Vernalisation and photoperiod responses of diverse wheat genotypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Context Wheat (Triticum aestivum L.) adaptation is highly dependent on crop lifecycle duration, particularly the time at which flowering occurs in a specific environment. Frost, low solar radiation, heat and drought can significantly reduce yield if a crop flowers too early or late. Wheat genotypes have different lifecycle durations determined by plant responses to temperature (thermal time accumulation and vernalisation) and photoperiod. These responses are largely controlled by five phenology genes (two PPD1 and three VRN1 genes). Advances in crop phenology modelling suggest that flowering time under field conditions could be accurately predicted with parameters derived from photoperiod and vernalisation responses obtained in controlled environments. Aims This study quantified photoperiod and vernalisation responses of 69 Australian wheat genotypes selected for diversity at the PPD1 and VRN1 loci. Methods Spring and winter genotypes were grown in four controlled environments at a constant temperature of 22°C with photoperiod (17 or 8 h) and vernalisation (0 or 8 weeks) treatments as factors. Key results Thermal time from coleoptile emergence to flowering in spring genotypes was typically decreased more by long photoperiod than by vernalisation; the opposite was true for winter genotypes. Spring genotypes that were sensitive to vernalisation contained a sensitive allele at the Vrn-A1 locus. Conclusions There is large diversity in phenological responses of wheat genotypes to photoperiod and vernalisation, including among those with matching multi-locus genotype. Implications Data from this study will be used to parameterise and test a wheat phenology model in a future study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it