Identification and Validation of Quantitative Trait Loci Associated with Fruit Puffiness in a Processing Tomato Population
Bibliographic record
Abstract
Physiological disorders impact the yield and quality of marketable fruit in tomato. Puffy fruit caused by cavities inside the locule can be problematic for processing and fresh market quality. In this paper, we used a recombinant inbred line (RIL) and three derived processing tomato populations to map and validate quantitative trait loci (QTLs) for fruit puffiness across environments. Binary interval mapping was used for mapping the incidence of fruit puffiness, and non-parametric interval mapping and parametric composite interval mapping were used for mapping severity. Marker-trait regressions were carried out to validate putative QTLs in subsequent crosses. QTLs were detected on chromosome (Chr) 1, 2, and 4. Only the QTL on Chr 1 was validated in progeny from subsequent crosses. This QTL explained up to 22.5% of the variance in the percentage of puffy fruit, with a significant interaction between loci on Chr 2 and 4, increasing the percentage of puffy fruit by an additional 15%. The allele responsible for puffy fruit on Chr 1 was inherited from parent FG02-188 and was dominant towards increased incidence and severity. Marker-assisted selection (MAS) for the QTL on Chr 1 was as efficient as genomic selection (GS) in reducing the incidence and severity of puffy fruit, despite the potential contribution of other loci.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".