Effects of two training systems on sugar metabolism and related enzymes in cv. Beibinghong (<i>Vitis amurensis</i> Rupr.)
Bibliographic record
Abstract
Liu, L., Nan, L., Zhao, X., Wang, Z., Nan, H. and Li, H. 2015. Effects of two training systems on sugar metabolism and related enzymes in cv. Beibinghong (Vitis amurensis Rupr.). Can. J. Plant Sci. 95: 987–998. Eight individual sugars and four sugar-metabolism-related enzymes were investigated in the development of Vitis amurensis Rupr. ‘Beibinghong’ grape berries. Two different training systems, vertical shoot positioning (VSP) and Y-shaped training system (Y-shape) were applied. Sucrose contents in the two training systems were significantly related to the sucrose phosphate synthase (SPS) and sucrosynthetic activity of sucrose synthase (SS-s) in berries before veraison. The results show that throughout the veraison period, individual sugars, such as glucose, fructose, galactose and lactose, increased. Glucose and fructose were affected by both training systems, mainly at 15 and 16 wk (weeks after fruit setting). Training systems had no significant effects on the rhamnose, arabinose, galactose and maltose contents, and barely had an effect on the lactose content. The VSP training system mainly affected the sucrose content during the harvest period, while the Y-shape affected sucrose content mainly after 9 wk. During 2011 to 2013, VSP and Y-shape strongly affected the sucrose contents before veraison, and also affected the cleavage activity of sucrose synthase (SS-c) mainly between 5 and 8 wk; however, different training systems barely affected the soluble acid invertase (SAI) activities in whole berry growth. From the perspective of the whole berry development, the results showed that different systems had no significant effects on individual sugars and enzymes.
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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) | 0.001 | 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 | 0.001 |
| 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".