I want you to act as an evaluator. I will give you a question, the solution, and the prediction, and you will give a score between 0 and 100 to the prediction. You will evaluate whether the prediction is relevant to the question by using the solution as the reference. You should take into account whether the prediction goes off topic, or contains unrelated, not mentioned or false information. Factuality and mention of unrelated information should be your priority, those answers should have a low score. If the prediction does not answer the question but is still trying to be helpful or polite, give it a score of 25. Your output will be a Python dictionary with the score and explanation as keys and the score you give and a brief explanation about why you have given that score as values.
Question:
Who are the leaders of each work package in the LESSEN project?
Solution:
Work package leadership o WP1.1: NAME_1 (UL, male, junior) o WP1.2: NAME_2 (RUG, female, junior) o WP2.1: NAME_3 (UL, female, medior) o WP2.2: NAME_4 (RUN, female, junior) o WP3.1: NAME_5 (RUG, male, junior) o WP3.2: NAME_6 (HvA, male, medior) o WP4: NAME_7 (UvA, male, senior) Dutch Research Agenda Research along routes by Consortia (NWA-ORC) 2020/21 Full proposal form IMPACT PLAN APPROACH Page 11 o WP5: NAME_6 (HvA, male, medior) o WP6: NAME_8 (UvA, male, senior) o WP7: NAME_8 (UvA, male, senior) and NAME_3 (UL, female, medior)
Prediction:

 The leaders of each work package in the LESSEN project are as follows:

- WP1.1: NAME_1 (UL, male, junior)
- WP1.2: NAME_2 (RUG, female, junior)
- WP2.1: NAME_3 (UL, female, medior)
- WP2.2: NAME_4 (RUN, female, junior)
- WP3.1: NAME_5 (RUG, male, junior)
- WP3.2: NAME_6 (HvA, male, medior)
- WP4: NAME_7 (UvA, male, senior)
- WP5: NAME_6 (HvA, male, medior)
- WP6: NAME_8 (UvA, male, senior)
- WP7: NAME_8 (UvA, male, senior) and NAME_3 (UL, female, medior) Provide your response in exactly 5 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
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