Grain number determination under contrasting radiation and nitrogen conditions in 2-row and 6-row barleys
Why this work is in the frame
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Bibliographic record
Abstract
Crop growth and developmental rate around the pre-heading phase are important for determining grain yield potential in barley (Hordeum vulgare L.) and other crop cereals. The photothermal quotient, Q (ratio between photosynthetically active radiation (PAR) and temperature) around the flowering period has been found to be a good predictor of grain number per unit area under potential growing conditions when both solar radiation and temperature vary, but not under suboptimal nitrogen (N) conditions. Under suboptimal conditions, Q might not account for differences in grain number due to modifications in radiation-use efficiency (RUE), biomass partitioning between vegetative and reproductive organs, fruiting efficiency, and/or a combination of these factors. This paper aims at providing insights into how grain yield is defined during the pre-heading phase in 2- and 6-row barleys under contrasting N and radiation environments, using a model proposed by RA Fischer for grain number determination. Nitrogen and radiation treatments affected grain number, and consequently grain yield, through changes in spike biomass at heading, and not by a direct N effect. When low and high N conditions were included, Q poorly explained variations in grain number. Nitrogen increased RUE during the pre-heading phase. When accumulated PAR intercepted between the maximum number of spikelet primordia and heading stages (PARia) was considered together with RUE, the accuracy of the model was increased. Nitrogen slightly increased biomass partitioning between reproductive and vegetative organs, but it was not strong enough to improve the model between PARia and grain number. In the case of fruiting efficiency, genotype × N and shading × N interactions highlighted that this trait was maximised when 6-rowed barleys and shading were imposed under the high N treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it