The Analysis on Fat Characteristics of Walnut Varieties in Different Production Areas of Shanxi Province
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Bibliographic record
Abstract
To gain knowledge of the fat characteristics of walnut varieties in different production areas of Shanxi Province, kernel oil content and its fatty acid composition of 6 walnut varieties were analyzed by using soxhlet extraction method and gas chromatography. The experiment sites were Yicheng and Tunliu. Yicheng has 800 m of altitude and 11 ºC of annual mean temperature while Tunliu has 1100 m of altitude and 9.3 ºC of annual mean temperature. The results indicated between Yicheng and Tunliu County, the most variety’s oil content was increased with increasing of altitude, but had no significant difference (64.9% > 64.3%, P > 0.05). Both the content of oleic acid and linoleic acid showed a significant difference between Tunliu and Yicheng, and there is a negative relationship between the contents of oleic and linoleic acid. Annual mean temperature has an obvious influence on ?-linolenic acid (ALA) content of walnut. The content of ALA from Yicheng with higher annual mean temperature is higher than that from Tunliu with lower annual mean temperature. The nutrition analysis showed that Tunliu’ walnut kernel has a lower saturated fatty acids (SFA) (7.6% < 8.2%, P < 0.05) and a higher unsaturated fatty acids (UFA) (92.5% > 91.8%, P < 0.05) compared to Yicheng, respectively, but the polyunsaturated fatty acids (PUFA) content of Yicheng was higher than that of Tunliu (76.3% > 68.5%, P < 0.05), and its ratio of N-6/N-3 was also better compared to Tunliu (5.4:1 < 5.9:1). These results showed that with the different altitude and annual mean temperature, the walnut nutrition of fat is also different. Higher altitude and lower annual mean temperature can help to produce oleic-rich walnut while lower altitude and higher annual mean temperature is helpful for the higher content of ALA in walnut kernel.
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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)
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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