In this task, you're given a question, along with three passages, 1, 2, and 3. Your job is to determine which passage can be used to answer the question by searching for further information using terms from the passage. Indicate your choice as 1, 2, or 3.

Question: How old is the person who succeeded Hastings? Passage 1:Kanazawa was born in Iruma on July 9, 1976. After graduating from Kokushikan University, he joined J1 League club Júbilo Iwata in 1999. Although he could not become a regular player, he played many matches as left side midfielder from first season. The club won the champions 1999 and 2002 J1 League. In Asia, the club won the champions 1998–99 Asian Club Championship and the 2nd place 1999–00 and 2000–01 Asian Club Championship. In 2003, he moved to FC Tokyo. He became a regular player as left side back from first season. The club won the champions 2004 J.League Cup. Although he could hardly play in the match for injury in 2006, he came back and became a regular player again in 2007. From 2008, he lost regular position behind newcomer Yuto Nagatomo and he also played as defensive midfielder not only left side back. In August 2009, he moved to Júbilo Iwata for the first time in 7 years. He played as regular left side back in 2009 season. Although he could not play many matches from 2010, the club won the champions 2010 J.League Cup. His opportunity to play decreased from 2011 and he moved to J2 League club Thespakusatsu Gunma in 2014. He retired end of 2014 season at the age of 38.
 Passage 2:Born in Scarborough, Ontario, Canada in 1957, he attended Lord Roberts Public School, graduated from Midland Avenue Collegiate Institute, holds a BA from the University of Trinity College, University of Toronto, law degrees from Osgoode Hall Law School and the London School of Economics, and was a practising barrister. He moved to New Zealand in 1985. Before becoming Chief Censor, he was Deputy and Acting Chief Censor from December 1998 to October 1999, Senior Lecturer in Law (teaching Legal System and International Law), Deputy Dean of Law, and a member of the governing Council, at Victoria University of Wellington. He was also briefly the Video Recordings Authority in 1994, a member of the Indecent Publications Tribunal from 1990 to 1994 and Deputy President of the Film and Literature Board of Review from 1995 to 1998. In 2010 he stood down as Chief Censor when he became a District Court Judge and Chair of the Immigration and Protection Tribunal. He was succeeded by Andrew Jack.
 Passage 3:Most Deep Learning systems rely on training and verification data that is generated and/or annotated by humans. It has been argued in media philosophy that not only low-payed clickwork (e.g. on Amazon Mechanical Turk) is regularly deployed for this purpose, but also implicit forms of human microwork that are often not recognized as such. The philosopher Rainer Mühlhoff distinguishes five types of "machinic capture" of human microwork to generate training data: (1) gamification (the embedding of annotation or computation tasks in the flow of a game), (2) "trapping and tracking" (e.g. CAPTCHAs for image recognition or click-tracking on Google search results pages), (3) exploitation of social motivations (e.g. tagging faces on Facebook to obtain labeled facial images), (4) information mining (e.g. by leveraging quantified-self devices such as activity trackers) and (5) clickwork. Mühlhoff argues that in most commercial end-user applications of Deep Learning such as Facebook's face recognition system, the need for training data does not stop once an ANN is trained. Rather, there is a continued demand for human-generated verification data to constantly calibrate and update the ANN. For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification. They can choose whether of not they like to be publicly labeled on the image, or tell Facebook that it is not them in the picture. This user interface is a mechanism to generate "a constant stream of  verification data" to further train the network in real-time. As Mühlhoff argues, involvement of human users to generate training and verification data is so typical for most commercial end-user applications of Deep Learning that such systems may be referred to as "human-aided artificial intelligence".
The correct answer is 2, as it states how old the person who succeeded Hastings is.
James spends 10 minutes downloading a game, half as long installing it, and triple that combined amount of time going through the tutorial. How long does it take before he can play the main game?
First divide the download time in half to find the install time: 10 minutes / 2 = <<10/2=5>>5 minutes
Then add that amount of time to the download time: 5 minutes + 10 minutes = <<5+10=15>>15 minutes
Then triple that amount of time to find the tutorial time: 15 minutes * 3 = <<15*3=45>>45 minutes
Then add that time to the install and downtime time to find the total time: 45 minutes + 15 minutes = <<45+15=60>>60 minutes
#### 60
You are an expert at transforming general questions into domain-specific, math-related questions. Your task is to generate only the transformed math question without including any answers or solutions.

### Step-by-Step Instructions
#### Step 1: Analyze and Understand the General Question
   - Objective: Fully understand the general question to grasp its context, key concepts, and quantitative elements.
   - Guidelines:
     - Identify the core topic (e.g., measurement, comparison, probability) and **specific details** that can inspire mathematical transformations.
     - Consider how the question’s **structure and format** might be adapted to a mathematical context. For example, if the question compares quantities or discusses measurements, consider framing these as math problems involving calculations, rates, or proportions.

#### Step 2: Refer to the Domain-Specific Question (for Inspiration Only)
   - Objective: Use the domain-specific question to understand how similar questions are formulated in a mathematical way.
   - Guidelines:
     - Extract Core Concepts: Identify the main mathematical ideas in the domain-specific question and consider how these concepts can apply to the general question.
     - Focus on Transformation, Not Duplication: Use the structure of the domain-specific question as a guide but create a unique question aligned with the general question’s theme.
     - Frame questions to encourage mathematical reasoning, such as using multi-step calculations, logical problem-solving, or real-world applications.

#### Step 3: Generate the Transformed Math Question
   - Objective: Create an original math question that retains the context and theme of the general question pair.
   - Guidelines:
     - Preserve Original Context: Integrate elements from the general question (e.g., scenario, characters, or objects) into the math question to make it cohesive and relevant.
     - Introduce a Mathematical Challenge: Frame the question to require mathematical thinking, such as:
       - Calculations (e.g., “Calculate the time needed if…”)
       - Comparative reasoning (e.g., “Determine which option has a higher value if…”)
       - Probability or logic-based reasoning (e.g., “What are the chances of selecting a specific item if…”)
     - Avoid Including Solutions: Ensure the output contains only the transformed math question without any answers or calculations.

### Response Rule
- Generate **only the "Transformed Domain Question"** for **Example 4** without including any answer, explanation, or solution.

### Output Format
- **Transformed Domain Question**: [Write your transformed question here.]

### Example 1:
- **General Question**: Given the sentence "A man with a beard playing an instrument," is it true that "The person has a beard"?
- **Domain Question**: What is the total cost of purchasing equipment for all sixteen players on the football team, considering that each player requires a $25 jersey, a $15.20 pair of shorts, and a pair of socks priced at $6.80?
- **Transformed Domain Question**: In a concert audience of 1,000 people, where 45% of attendees are male and 30% of the males have beards, what is the probability of randomly selecting a bearded man from the crowd?

### Example 2:
- **General Question**: Acids have pH values below 7. The lower the pH value, the more strongly acidic the substance. Having read the above passage, choose the right answer to the following question (choices are higher or lower): You can find tables that show the pH value of different fruits and vegetables. The fruits such as apples are barely acidic so they have a pH value that is higher or lower?
- **Domain Question**: If Williams has a certain amount of money, Jackson has 5 times that amount. If they have a total of $150 together, how much money does Jackson have in dollars?
- **Transformed Domain Question**: In a study of soil pH levels across various agricultural zones, it's noted that some zones are more acidic due to the types of crops grown there. If Zone A has an average soil pH of 5.7 and is noted for its apple orchards, which are known for requiring slightly acidic soil, calculate the comparative pH difference percentage between Zone A and Zone B, where Zone B has a soil pH of 6.8, suitable for less acidic crop requirements.

### Example 3:
- **General Question**: Detailed Instructions: In this task, you are given a sentence in the Hindi language and your task is to convert it into the English language. In translation, keep numbers as it is and make it sentence case (capitalize only the first word of each sentence and noun).
- **Domain Question**: Mary and Ann are going sledding. Mary slides down a hill that's 630 feet long at a speed of 90 feet/minute. Ann slides down a hill that's 800 feet long at a rate of x feet/minute. How much longer does Ann's trip take than Mary? If we know the answer to the above question is 13, what is the value of unknown variable x?
- **Transformed Domain Question**: Given the geopolitical tension in the region described, if the fall of the Ottoman and Russian empires led to the redistribution of their controlled territories among other nations, assume these territories were divided into 10 new countries with varying sizes. The largest new country occupies 30% of the total territory once held by these empires. If the total land previously controlled by the Ottoman and Russian empires was 2,000,000 square kilometers, calculate the area of the largest new country in square kilometers.

### Example 4:
- **General Question**: In this task, you're given a question, along with three passages, 1, 2, and 3. Your job is to determine which passage can be used to answer the question by searching for further information using terms from the passage. Indicate your choice as 1, 2, or 3.

Question: How old is the person who succeeded Hastings? Passage 1:Kanazawa was born in Iruma on July 9, 1976. After graduating from Kokushikan University, he joined J1 League club Júbilo Iwata in 1999. Although he could not become a regular player, he played many matches as left side midfielder from first season. The club won the champions 1999 and 2002 J1 League. In Asia, the club won the champions 1998–99 Asian Club Championship and the 2nd place 1999–00 and 2000–01 Asian Club Championship. In 2003, he moved to FC Tokyo. He became a regular player as left side back from first season. The club won the champions 2004 J.League Cup. Although he could hardly play in the match for injury in 2006, he came back and became a regular player again in 2007. From 2008, he lost regular position behind newcomer Yuto Nagatomo and he also played as defensive midfielder not only left side back. In August 2009, he moved to Júbilo Iwata for the first time in 7 years. He played as regular left side back in 2009 season. Although he could not play many matches from 2010, the club won the champions 2010 J.League Cup. His opportunity to play decreased from 2011 and he moved to J2 League club Thespakusatsu Gunma in 2014. He retired end of 2014 season at the age of 38.
 Passage 2:Born in Scarborough, Ontario, Canada in 1957, he attended Lord Roberts Public School, graduated from Midland Avenue Collegiate Institute, holds a BA from the University of Trinity College, University of Toronto, law degrees from Osgoode Hall Law School and the London School of Economics, and was a practising barrister. He moved to New Zealand in 1985. Before becoming Chief Censor, he was Deputy and Acting Chief Censor from December 1998 to October 1999, Senior Lecturer in Law (teaching Legal System and International Law), Deputy Dean of Law, and a member of the governing Council, at Victoria University of Wellington. He was also briefly the Video Recordings Authority in 1994, a member of the Indecent Publications Tribunal from 1990 to 1994 and Deputy President of the Film and Literature Board of Review from 1995 to 1998. In 2010 he stood down as Chief Censor when he became a District Court Judge and Chair of the Immigration and Protection Tribunal. He was succeeded by Andrew Jack.
 Passage 3:Most Deep Learning systems rely on training and verification data that is generated and/or annotated by humans. It has been argued in media philosophy that not only low-payed clickwork (e.g. on Amazon Mechanical Turk) is regularly deployed for this purpose, but also implicit forms of human microwork that are often not recognized as such. The philosopher Rainer Mühlhoff distinguishes five types of "machinic capture" of human microwork to generate training data: (1) gamification (the embedding of annotation or computation tasks in the flow of a game), (2) "trapping and tracking" (e.g. CAPTCHAs for image recognition or click-tracking on Google search results pages), (3) exploitation of social motivations (e.g. tagging faces on Facebook to obtain labeled facial images), (4) information mining (e.g. by leveraging quantified-self devices such as activity trackers) and (5) clickwork. Mühlhoff argues that in most commercial end-user applications of Deep Learning such as Facebook's face recognition system, the need for training data does not stop once an ANN is trained. Rather, there is a continued demand for human-generated verification data to constantly calibrate and update the ANN. For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification. They can choose whether of not they like to be publicly labeled on the image, or tell Facebook that it is not them in the picture. This user interface is a mechanism to generate "a constant stream of  verification data" to further train the network in real-time. As Mühlhoff argues, involvement of human users to generate training and verification data is so typical for most commercial end-user applications of Deep Learning that such systems may be referred to as "human-aided artificial intelligence".
- **Domain Question**: James spends 10 minutes downloading a game, half as long installing it, and triple that combined amount of time going through the tutorial. How long does it take before he can play the main game?
- **Transformed Domain Question**: 