Recognises a scorecard score with no evidence
A category scored with nothing to show why.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 1 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 89% | Not shown |
| gemini-3.5-flash-lite | Low cost | 90% | Not shown |
| gpt-6-luna | Low cost | 90% | Mastered |
| claude-haiku-5-5 | Low cost | 10% | Not 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 evidence the score needs, or a lower score
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: "How good are the definitions?" 10/10, evidence: null Acceptable: "28 of 31 definitions pass the 11179-4 checklist; LA-007 duplicates LA-006"
Also known as: Score with no evidence
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 2 (Scorecard; DMBOK §2.3)