A tier is a categorical label assigned to a score based on where the score falls among the trait's trained boundaries. Four tiers are defined: Strong, Solid, Developing, Weak.
A tier is the label returned alongside a trait score. The label is determined by comparing the score against three boundaries derived from the trait's training distributions. The four tiers partition the score axis as follows.
| Tier | Condition | Source boundary |
|---|---|---|
| Strong | score ≥ strong | Upper quartile of the positive training distribution |
| Solid | solid ≤ score < strong | Lower quartile of the positive training distribution |
| Developing | developing ≤ score < solid | Upper quartile of the negative training distribution |
| Weak | score < developing | Within or below the negative training distribution |
Table 1. Tier labels and the score conditions that assign them. Boundary names (strong, solid, developing) refer to the trained thresholds described on breaks.
The three boundaries are computed during training from the quartiles of the trait's positive and negative sample distributions. Because each trait has its own training distributions, each trait has its own boundaries. A score of 70 on one trait may fall in the Solid band; a score of 70 on another trait may fall in the Developing band.
At scoring time, the score is compared against the trait's boundaries from highest to lowest. The first condition the score satisfies determines the tier. No tier is computed for the composite — the composite is reported as a score only.
Each tier expresses where the score sits relative to the training distributions that defined the trait.
| Tier | What it says about the score |
|---|---|
| Strong | In the upper quartile of the positive training distribution. At or above the strongest examples the trait was trained on. |
| Solid | Within the positive training distribution's inter-quartile range. Comparable to the bulk of training examples that exemplified the trait. |
| Developing | Between the positive and negative distributions. Above the bulk of examples that did not exemplify the trait, below the bulk that did. |
| Weak | At or below the upper quartile of the negative training distribution. Within the range of examples that did not exemplify the trait. |
When the positive and negative training distributions overlap enough that the boundaries collapse or invert, no tier label is assigned. The score is returned with a null tier. The same condition produces low confidence, so the two signals appear together.
Scores in the Developing band fall between the two training distributions, where the system has less direct training signal than in either distribution's bulk. Developing scores are therefore always reported with moderate confidence. All other tiers pair with high confidence for well-separated training distributions.