Collection and evaluation of thirty-seven pomegranate germplasm resources
Bibliographic record
Abstract
Abstract Pomegranates (Punica granatum L.) are gaining popularity among consumers because of their high antioxidant activity and multiple medical benefits. China is rich in pomegranate genetic resources, but how to use them effectively is a problem worthy of deep consideration. In this article, thirty-seven pomegranate varieties from seven provinces in China were collected and analyzed for twelve phenotypic traits and twelve biochemical indicators (seeds and juices). The fruit and aril fresh weight ranged between 210.5 and 576.5 g and 121.0 to 327.5 g, respectively, and the edible rate (42.58–64.80%), seed weight (1.80–3.41 g), seed number (249.1–838.9), fruit height (10.51–15.48 mm), fruit diameter (11.46–17.50 mm), skin thickness (2.14–6.98 mm), and shape index (0.82–0.96) varied among the different genotypes. The pomegranate juice total phenolic content ranged from 40.91 to 132.47 µg/mL, and the total flavonoid content (14.08–137.72 µg/mL), vitamin C content (12.80–66.63 µg/mL), pH (3.10–4.34), total soluble solids (13.13–17.50°Brix), and titratable acidity (0.26–2.71%) also varied; the pomegranate seed total phenolic content ranged from 0.62 to 1.78 mg/g, and the total flavonoid content (0.39–0.99 mg/g), vitamin C content (7.55–13.90 mg/g), DPPH radical scavenging capacity (85.98–98.24%), and ABTS scavenging ability (28.72–51%) were also measured. The coefficients of variation of the studied traits ranged from 5.62 to 54.02%, and the phenotypic traits’ Shannon–Weaver diversity indexes ranged from 0.67 to 1.53. Cluster analysis divided the 37 varieties into three categories, providing a reference for improved variety breeding. In addition, genotypic and environmental effects mainly affected the pomegranate flavor and antioxidant activity, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".