Identification, selection and observation of Nuansa Sanggabuana soybean, its yield and resistance to diseases
Bibliographic record
Abstract
Study to get Nuansa Sanggabuana (NS) of Karawang soybean variety to be released to become superior variety was conducted district of Karawang, West Java province, from 2010 to 2014. The study consisted of identification, purification and description, adaptation test. The identification step was conducted positive mass selection; purification was done a negative mass selection, adaptation yield test and also resistances to diseases. The test was arranged in randomized block design by comparing NS Karawang with Anjasmoro, Argomulyo, Orba, Grobogan, and Rajabasa which was repeated 4 times. Identification result had two NS Karawang local variants based on colour on the trunk, which were grey trunk fur and brown trunk fur. Variant which was purified for the next generation was one brown trunk fur. On-off type individual purification step was discarded and made description based on UPOV standard. Adaptation test result showed that differences between NS Karawang and Anjasmoro, Argomulyo, Orba, Grobogan, and Rajabasa variety on plant’s height, number of productive trunk, number of pod for each plant, weight of 100 grains, yield per plant, yield per unit, and yield per hectare. NS Karawang had higher yield than Anjasmoro, Argomulyo, Orba, and Rajabasa but lower yield than Grobogan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".