Cultivar difference in within-spike N remobilization in CWRS wheat
Bibliographic record
Abstract
The objective of the present study was to compare four new CWRS wheat cultivars with two older cultivars, Neepawa and Marquis, in terms of spike dry matter and nitrogen accumulation and within-spike partitioning. Results showed that new cultivars had significantly higher kernel mass and N content per spike than old cultivars, which were mainly attributed to their higher accumulation rates, instead of accumulation duration. N remobilization from glumes occurred during the linear phase of grain filling and new cultivars started remobilizing N earlier and had a higher rates than old cultivars. N remobilization of rachis started later and the rate was smaller than glumes. New cultivars had higher total N remobilization and maximum remobilization rate from rachis than old ones. It seems that cultivars with large sink size had a high rate of accumulation and were able to remobilize more carbohydrates and N into the grains before maturity. Cultivars with small sink size had both relatively low accumulation rate and remobilization rate. Although visual observations showed that Marquis and Neepawa had 1-3 days longer maturity than new cultivars, grain accumulation of carbohydrates and N was minimal during these days because of the severe water stress and/or heat stress.
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) | 0.000 | 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 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".