Reliability of Measurement and Genotype × Environment Interaction for Potato Specific Gravity
Bibliographic record
Abstract
Specific gravity (SpGr) is often used to measure the processing quality of potato ( Solanum tuberosum L.) tubers for French fries or potato chips because of its strong correlation with dry matter content and ease of measurement. For French fry processing genotypes, the desirable range for mean SpGr is typically 1.080 to 1.095, and a small variance around the mean is essential for product uniformity. Two multi‐year, multi‐location trials were conducted to investigate the genetics of SpGr in elite russet germplasm. Consistent with earlier studies, the mean SpGr was measured with high repeatability within each environment: the median plot‐basis value was 0.83 for a national trial with six locations and 3 yr. In contrast, the median repeatability of the SD between tubers was only 0.21. Thus, multi‐environment trials are needed to identify genotypes with a narrow SpGr distribution. Finlay–Wilkinson stability analysis of the mean SpGr established one genotype as an outlier: when best linear unbiased predictions were regressed on the environment means, this genotype had a regression coefficient of 2.1, compared with 0.4 to 1.4 for the others. The genetic correlation between environments showed a consistent regional pattern in mean SpGr over the years. There was a higher mean correlation between environments within the Pacific Northwest (0.97), Upper Midwest (0.91), and Northeast (0.85) than between environments from the different regions (0.35–0.78). Although breeding for national adaptation is an attractive idea, our results suggest that genetic gain may be easier to achieve at the regional level.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".