EXOGENOUS APPLICATION OF INORGANIC SALTS DURING GROWTH STAGES INFLUENCES THE VEGETATIVE GROWTH, MARKETABLE YIELD AND QUALITY OF STRAWBERRY (FRAGARIA × ANANASSA) CULTIVAR ‘CHANDLER’
Bibliographic record
Abstract
In Pakistan strawberry yield and cultivation area is significantly lower as compared the major strawberry growing countries. Defective agronomic practices cause reduction in yield and quality. This experiment was conducted by using non-chemical techniques including exogenous application of inorganic salts CaCl2 (3, 5 and 7 mM/L) and ZnSO4 (50, 100 and 150 mg/L) during growth stages (3-4 leaves stage and after fruit setting) and their effects on marketable yield and quality of strawberry were documented. The highest number of leaves (18.25), leaf area (47 cm 2 ) and crowns (7.50) were found with 7 mM/L CaCl2 as compared to control plants. Minimum numbers of days (27.7) for flower anthesis were recorded with 100 mg/L ZnSO4. Maximum marketable yield (348.5 g plant -1 ), firmness (0.96 kg. cm -2 ), total soluble solids (8.2 Brix), TSS:TA ratio (7.62), vitamin C (55.69 mg 100 g -1 ), total phenolic contents (186.5 mg GAE 100 g -1 ) and higher total antioxidants (75.5% DPPH) were observed with 7 mM/L CaCl2 application. However, maximum superoxide dismutase activity (30.2 U mg -1 protein) was examined with 100 mg/L ZnSO4 while peroxidase activity (1.05 U mg -1 protein) decreased with this concentration. Overall, the response of 7 mM/L CaCl2 was highly effective for increasing marketable yield and quality.
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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".