Relationships of Pinnate (Fern) and Simple (Unifoliate) Leaf Traits with Seed Yield and Seed Size in Kabuli Chickpea
Bibliographic record
Abstract
Chickpea typically has pinnate type of compound leaves \nin which the leaf lamina (blade) is differentiated into a \nrachis and a number of leaflets. These leaflets are generally \nodd in number and borne directly on the rachis. Mutants \nhave been identified that have simple (unifoliate) leaves \nin which the lamina is not differentiated into rachis and \nleaflets, though there may be deep incisions in the lamina. \nA single recessive gene is known to control the simple \nleaf trait (Pundir et al. 1990). Most chickpea cultivars \nreleased in different countries have normal pinnate leaves. \nThe simple leaf mutants have also been exploited in \nchickpea breeding and some cultivars, mainly kabuli type, \nwith simple leaves have been released, e.g. Surutato 77 \nand Macarena in Mexico; Dwelley, Sanford, Evans and \nSierra in USA; and CDC Diva and CDC Xena in Canada \n(FJ Muehlbauer, personal communication; Warkentin et \nal. 2003).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".