Node and branch development of chickpea in a semiarid environment
Bibliographic record
Abstract
Node development of a crop plant can be used as a reference in making crop management decisions such as timing foliar fungicide application. A study was conducted in southwest Saskatchewan in 2001 and 2002 to characterize the nodal development, branching, and thermal time requirements on the main stem (MS) and on the branches of chickpea (Cicer arietinum L.). Large (9.1–11.0 mm) and small (8.1–9.0 mm) diameter seeds were planted at early-, normal-, and late-seeding dates in each year, and the crop was grown with recommended agronomic practices. The MS nodes were numbered upwards from the plant base, with the 1st node being the one immediately above the hypocotyls. All branches were identified and named in correspondence with their positions on the MS. The total number of nodes produced on the MS was 20 in the dry year of 2001 and 24 in the wet year of 2002, and their appearance was a function of growing-degree-days (GDD). The rate of the branch development in 2001 was greater compared with 2002 due to higher temperatures during the vegetative growth period. On average, branches No. 1 and 2 required 40 to 50 GDD units to emerge from the MS, while the emergence of branches No. 5 and 6 required 80 to 120 GDD units. Nodes on the branches that were positioned on the upper stems required more GDD units to develop compared with nodes on the branches that were positioned at the bottom of the MS. To use node development as a reference for crop management decisions in chickpea, one should bear in mind that the GDD requirements vary with node and branch positions on the culm. Key words: Cicer arietinum, legume, plant architecture, foliar fungicide, morphology
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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 | 0.000 | 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".