Based on the given feature "Repetition" and the task of predicting the grain yield of a soybean cultivar as low or high, we can assume that the repetition refers to the number of times a specific cultivar was tested or replicated in the experiment.

To analyze the relationship between the feature and task, we need prior knowledge about the typical ranges of values for "Repetition" that result in low or high grain yield. Since the specific ranges are not provided, I will provide a general analysis based on common assumptions.

Assuming that higher repetitions indicate a more robust and reliable evaluation of a cultivar, we can hypothesize that higher values of "Repetition" will be associated with higher grain yield (high target class). Conversely, lower values of "Repetition" could suggest a lack of reliability or represent experimental errors, which might result in lower grain yield (low target class).

To create the requested dictionary, I will provide example values of "Repetition" that could correspond to low and high grain yields. Please note that these values are hypothetical and might differ in specific contexts.

```json
{
	"low": [1.0, 2.0, 3.0, 4.0, 5.0],
	"high": [15.0, 20.0, 25.0, 30.0, 35.0]
}
```

These values are provided as floats and represent typical repetitions that might be associated with low and high grain yields. However, it is essential to consider the actual experimental conditions and domain knowledge to determine the specific ranges for low and high grain yield in soybean cultivars.