Calcium Nitrate Priming Increases the Germination Rate of Eggplant Seeds
Bibliographic record
Abstract
The priming may promote greater expression of the physiological potential of the seeds and contribute to the germination process under adverse environmental conditions. The objective of this study was to evaluate the physiological potential of eggplant seeds primed with different chemical agents and exposed to adverse temperatures. Seeds were subjected to priming with five chemical agents [unprimed (control); water (hydropriming); calcium nitrate (0.2%); amino acid L-phenylalanine (0.05%); L-phenylalanine (0.05%) + calcium nitrate (0.2%)] and at temperatures of 15, 25, 35 and 41 ºC, considering a completely randomized design in a factorial arrangement (5 × 4) with four replicates. The first count of radicle emission and germination, final radicle emission, seed germination, radicle emission rate index, mean radicle emission time, and seedling dry mass were measured. Germination process of eggplant seeds was completely inhibited at 41 ºC, and the optimal temperature for seed germination is 25 ºC. Calcium nitrate priming potentiates the seed germination process, resulting in higher radicle emission rate index and higher germination rate. Low temperature (15 ºC) has greater interference in the germination rate of eggplant seeds when compared to high temperature (35 ºC).
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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".