Performance of Acid-Tolerant Soybean Promising Lines in Two Planting Seasons
Bibliographic record
Abstract
Phenotypic performance of a genotype is not similar from season to season depends on the environment condition such as amount and time of water availability. Objective of this study was to identify soybean promising lines with high yield on acid soil in two planting seasons. The research was conducted in rainy season 2009 and 2010 at Sitiung Research Station, West Sumatera, Indonesia. The soil of the experimental field is Ultisols with pH (H2O) 4.3, exchangeable-Al 3.92 me/100 g, and Al saturation 56.48%. The experiment was arranged in randomized completely block design with four replications. Result showed that the average of seed yield in RS 2010 was higher than that in RS 2009. Water supply was very important in pod filling period, plant height, filled pod, flowering and maturity days that may lead the seed yield performance. Based on the average of yield in two planting seasons, there were two genotypes having seed yield higher than the highest check variety, i.e. genotypes of SC5P2P3.23.4.1-3-28-3 and SC5P2P3.23.4.1-5. Therefore, these two genotypes are possibly to be released as new acid-tolerant varieties or can be served as new genetic materials for developing acid-tolerant variety.
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 | 0.000 | 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".