An analysis of selection on candidate genes for regulation, mobilization, uptake, and transport of iron in maize
Bibliographic record
Abstract
Insufficient iron (Fe) availability, which frequently occurs in soils with high pH levels, can lead to leaf chlorosis, a reduced Fe content in harvest products, and yield reduction in maize. The objectives of this study were (i) to describe patterns of sequence variation of 14 candidate genes for mobilization, uptake, and transport of Fe in maize, as well as regulatory function on these processes; (ii) to examine whether Fe-efficiency is an adaptive trait by determining if these genes were targets of selection during domestication; and (iii) to test if the allele distribution at these candidate genes is different for the different subpopulations of maize. The nucleotide diversity of Mtk was reduced by 78% in maize compared with teosinte. The results of our study revealed for the genes Naat1, Nas1, Nramp3, Mtk, and Ys1 a selective sweep, which suggests that these genes might be important for the fast adaptation of maize to new environments with different Fe availabilities.
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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 | 0.000 |
| 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".