Morphological plasticity of chickpea in a semiarid environment
Bibliographic record
Abstract
Chickpea ( Cicer arietinum L.) is being rapidly adapted to the semiarid northern Great Plains, but little is known about the morphological responses of this annual grain legume to the dry environment. This study, conducted in southwestern Saskatchewan, examined the morphological plasticity of three market classes of chickpea by growing the crop at four plant population densities. Chickpea grown at high (50 plants m −2 ) population density produced approximately half as many fertile pods per plant as those grown at low (20 plants m −2 ) density, but total number of pods per unit area increased with increasing plant population density. Large‐seeded kabuli chickpea produced fewer pods per unit area, or <60% of that produced by small‐seeded kabuli, and <50% of that by desi chickpea. Infertile pods accounted for 17 to 23% of the total pods for large‐seeded kabuli, compared with 9 to 12% for small‐seeded kabuli, and 6% for desi chickpea. The large‐seeded kabuli produced <87 seeds for every 100 pods produced, whereas desi and small‐seeded kabuli produced >110 seeds for every 100 pods. Consequently, the large‐seeded kabuli chickpea produced <90% of seed yield per unit area than small‐seeded kabuli and desi chickpea. As plant population increased from 20 to 50 plants m −2 , the seed yield m −2 increased by 20% for desi and 27% for small‐seeded kabuli, but only 17% for the large‐seeded kabuli chickpea. In the semiarid northern Great Plains, seed yield potential of desi and small‐seeded kabuli chickpea can be increased by increasing plant population density, whereas the seed yield of large‐seeded kabuli can be improved by increasing percentage pod fertility.
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 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 | 0.001 |
| 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 teacher head, 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".