Recognises an incomplete data model scorecard
A category missing, or weights not totalling 100.
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 90% | Mastered |
| gemini-3.5-flash-lite | Low cost | 10% | Not shown |
| gpt-6-luna | Low cost | 90% | Mastered |
| claude-haiku-5-5 | Low cost | 90% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The missing category and the weights restored to 100
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: Nine categories; "How well does the metadata match the data?" missing; weights total 90 Acceptable: All ten categories, weights 100
Also known as: Scorecard incomplete
Related knobits
Builds on
Usually followed by
All 8 knobits of Review the data model against the scorecard.
Source: Review checklist 06-model-review, row 1 (Scorecard)