Quality Attributes of Okra (Abelmoschus esculentus L. Moench) Pods as Affected by Cultivar and Fruit Size
Bibliographic record
Abstract
Okra is a vegetable widely grown in the tropics, sub-tropics and warmer areas of the temperate zones. Fruit quality plays an important role in marketability and it is mainly related to the characteristic pod length. This work was intended to classify okra fruits belonging to different genotypes (a local variety - LV - and the cultivars Emerald, Clemson Spineless and Annie Oakley II) according to their length and to compare certain quality attributes. LV fruits were considerably firm although they were smaller, thus they should be more suitable for canning or pickling. The hybrid material (Annie Oakley II) yielded high quality fruits. These fruits may be destined for fresh consumption, given that they showed lower fibrousness. At the same time, they had high dry matter content, being suitable for dehydration. Okra fruits highlighted for their contribution of phenolic compounds.Total phenols levels significantly increased with fruit size in LV fruits, meanwhile no significant difference was observed for Annie Oakley II samples. Total flavonoids content showed a similar tendency although values did not differ significantly. Total flavonoids represented between 18-22% of the total phenols contents for the analyzed samples.
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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 | 0.000 | 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".