The Quest for the Sli Locus
Bibliographic record
Abstract
Abstract Genetic gain in potato breeding is limited by the heterozygous tetraploid genome of cultivated potato. Recent efforts to breed potato at the diploid level promise to improve genetic gain and allow more straightforward genetics and introgression breeding. Diploid F1 hybrid potato breeding relies on the ability to create diploid inbred lines via repeated self-fertilization. However, self-fertilization of diploid potato is hampered by a gametophytic self-incompatibility system encoded by the S-locus that prevents fertilization by self-pollen. Nonetheless, self-compatible diploid potato genotypes exist and have been used to create inbred lines. The S-locus inhibitor ( Sli ) gene is a dominant gene that provides strong self-compatibility in diploid potato and was previously mapped to Chromosome 12. While the Sli gene has already been identified and characterized, the most tedious challenge was to develop the optimal phenotyping methods and genetic populations preceding the cloning of this gene. To this end, we developed an effective phenotyping protocol to identify suitable parents and create diploid populations segregating for Sli . We show that an accurate phenotyping method is crucial to discriminate between confounding fertility factors and self-compatibility. In addition, we found that the Sli locus shows extreme segregation distortion on Chromosome 12. Finally, we used these insights to develop three F1 populations that segregate for Sli , which we later used for the identification of the Sli gene.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".