Morphological and Agronomic Characteristics of Common Beans Subjected to Seed Priming With Different Doses of N6-benzyladenine
Bibliographic record
Abstract
In field conditions, the more usual application of cytokinins is based mainly on seed treatment and foliar spraying. Nowadays, seed priming with plant growth regulators is a successful agricultural practice due to its easy application in annual crops, resulting in a higher vigor and production. In order to evaluate the effect of the treatment of common bean seeds with N6-benzyladenine (6-BA), in the morphophysiology and crop yield, an experiment was carried out in a greenhouse, in a completely randomized design with four replicates and five doses of 6-BA (0, 0.0375, 0.075, 0.1125 and 0.15 g kg-1 of seed). The growth regulator was applied to the seeds in order to cover their entire surface. At 30 days after emergence, the physiological parameters were increased as a function of the doses of 6-BA. On the other hand, at 30 and 45 days after emergence of the crop, there was a drastic reduction in nodulation with increased 6-BA doses, which resulted in the lower accumulation of leaf nitrogen, pod number, and common bean yield. The ranging doses till 0.15 g kg-1 of seed of the cytokinin 6-BA are not recommended in seed priming of common beans. However, the effective recommendation of cytokinin as a seed priming is still not determined, and studies about products and doses must be improved before recommendation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".