Card: replace the trust section with Limitations

README.md

@@ -100,14 +100,14 @@ Accuracy on human-labelled data, by the kind of question:
 | General questions about a photo | MMBench-EN / POPE / MMStar | 0.902 / 0.897 / 0.674 |
 | Bugs in game screenshots | glitch / object clipping (VideoGameQA-Bench) | 0.626 / 0.729 |
 
-The text rows are public benchmarks; the image rows are judgment sets converted from labelled public data. Check it
-on a labelled sample of your own questions before relying on it. Significance tests and the comparison with the
-base model are in the [GitHub README](https://github.com/Xiaooolong/vev#detailed-results).
+The text rows are public benchmarks; the image rows are judgment sets converted from labelled public data. Jev is
+more accurate on nimble (by 18 points) and kev transfer-v4 (7); JevBench and judgekit are not significantly
+different. Jev's API takes text only, so the image rows have no Jev comparison. Check Vev on a labelled sample of
+your own questions before relying on it. Significance tests and the comparison with the base model are in the
+[GitHub README](https://github.com/Xiaooolong/vev#detailed-results).
 
-## How far to trust it
+## Limitations
 
-- Jev is more accurate on nimble (by 18 points) and kev transfer-v4 (7); JevBench and judgekit are not
-  significantly different.
 - The probabilities rank answers well but are not exact frequencies: expected calibration error is 0.01–0.14
   depending on the task. Choose any threshold on your own labelled data.
 - Reversing the order of the options changes the top answer on 13% of JevBench and kev transfer-v4 questions (Jev: