Which method is described as adaptable but rarely used for adjusting traverses?

The least squares method is often described as adaptable due to its ability to process and adjust data sets with varying characteristics and complexities. It operates by minimizing the sum of the squares of the residuals, which are the differences between observed values and the values predicted by a model. This method can effectively handle multiple observations and can adjust for errors in measurement, making it versatile for various surveying applications.

In the context of adjusting traverses, while the least squares method provides high accuracy by incorporating all data points to arrive at a statistically sound estimate, it is less commonly used in practice compared to more traditional methods. This is often due to the complexity of implementation and the need for specialized software or knowledge to apply it correctly.

In contrast, other methods like the compass rule, Crandall, and transit rule have been traditionally preferred because they are simpler to apply and understand in many cases. These methods do not leverage the full statistical capabilities of least squares, which is why they are frequently employed for adjustments, especially in straightforward applications or for those less familiar with sophisticated data analysis techniques.