Effectiveness of selection for quality traits during the early stage in the potato breeding population
Bibliographic record
Abstract
Abstract Potato cultivars resistant to cold‐temperature sweetening are of major importance to the processing industry producing both chips (crisps) and French fries. When most modern potato cultivars are maintained in cold storage to retard sprouting, the tubers accumulate reducing sugars, and the products become an unacceptable brown colour when fried. Selection for better processing quality during the early generations of a breeding programme could be of considerable advantage. Using a portable ‘sugarmeter’, which requires only a drop of sap from the tuber on a test strip, many samples can be efficiently surveyed for low sugar as early as the F1 generation. Using seedlings of three test crosses, glucose and specific gravity of field‐grown tubers, minitubers from greenhouses and microtubers from in vitro culture were compared after cold treatment. Although the mean glucose levels of minitubers and microtubers were higher than field‐grown tubers, the correlation between the glucose contents of the three types of tubers was fairly high. A considerable genetic improvement was noted when progenies were grown as minitubers or microtubers, even though the response to selection for low glucose levels in minitubers and microtubers was lower than from direct selection from field‐grown tubers. The specific gravity of field‐grown tubers showed a significant association with freshly harvested minitubers and microtubers. Selection for low glucose content in minitubers can therefore save considerable resources in a breeding programme.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".