Bibliographic record
Abstract
Purity of hybrids seed is one of the key factor influencing maize industry.To establish rapid and accurate purity detection technique is an effective measure for maize seed purity controlling.Six primers were selected from 30 pairs of SSR,which gave stable and polymorphic amplification profiles.The amplification bands of hybrids were complementary with that of the parents.Umc 1002,bnlg 1112,bnlg 238,Umc 1153,phi 072 and bnlg 240 could detect the purity of Nongda No.108,Xundan No.20,Zhengdan No.958,Lainong No.14,Lainong No.15 and Luyu No.16,respectively.Alleles frequency of the complement bands were relatively low with a range of 0.028-0.072,which ensured to separate self-cross accurately and off-type individuals with hybrid plants.The results obtained by SSR markers were a little less than that of field plot planting testing,but there were no significant difference when statistical analysis was performed.The correlation coefficient was 0.8434.The SSR identification system established in this paper could be used to identify seed purity of six maize hybrid varieties rapidly.
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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 | 0.001 |
| 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 | 0.000 |
| 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".