In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Q:  To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.  
 Question: What was their system's performance?

A: accuracy and F1-score of 89.6% and 89.2%, respectively
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Q: We used two strategies in combining prediction results of two types of models. Specifically, the Max Score Ensemble model made the final decisions based on the maximum of two scores assigned by the two separate models; instead, the Average Score Ensemble model used the average score to make final decisions. 
 Question: How do they combine the models?

A: maximum of two scores assigned by the two separate models average score
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Q: The quality class of a Wikipedia article is assigned by Wikipedia reviewers or any registered user, who can discuss through the article's talk page to reach consensus. The arXiv dataset BIBREF2 consists of three subsets of academic articles under the arXiv repository of Computer Science (cs), from the three subject areas of: Artificial Intelligence (cs.ai), Computation and Language (cs.cl), and Machine Learning (cs.lg). In line with the original dataset formulation BIBREF2 , a paper is considered to have been accepted (i.e. is positively labeled) if it matches a paper in the DBLP database or is otherwise accepted by any of the following conferences: ACL, EMNLP, NAACL, EACL, TACL, NIPS, ICML, ICLR, or AAAI. Failing this, it is considered to be rejected (noting that some of the papers may not have been submitted to one of these conferences). 
 Question: Where do they get their ground truth quality judgments?

A:
Wikipedia articles are labelled with one of six quality classes, in descending order of quality: Featured Article (“FA”), Good Article (“GA”), B-class Article (“B”), C-class Article (“C”), Start Article (“Start”), and Stub Article (“Stub”). The quality class of a Wikipedia article is assigned by Wikipedia reviewers or any registered user, who can discuss through the article's talk page to reach consensus. The arXiv dataset BIBREF2 consists of three subsets of academic articles under the arXiv repository of Computer Science (cs), from the three subject areas of: Artificial Intelligence (cs.ai), Computation and Language (cs.cl), and Machine Learning (cs.lg). In line with the original dataset formulation BIBREF2 , a paper is considered to have been accepted (i.e. is positively labeled) if it matches a paper in the DBLP database or is otherwise accepted by any of the following conferences: ACL, EMNLP, NAACL, EACL, TACL, NIPS, ICML, ICLR, or AAAI. Failing this, it is considered to be rejected (noting that some of the papers may not have been submitted to one of these conferences). 
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