Physiological Quality of Salvia hispanica L. Seeds at Differents Paraquat Application Moments
Bibliographic record
Abstract
Chía (Salvia hispanica L.) seed contains more oil and protein than other grains, has a high content of omega-3 linolenic acid, essential for human nutrition. Paraguay is currently among the five largest chía producer countries in the world, with 80 000 ha planted in 2013. The experiments were carried out during the months of March and October of 2017, the field work was made in the district of Chore-San Pedro, Paraguay departament, and laboratory evaluations in the Seed Analysis and Quality Laboratory of Agrarian Sciences, National University of Asunción-Paraguay. The objective of this investigation was to determine the physiological quality effect on chia seeds with differents paraquat moments application. The variables study consisted in evaluate the seeds germinative power, calculate the vigor by measuring length of seedlings and accelerated aging, also estimate different harvest times yield. The randomized complete blocks design was used with 4 treatments with 4 repetitions. The obtained results were analyzed by ANOVA and the means were compared using the Tukey test at 5% probability. The Paraquat herbicide was applied as a desiccant to accelerate and standardize the chia harvest at different maturing times, called as early, middle and late. In all the analyzed variables it is shown that the early application obtained the best results concluding that the application of paraquat in early harvest favors the yield and the final quality of the seeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".