Genetic dissection of climacteric fruit ripening in a melon population segregating for ripening behavior
Bibliographic record
Abstract
Melon is as an alternative model to understand fruit ripening due to the coexistence of climacteric and non-climacteric varieties within the same species, allowing the study of the processes that regulate this complex trait with genetic approaches. We phenotyped a population of recombinant inbred lines (RILs), obtained by crossing a climacteric (Védrantais, cantalupensis type) and a non-climcteric variety (Piel de Sapo T111, inodorus type), for traits related to climacteric maturation and ethylene production. Individuals in the RIL population exhibited various combinations of phenotypes that differed in the amount of ethylene produced, the early onset of ethylene production, and other phenotypes associated with ripening. We characterized a major QTL on chromosome 8, ETHQV8.1, which is sufficient to activate climacteric ripening, and other minor QTLs that may modulate the climacteric response. The ETHQV8.1 allele was validated by using two reciprocal introgression line populations generated by crossing Védrantais and Piel de Sapo and analyzing the ETHQV8.1 region in each of the genetic backgrounds. A Genome-wide association study (GWAS) using 211 accessions of the ssp. melo further identified two regions on chromosome 8 associated with the production of aromas, one of these regions overlapping with the 154.1 kb interval containing ETHQV8.1. The ETHQV8.1 region contains several candidate genes that may be related to fruit ripening. This work sheds light into the regulation mechanisms of a complex trait such as fruit ripening.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".