Recognises a non-discriminating evaluation criterion
Every candidate scores the same: weight spent on nothing.
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 73% | Not shown |
| gemini-3.5-flash-lite | Low cost | 47% | Not shown |
| gpt-6-luna | Low cost | 73% | Not shown |
| claude-haiku-5-5 | Low cost | 73% | 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 defect named on the criterion every candidate scores alike, and its fix or the question for its owner
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: EC-005 SQL support: 5, 5, 5 Acceptable: Removed from the weighted set (or a gate)
Also known as: Criterion that cannot discriminate
Related knobits
Builds on
All 14 knobits of Evaluate data storage technology.
Source: Review checklist 01-technology-evaluation, row 2 (MCDA)