Collection and evaluation of thirty-seven pomegranate germplasm resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it