Genotype by environment interactions of heat stress disorder resistance in crisphead lettuce
Bibliographic record
Abstract
Abstract Lettuce is a cool season crop susceptible to physiological disorders when exposed to supra optimal temperatures. Genotype (G) by environment (E) interaction (GE) of rib discolouration, tipburn, premature bolting and ribbiness in crisphead lettuce was characterized under high temperature and long day growing conditions. Replicated data of 10 crisphead lettuce varieties from two plantings in each of four growing seasons at two locations in Quebec were analysed using the GGE biplot method. Head‐weight‐over‐stem‐length ratio, ribbiness, rib discolouration incidence and tipburn incidence were affected (P = 0.00001) by E, G and GE. E explained more variation in head‐weight‐over‐stem‐length ratio (77.1%) and rib discolouration incidence (77.3%) than G and GE, whereas GE explained more variation (72.4%) in tipburn incidence and G explained more variation (38%) in ribbiness. Strong crossover GE was detected with rib discolouration and tipburn incidence, but not with head‐weight‐over‐stem‐length ratio and ribbiness. Under heat stress, varieties of the Vanguard group had lower ribbiness than those of the Great Lakes group. Cultivar ‘Estival’ showed consistent resistance to bolting, ribbiness, tipburn and rib discolouration across all E.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".