Bibliographic record
Abstract
Indirect selection in early generations through traits having heritability higher than yield as well as correlated significantly with seed yield is one of the most important breeding procedures.Production of new cultivars adaptable to different environments also has importance for wheat breeders.Cross among new cultivars and selection of superior genotypes among their progenies based on suitable traits is efficient breeding procedures.Therefore، in order to determination of the most yielding bread wheat genotypes، identification of the traits affective on seed and protein yield as well as parents of the best crosses an experiment was conducted 2011-2012.The randomized complete block design with three replications was used.Bread wheat genotypes comprised; Parsi and Sivand cultivars along with 18 lines entitled M-90-3 to M-90-20.Correlation، step-wise regression and path analysis designated that grain filling rate and no.spike/m 2 are the efficient indirect selection criteria to increase seed yield.Increasingly، peduncle length، no.seed/spike and no.spikelet/spike were recommended to improve spike yield while peduncle diameter، days to flowering، days to maturity and plant height for photosynthetic reservoir.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.923 | 0.953 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".