Explains a weighted score as the sum of weights times scores
Each criterion's score is multiplied by its weight, the weights being shares of the whole that sum to 100%, and the products are added; a weight is the share of the total the criterion can decide.
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | 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: A total score computed as the sum of weight times score, with weights summing to 100%
How it is assessed: Computes a weighted total from given weights and scores and tells what share of the total each criterion decides
Related knobits
Builds on
Needed for
All knobits of Data storage and operations.
Source: Part B candidate of k12-dmbok for activity OV-10 (a need for school knowledge no knobit of the activity reached), accepted by Peep Küngas on 31 Oct 2026