Comparison of Yield and Content of Anthocyanin of Purple Corn of Different Combining Types
Bibliographic record
Abstract
Content and yield of anthocyanin in grain,cob,bract and tassel of purple corn of different combining types were studied using 16 purple corn hybrids grouped by 18 maize inbred lines.The results showed that:1. Differences of content of grain anthocyanin,grain yield and grain anthocyanin yield of purple corn of different combining types were all great.Content of anthocyanin of Deep Purple corn×Deep Purple corn was the highest;grain yield of Deep Purple non-waxy corn×waxy corn was the highest;yield of grain anthocyanin of Deep Purple corn×Deep Purple corn was the highest.2.Yield and content of cob anthocyanin of Deep Purple corn×Deep Purple corn were both the highest;content and yield of bract anthocyanin of Purple non-bract corn×Deep Purple bract corn were both the highest;content and yield of tassel anthocyanin of Purple non-tassel corn×Deep Purple tassel corn were both the highest.3.Content of cob and grain anthocyanin were highly significant positive correlation;content of tassel and bract anthocyanin were also highly significant positive correlation.4.Anthocyanin total yield of purple corn(grain,cob,husk,tassel) was Deep Purple corn×Deep Purple cornDeep Purple corn×Shallow Purple cornnon-purple corn×Deep Purple cornShallow Purple corn x non-purple corn;yield of grain anthocyanin made the greatest contributions to total yield of anthocyanin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".