Correlation of Kennedy pathway efficiency with seed oil content of canola (<i>Brassica napus</i> L.) lines
Bibliographic record
Abstract
Chen, J. M., Qi, W. C., Wang, S. Y., Guan, R. Z. and Zhang, H. S. 2011. Correlation of Kennedy pathway efficiency with seed oil content of canola (Brassica napus L.) lines. Can. J. Plant Sci. 91: 251–259. The Kennedy pathway (KP) has been demonstrated to be crucial for seed oil accumulation. To answer whether the KP enzyme activities can explain seed oil content variation of conventional canola lines, six lines with three different levels of oil (high, middle and low) were tested for seed KP enzyme activities and hexose (2 sucrose+glucose+fructose) concentrations at 18, 25, 32, 39 and 46 d after pollination (DAP). The results showed that lines with high and middle seed oil content levels (HO and MO) had higher phosphatidate phosphatase (PAP) and diacylglycerol acyltransferase (DGAT) activities and higher sugar contents than the lines with low oil levels (LO). The lowest KP enzyme activity (LEA) for each combination of line and DAP can be regarded as indicator to the bioassembly efficiency. In most of the combinations, DGAT was the enzyme with LEA, and glycerol-3-phosphate acyltransferase (GPAT) or PAP acted as the enzyme with LEA in a few cases. Correlation analyses showed that peak values and averages of LEA in the lines were significantly correlated with seed oil content, indicating that KP enzyme efficiency is tightly associated with seed oil content.
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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 | 0.001 | 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".