Bibliographic record
Abstract
Temperatures greater than 27°C, in a growth cabinet, have resulted in floral sterility and yield loss in Brassica napus L. Maximum daily temperatures often exceed this in the major canola growing regions. Our objective was to examine the effects of heat stress during flowering on yield and yield components of B. napus, B. rapa L., and B. juncea (L.) Czernj. & Cosson. Cultivars of the three Brassica species were grown in a split‐plot design in the field for 3 yr at Ottawa, Canada. Three seeding dates were used each year to obtain different levels of heat stress during flowering. The number of flowers on the main raceme and the first and second branch racemes were counted at anthesis, from 10 plants per plot. At maturity, pods from the sampled plants were counted and seed numbers and weight determined. Two rows of the remainder of each plot was harvested for seed yield. A heat stress index was developed on the basis of the accumulation of daily maximum temperatures greater than a threshold temperature. The threshold temperature during flowering, which resulted in seed yield losses, was 29.5°C for all Brassica species. High mean maximum temperature during vegetative development resulted in a reduction in flower number for all Brassica species. Seed yield decreased as heat stress during flowering increased. The reduction in seed yield was primarily due to a reduction in flower number and in the number and size of the seeds produced per flower. Plant breeders should actively select for heat stress tolerance in future canola cultivars.
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".