions of sodium chloride (NaCl), potassium chloride (KCl), calcium chloride (CaCl2) and magnesium chloride (MgCl2) were used to simulate the effect of salinity using the osmotic potentials, temperature and light conditions previously described. Assessed parameters were the germination percentage, first count, length and dry mass of seedlings. There was a reduction in quinoa germination percentage, first seed count and seedling length as the osmotic potential decreased in CaCl2, NaCl, KCl, MgCl2 and PEG-6000 solutions. The quinoa seeds exhibited higher tolerance to NaCl and KCl salts in the germination process and initial seedling growth. The progressive reduction of the osmotic potential induced by salts NaCl, KCl, CaCl2, MgCl2 and PEG-6000 negatively affects seed germination and initial growth of quinoa seedlings.
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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".