Interaction between parental environment and genotype affects plant and seed performance in Arabidopsis
Bibliographic record
Abstract
Seed performance after dispersal is highly dependent on parental environmental cues, especially during seed formation and maturation. Here we examine which environmental factors are the most dominant in this respect and whether their effects are dependent on the genotypes under investigation. We studied the influence of light intensity, photoperiod, temperature, nitrate, and phosphate during seed development on five plant attributes and thirteen seed attributes, using 12 Arabidopsis genotypes that have been reported to be affected in seed traits. As expected, the various environments during seed development resulted in changed plant and/or seed performances. Comparative analysis clearly indicated that, overall, temperature plays the most dominant role in both plant and seed performance, whereas light has a prominent impact on plant traits. In comparison to temperature and light, nitrate mildly affected some of the plant and seed traits while phosphate had even less influence on those traits. Moreover, clear genotype-by-environment interactions were identified. This was shown by the fact that individual genotypes responded differentially to the environmental conditions. Low temperature significantly increased seed dormancy and decreased seed longevity of NILDOG1 and cyp707a1-1, whereas low light intensity increased seed dormancy and decreased seed longevity of NILDOG3 and NILDOG6. This also indicates that different genetic and molecular pathways are involved in the plant and seed responses. By identifying environmental conditions that affect the dormancy vs longevity correlation in the same way as previously identified naturally occurring loci, we have identified selective forces that probably shaped evolution for these important seed traits.
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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 | 0.000 | 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) | 0.001 | 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".