Based on the given feature description "SEQN, Respondent Sequence Number", it seems that the feature represents a numeric identifier for each respondent. 

Since the feature description does not provide explicit information about age, it is difficult to determine the appropriate ranges of values for each age group. However, we can make some assumptions based on common age ranges.

Typically, Senior citizens are considered to be individuals aged 65 and above, while non-senior adults are generally considered to be individuals aged below 65. 

For the purpose of this analysis, we can assume the following:

- SEQN values between 1000 and 2000 represent non-senior adults
- SEQN values between 8000 and 9000 represent seniors

Based on these assumptions, we can create a dictionary with typical SEQN values for each target class:

```json
{
	"Adult": [1001.0, 1500.0, 1702.0, 1900.0, 1985.0],
	"Senior": [8130.0, 8502.0, 8701.0, 8900.0, 8985.0]
}
```

Please note that these values are just hypothetical examples based on the assumptions made. In a real-world scenario, the appropriate ranges and typical values would need to be determined based on the specific dataset and domain knowledge.