Effect of Irrigation Water Salinity on the Growth of Quinoa Plant Seedlings
Bibliographic record
Abstract
The experiment aimed to study the effect of the irrigation water quality on the growth of seedlings and its yield of quinoa plant through some traits i.e., plant height, number of leaves per plant, 1000 grains weight, dry weight per plant , stem diameter, inflorescence length and grain yield per plant. Four treatments were used as follow: T1 (low salinity water, EC 1.25 dS m-1), T2 (mix water between low salinity water and agricultural drainage water at ratio 1:1, EC 4 dS m-1), T3 (agricultural drainage water, EC 8 dS m-1) and T4 (high salinity water, EC 16 dS m-1). The treatments application was at the beginning of the plant buds so that the amount of irrigation water up to 75% from field capacity. The significant effects of treatments were found on all tested traits. Also, the results clarified that the rate of chlorophyll ranged between 44.18 (treatment T4) and 53.75 SPAD (treatment T3), water potential of the fourth leaf has ranged from -0.83 to -1.745 MPa for T1 and T3 treatments, respectively, number of leaves per plant was ranged between 26.5 and 28.5 when the plants were irrigated with T4 and T1 irrigation water treatments, respectively. The inflorescence lengths were varied between 8 cm at T4 treatment and 12 cm at T2 treatment. The plant height was ranged between 53.5 cm (T4) and 60.75 cm (T3). The low values of seed yield were recorded at T4 (17.05 g/plant) while the higher values were recorded with T2 treatment (34.08 g/plant). 1000-grain weight values were ranged between 2.97 g at T2 treatment, and 3.49 g at treatment T1.
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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".