Perspectives on cereal science and technology: Papers presented at the 15th International Cereal and Bread Congress, 18 to 21 April 2016, Istanbul (Turkey)
Bibliographic record
Abstract
Fluctuations of environmental conditions increase variability in both grain yield and quality of wheat (Triticum aestivum L.). In order to evaluate stability, different stability parameters of the static or the dynamic concept can be applied, which could be negatively correlated. While correlation analysis of the mean performance between traits is common, correlations between stability estimates for quality traits have not been investigated, to the best of the authors' knowledge. Therefore, indirect, rheological and baking traits, and grain yield from two datasets were analysed, and the mean performance, each three stability parameters of the two different concepts (static and dynamic) were calculated over all environments within each dataset.Results showed that stability parameters of the same concept were significantly positively correlated for almost all traits. Between the stability concepts, there was only one significant negative correlation, indicating that no trade-off between the two stability concepts exists. A trade-off between stability and mean performance occurred only for five traits, suggesting only a weak trade-off between stability and mean performance, allowing the development of cultivars that are of both stable and of high quality. Clusters of traits that showed similar stability could be identified but were not consistent across stability parameters and datasets.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".