The performance and genetic variation of first and second generation tropical alfalfa (Medicago sativa)
Bibliographic record
Abstract
Abstract. Suwignyo B, Arifin L, Umami N, Muhlisin, Suhartanto B. 2021. The performance and genetic variation of first and second generation tropical alfalfa (Medicago sativa). Biodiversitas 22: 3265-3270. This study aimed to compare the growth performance, nutrient content, seed viability, and genetic variation of first- and second-generation alfalfa (Medicago sativa L.). First and second-generation alfalfa seeds were obtained from the Forage and Pasture Science Laboratory, Department of Animal Nutrition and Feed Science, Faculty of Animal Science, Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia. First generation alfalfa (F1) seeds were obtained from cross breeding of two different parental alfalfa varieties, namely, Canadian and local. The second-generation (F2) seeds were obtained from plants of the first-generation alfalfa (F1). A randomized design experiment was conducted using the two types of alfalfa (first- and second generation). Alfalfa from Canada as female parent was used as the baseline in the genetic masker test. Seeds were planted in a polybag, watered twice a day, and received 12 hours of daylight and 4 hours of artificial light. Plants were then harvested 8 weeks after planting by cutting the plant canopy. Genetic variation was examined using the Inter Simple Sequence Repeat (ISSR) method followed by descriptive analysis. Germination, plant height, dry matter content, organic matter, and crude protein were assessed as variables using a Student’s T-test. Our results showed that germination, plant height, leaf color, and nutrient content (dry matter, organic matter, and crude protein) of the first- and second-generation alfalfa plants were not significantly different. However, the second-generation alfalfa demonstrated better seed viability than the first generation plants, then it can be categorized as a new genotype (tropical alfalfa) based on genetic variation analysis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".