Stimulantia Effect Of Single Bulb Garlic Extract (Allium Sativum Var.Solo Garlic) in Swiss Webster Mice
Bibliographic record
Abstract
Stimulant is an agent that stimulates the central nervous system thereby increasing physical and mental abilities and minimizing fatigue. The use of synthetic caffeine stimulants of 10 mg / kg BW is known to have side effects of increasing total cholesterol and increasing LDL, therefore alternative stimulants from natural ingredients are needed. Natural materials that have been studied contain flavonoids and phenolic as a stimulant compound is a single garlic bulbs. The purpose of this study was to determine the stimulant effect of a single garlic bulbs ethanolic extract on mice from the difference in swimming time. The research experimental used Pre test and Post test control design. Sample of this research used mice which were divided into 6 groups. Group 1 pretest dose 5 g / kgBB, group 2 (negative control), group 3 (caffeine), group 4 extract dose 5g / kgBB, group 5 dose 10g / kgBB and group 6 dose 20g / kgBB. Data was analyze using one way Anova continued with Post Hoc test. The group of single garlic bulb ethanolic extract dose 20 g / kgBB had the highest stimulant effect with 222,722 minutes fatigue time difference and statistically have significant difference (p <0.05) than the negative control group. Group of single garlic bulb extract can influence the time of fatigue of mice by extending the swimming time of mice so that it has a longer fatigue time which means it has a stimulant effect
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".