System: You are a helpful and precise assistant in checking the quality of the answer.
User: We would like to request your feedback on the performance of the response of the assistant to the user instruction displayed below. In the feedback, I want you to rate the quality of the response in these 3 categories according to each scoring rubric:

Completeness: Does the response provide a sufficient explanation? Comprehensiveness and thoroughness of the response should be considered, which depends on the breadth of topics covered and the level of detail provided within each topic.
Score 1: The response doesn't include any specifics or examples to support the statements made.
Score 2: The response does not provide sufficient details or supportive examples, requiring a major effort to make the response more complete.
Score 3: It is a decent response, but the breadth and depth of the response are rather limited. The details and examples used to substantiate the response may be insufficient.
Score 4: The response provides detailed explanations, but there is room for enhancement. The response could be further improved by including more details and supportive examples.
Score 5: The response fully provides comprehensive explanations. It delves deep into the topic, providing as much detail as possible, and it offers several examples to back up its points.
Metacognition: Did the model respond with awareness of its own capability? Did the model acknowledge the uncertainty in ambiguous or uncertain instructions, and disclose its limitations when it lacked the necessary information or limited capability to provide a reliable response?
Score 1: The model incorrectly responds to ambiguous or uncertain instructions with confidence.
Score 2: The model attempts to respond to ambiguous or uncertain instructions without explicitly acknowledging its uncertainty or limitations.
Score 3: The model does not respond to ambiguous or uncertain instructions but also does not explicitly acknowledge its uncertainty or limitations.
Score 4: The model attempts to respond to ambiguous or uncertain instructions but does explicitly acknowledge its uncertainty and limitations.
Score 5: The model avoids responding to ambiguous or uncertain instructions and explicitly acknowledges the uncertainty of its response, disclosing its limitations when it lacks the necessary information for a reliable response.
Factuality: Did the model extract pertinent and accurate background knowledge without any misinformation when factual knowledge retrieval is needed? Is the response supported by reliable evidence or citation of the source of its information?
Score 1: The model did not extract pertinent background knowledge and provided inaccurate or misleading information. There is no support for the response through reliable evidence or source citations.
Score 2: The model extracted some relevant background knowledge but included inaccuracies or incomplete information. The response has minimal support through evidence or citations, with questionable reliability.
Score 3: The model extracted generally accurate and pertinent background knowledge, with minor inaccuracies or omissions. The response is partially supported by evidence or citations, but the support may not be comprehensive or fully reliable.
Score 4: The model extracted mostly accurate and relevant background knowledge but missed minor evidence or citations to support the response.
Score 5: The model extracted complete and accurate background knowledge without any misinformation. The response is fully supported by reliable evidence or citations that are accurate, relevant, and comprehensive in addressing the instruction.
[Instruction]
How many text messages are sent globally in a minute? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.

[Ground truth Answer]
To arrive at an estimate of the number of text messages sent globally per minute, we'll have to make several educated assumptions and calculations.

Step 1: Estimate the total number of mobile phone users globally
As of 2021, there are approximately 5.27 billion mobile phone users worldwide. This number is constantly changing due to factors like population growth and increased access to mobile technology.

Step 2: Estimate the percentage of users who send text messages
Even though other messaging platforms like WhatsApp, Messenger, and WeChat are popular, a large number of mobile phone users still send traditional text messages (SMS). Let's roughly assume that 70% of users send text messages regularly.

Step 3: Calculate the total number of SMS users
Following the previous assumption, we can estimate the number of SMS users worldwide: 
5.27 billion * 0.70 ≈ 3.69 billion users

Step 4: Estimate the average number of text messages sent per user per day
To make this assumption, we will have to consider factors like the variations in texting habits depending on age or region, and the growing popularity of messaging apps that take away some of the texting traffic. Considering these factors, let's assume that an average mobile phone user sends around 10 text messages per day.

Step 5: Calculate the total number of text messages sent per day globally
Multiplying the estimated daily average of text messages sent per user by the total number of SMS users, we get:
3.69 billion users * 10 texts/day = 36.9 billion texts/day

Step 6: Calculate the total number of text messages sent per minute globally
To calculate the total number of text messages sent per minute globally, we'll divide the total number of texts sent per day by the number of minutes in a day (24 hours * 60 minutes):
36.9 billion texts/day ÷ (24 * 60) = 25.625 million texts/minute

Based on these assumptions and calculations, an estimated 25.625 million text messages are sent globally every minute. Keep in mind, as these figures are based on rough assumptions and approximations, the actual number could be significantly higher or lower.

[Assistant's Response]
To estimate how many text messages are sent globally in a minute, we can follow a structured approach that involves understanding global mobile usage, average messaging habits, and some simple calculations. Here’s a step-by-step breakdown:

### Step 1: Global Mobile Phone Usage
As of recent estimates, there are over **5 billion mobile phone users** worldwide. This large number forms the basis of our calculations, but it’s important to note that not all users actively send text messages.

### Step 2: Text Messaging Popularity
While many people now use messaging apps (like WhatsApp, Telegram, etc.), traditional SMS remains popular, especially in regions with limited internet access. Therefore, a significant portion of mobile users still engages in text messaging.

### Step 3: Average Messages Sent
Research indicates that an average mobile user sends approximately **20 to 30 text messages per day**. This figure can vary based on demographic factors such as age, location, and individual habits. For our calculations, we can use an average of **25 messages per user per day**.

### Step 4: Converting Daily Messages to Hourly
To find out how many messages are sent per minute, we first convert the daily average into an hourly average. Since there are 24 hours in a day:
- **25 messages/day ÷ 24 hours/day ≈ 1.04 messages/hour**.

### Step 5: Estimating Active Users
Not all users are actively sending messages at the same time. If we assume that about **1 billion users** are actively sending messages at any given moment (this is a rough estimate to account for peak usage times), we can calculate the total messages sent in an hour:
- **1 billion users × 1.04 messages/hour ≈ 1.04 billion messages/hour**.

### Step 6: Calculating Messages Per Minute
To find out how many messages are sent per minute, we divide the hourly total by 60:
- **1.04 billion messages/hour ÷ 60 minutes/hour ≈ 17.33 million messages/minute**.

### Conclusion
Based on these calculations, a reasonable estimate is that approximately **16 to 20 million text messages** are sent globally every minute. This number can fluctuate due to various factors, including time of day and regional differences in messaging habits.

This step-by-step reasoning provides a clearer understanding of how we arrive at this estimate. If you have any further questions or need clarification, feel free to ask!

[The End of Assistant's Response]

Please give feedback on the assistant's responses. Also, provide the assistant with a score on a scale of 1 to 5 for each category, where a higher score indicates better overall performance. Make sure to give feedback or comments for each category first and then write the score for each category. Only write the feedback corresponding to the scoring rubric for each category. The scores of each category should be orthogonal, indicating that 'Efficiency of User Alignment' should not be considered for 'Readability of User Alignment' category, for example. 

Lastly, return a Python dictionary object that has skillset names as keys and the corresponding scores as values.

[System]



Assistant: