Recognises a scorecard score contradicting the validator
Full marks where the validator reports errors in that category's subject.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 90% | Not shown |
| gemini-3.5-flash-lite | Low cost | 10% | Not mastered |
| gpt-6-luna | Low cost | 90% | Not shown |
| claude-haiku-5-5 | Low cost | 90% | Not shown |
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 score lowered and the errors cited as evidence
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: "How structurally sound?" 15/15 while validation/ reports duplicate attribute LA-006/LA-007 and a missing key Acceptable: 9/15, evidence citing the errors
Also known as: Score contradicts the validator
Related knobits
Builds on
- K-915 Explains data model review and the Data Model Scorecard
- K-920 Explains database design principles
Usually followed by
All 8 knobits of Review the data model against the scorecard.
Source: Review checklist 06-model-review, row 6 (DMBOK §2.3)