Effect of Osmotic Conditioning in Physiological Quality of Cucumis sativus L. and Solanum lycopersicum L. Seeds
Bibliographic record
Abstract
The human consumption of vegetables in different parts of the world is quite high, among them tomato stands out on a larger scale and not so far, also is cucumber, both with great economic importance. Due to the problems regarding the vigor of many seeds, techniques that seek to improve the establishment of the seedlings in a uniform way have been implemented. One technique is the osmotic conditioning to which the seeds are submitted, for which compound solutions such as polyethylene glycol or potassium nitrate can be used, with which very satisfactory results are obtained under laboratory and field conditions The objective of this investigation was to evaluate the effect of different doses and osmotic solutions on the physiological quality of seeds of tomato (Solanum lycopersicum L.) and cucumber (Cucumis sativus L.) in laboratory and field conditions. The evaluated variables were germination percentage, germination speed index, root length and emergence of seedlings. The tomato seeds were conditioned with PEG-6000 (-0.2, -0.4, -0.6 and -0.8 Mpa) and stored for a 30 days period, and as for the cucumber seeds with PEG-6000 (-0.2 and -0.1 Mpa) and with KNO³ (100 mg and 300 mg) with a storage period of 45 days. It is concluded that, in tomato seeds, germination did not present statistical differences, however, the variables referring to vigor were favored with the conditioning in the presence of PEG-6000 with the (-0.2 Mpa) concentration, while in cucumber seeds the germination percentage was better using PEG 6000 compared to nitrate, while for vigor variables it behaved better in the presence of a concentration of (-0.2 Mpa) of PEG.
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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".