Haploidy in Cultivated Wheats: Induction and Utility in Basic and Applied Research
Bibliographic record
Abstract
The usefulness of haploid plants in basic research in cytogenetics, genetics, evolution, and practical plant breeding is well known. Haploid plants provide an efficient research tool for studies on induced mutagenesis and genetic transformation. They also help elucidate the genetic control of chromosome pairing inherently present in allopolyploids such as bread wheat, durum wheat, and oats. Genetic control of chromosome pairing in haploid nuclei has helped in assessing intergenomic relationships. By analyzing the degree and specificity of chromosome pairing in the Ph1‐ and ph1b‐euhaploids (2n = 3x = 21; ABD), we demonstrated that the A and D genomes of wheat are more closely related to each other than either one is to the B genome. It is significant that the totipotent nature of a haploid cell is being exploited in several facets of biological research. In addition to its numerous applications in basic research, the haploidy approach provides an efficient means of producing truly homozygous lines, thereby accelerating the breeding process. Wheat cultivars developed from doubled haploids (DHs) have been released for cultivation in Canada, China, Europe, and Brazil. General characteristics and classification of haploids derived from diploid and polyploid species are provided in this article. Methods of extracting haploids of polyploid wheats are described, and applications of haploidy in basic and applied research are discussed.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".