Classification of Rice Based on Statistical Analysis of Pasting Properties and Apparent Amylose Content: The Case of <i>Oryza glaberrima</i> Accessions from Africa
Bibliographic record
Abstract
ABSTRACT The diversity of 1,020 Oryza glaberrima rice accessions being kept at the Genetic Resources Unit of the Africa Rice Center with a varied range of apparent amylose content (AAC) and pasting properties was explored with cluster analysis. Rice cultivars are usually characterized according to grain dimensions, AACs, and gelatinization temperatures; however, this work focused on grouping African rice accessions based on their pasting properties and AAC. Using the Ward method of hierarchical cluster analysis, 1,020 rice accessions were initially distributed into five major clusters and further into 23 subclusters. The distribution pattern indicated that clusters I, II, III, IV, and V formed 27.6, 10.2, 15.8, 23.7, and 22.6% of the entire population, respectively. Although some of the groups had similar AAC, their pasting properties were very different, making it imperative for further investigations. Peak viscosity highly correlated (P < 0.01) with trough, breakdown, and final viscosities in all five clusters, whereas correlation between peak viscosity and AAC was not significant within clusters II and IV. Additionally, this categorization serves as a tool for exploring materials that can be employed in the development of rice cultivars for specific end uses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".