Inheritance of Time to Flowering in Chickpea in a Short-Season Temperate Environment
Bibliographic record
Abstract
Time to flowering is central in determining the adaptation and productivity of chickpea in short-season temperate environments. We studied the genetic control of this trait in three crosses, 272-2 x CDC Anna, 298T-9 x CDC Anna, and 298T-9 x CDC Frontier. From each cross, 180 F2 plants and parents were evaluated for time to flowering under greenhouse conditions. In summer 2004, multiple generations including P1, F1, P2, F2, and F2:3 (also called MG5) were evaluated for time to flowering under field conditions. The data on time to flowering in the F(2) populations were continuous in distribution but deviated from normal distribution. The F2:3 families derived from this showed a bimodal distribution for time to flowering, a typical case of major-gene inheritance model with duplicate recessive epistasis. A joint segregation analysis of MG5 also revealed that time to flowering in chickpea was controlled by two major genes along with other polygenes. Late flowering was dominant over early flowering for both major genes with digenic interaction between them, mainly an additive x additive type. This information can be used to formulate the most efficient breeding strategy for improvement of time to flowering in chickpea in short-season temperate environments.
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 | 0.001 | 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".