Characterising canola pollen germination across a temperature gradient
Bibliographic record
Abstract
If predictions are correct, heat stress during reproduction will become a yield limiting factor in many world crops and breeding heat stress tolerance a major goal. The objective of our paper was to highlight a novel system to investigate the influence of temperature (T) on pollen germination using a thermal gradient PCR programmed to establish differential Ts across 12 wells of a PCR plate. Seven cultivars of Brassica napus L. were grown through flowering in a cool growth cabinet (20/15°C day/night) or a heat stress cabinet (HST, 27/22°C day/night). Pollen from each cultivar × cabinet combination was aspirated from 6 opened flowers, and suspended in germination media. Drops of the pollen suspension were floated on media in each well, and the PCR T was set to 30°C with a gradient of ± 10°C, creating a range from ~20 to 40°C from left to right. After an 8 h treatment, the pollen germination (pg, %) and pollen tube growth score (ptg, 1–5) were evaluated using a microscope. There were significant differences among cultivars for pg and ptg score and significant differences among well T for pg and ptg score. Pollen tubes grew best at T from 20 to 23°C. Well T exceeding 33°C reduced pg and ptg score, although 3 of the 8 cultivars had good pg even at 36°C. HST >29°C, in a growth cabinet, generally resulted in B. napus raceme sterility, although our experiment showed that pollen was still capable of germinating up to 33°C, indicating that pollen germination may not be the only reason for heat stress susceptibility.
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.
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".