Case context: You are building a model that labels pictures of fruit. A noticeable share of the mistakes come from photos that were edited with a strong warm-color filter in a mobile app. You do not yet know the best correction method, but you think it may be possible to estimate the original colors.

Question: Should you add a "photo filter effects" category to your mistake log? Why or why not, using the ideas behind organizing errors?

Sample answer: Yes. A separate "photo filter effects" category makes sense because you have at least one concrete direction for improvement: recovering or approximating the unedited image. You also should not wait until the fix is fully proven before recording these cases, because keeping track of them helps you judge whether this pattern is important enough to prioritize.

Key points:

• Yes, the category should be added
• It is useful because there is a plausible improvement idea (restoring the original image)
• Recording it helps you decide whether it is worth more attention later

Rubric: A correct response will say the category should be added and explain that the learner has an idea for improvement, and/or that logging the cases helps assess whether this is a promising area to focus on later.