<i>Corrigendum to</i> : Identification of salt tolerance QTL in a wheat RIL mapping population using destructive and non-destructive phenotyping
Bibliographic record
Abstract
Bread wheat (Triticum aestivum L.) is one of the most important food crops, however it is only moderately tolerant to salinity stress. To improve wheat yield under saline conditions, breeding for improved salinity tolerance of wheat is needed. We have identified nine quantitative trail loci (QTL) for different salt tolerance sub-traits in a recombinant inbred line (RIL) population, derived from the bi-parental cross of Excalibur × Kukri. This population was screened for salinity tolerance subtraits using a combination of both destructive and non-destructive phenotyping. Genotyping by sequencing (GBS) was used to construct a high-density genetic linkage map, consisting of 3236 markers, and utilised for mapping QTL. Of the nine mapped QTL, six were detected under salt stress, including QTL for maintenance of shoot growth under salinity (QG ( 1-5 ) .asl -5A , QG ( 1-5 ) .asl -7B ) sodium accumulation (QNa.asl -2A ), chloride accumulation (QCl.asl -2A , QCl.asl -3A ) and potassium : sodium ratio (QK :Na.asl -2DS2 ). Potential candidate genes within these QTL intervals were shortlisted using bioinformatics tools. These findings are expected to facilitate the breeding of new salt tolerant wheat cultivars. Soil salinity causes major yield losses in bread wheat, which is moderately tolerant to salinity stress. Using high throughput genotyping and phenotyping techniques, we identified quantitative trail loci (QTL) for different salt tolerance sub-traits in bread wheat and shortlisted potential candidate genes. These QTL and candidate genes may prove useful in breeding for salt tolerant wheat cultivars.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.065 |
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".