The Adoption of Temperate Selected Sesame Accessions in the Tropics: Selected for Japan and Grown in Ghana
Bibliographic record
Abstract
Sesame is an oilseed crop which can be grown on marginal lands. Selection of stable sesame cultivars that can adapt to local environmental conditions can be a very important food security strategy. A set of 21 high yielding sesame accessions that have been selected for a temperate region were grown in the transitional zone of Ghana during the major season of 2014. The seeds were sown after the first rain in the year in a completely randomized design, with three replications. The objective was to evaluate the effect of the contrasting environment on sesame phenology and to select cultivars with yield potential that can be accepted into local farming systems in the new environment. Morphological, physiological and agronomic traits, leading to yield were recorded in this study. Number of capsules per plant had the strongest association (72%) with seed yield. Five accessions showed a combination of early maturity < 12 weeks with high overall mean seed yield (> 20 g per plant) and good harvest index (0.29). Based on their mean performance these cultivars have been selected as promising exotic cultivars for the new locality.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".