Temperature and Watering Regime Interactions in Shaping Canola Reproductive Yield and Seed Quality
Bibliographic record
Abstract
Crops are continually subjected to frequent and extreme changes in climate, such as high temperatures and soil water deficits. Many studies have shown the individual effects of these factors on plants, but their combined effects on reproductive growth and subsequent seed germinability have received little attention. In this study, we used canola (Brassica napus) plants and grew them through their lifecycle under two temperature regimes (20/10 °C and 24/14 °C, 16 h light/8 h dark) in controlled-environment growth chambers. Half of the plants were watered to field capacity (well-watered) and the other half at wilting point (water-stressed). During the reproductive stage, the flower, silique, and seed traits were measured. Higher temperatures decreased the petal width by 1.17 times but increased petal anthocyanins by 1.03 times. The water deficit decreased the silique length and total seed number by 1.21 and 1.32 times, respectively, but increased nectar sugar concentration by 1.28 times. The total volume of nectar was affected by the interaction of temperature and water. The nectar volume was lowest in the water-stressed plants under higher temperatures (2.66 ± 0.29 µL per flower) but highest in the well-watered plants under the same temperature regime (5.73 ± 0.37 µL per flower). In conclusion, the combined effects of temperature and water were less pronounced than the individual effects of these factors on canola reproductive yield.
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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 | 0.001 | 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".