[Mapping of QTLs controlling Pb(2+) content in maize kernels under Pb(2+)stress].
Bibliographic record
Abstract
Lead (Pb(2+)) is one of the most abundant heavy-metal elements in the environment. Pb(2+) pollution has become increasingly serious in maize planting areas, especially in the southwest of China, which even threatens food security. In the present study, a RILpopulation derived from 178 (an inbred line with low accumulation of Pb(2+) in the kernels) and 9782 (a Pb(2+)-hyperaccumulator in the kernels) was used for QTL mapping. A molecular genetic map with the length of 1499.85 cM and an average inter-marker distance of 9.07 cM was constructed with 165 pairs of SSR markers. QTLs controlling Pb(2+) content in maize kernels were then analyzed to provide the basis for breeding elite maize varieties with low Pb(2+)in the kernels. Two QTLs, qPC1 and qPC4, related to Pb(2+) content in maize kernels were identified on chromosome 1 and 4, respectively. qPC1 was located between markers umc1661 and phi002, accounting for 11.13% of phenotypic variance with an additive effect value of 0.062. While qPC4 was located between markers umc1117 and nc005, explaining 5.55% of the phenotypic variance with an additive effect value of -0.044. However, there was no significant correlation observed between Pb(2+) content in the kernels and any of yield-related traits including ear length, ear diameter, kernel row number and weight of per-hundred kernels, indicating that yield-related traits would not be changed in the process of low-Pb(2+)maize breeding. This suggested that the Pb(2+)content in maize kernel under Pb(2+)stress was an independent genetic trait.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".