
    Design a content moderation scoring function that evaluates posts across multiple severity thresholds. Starting from the maximum severity level n, the system needs to calculate a weighted score by summing the decreasing severity levels (n, n-2, n-4, etc.) until reaching zero or a negative threshold. Each decremental step represents a more lenient moderation threshold, with the sum determining the final moderation score for the content. The function should process these graduated thresholds by calculating the sum of (n - 2*i) for each step i from 0 to n/2, where n is the highest severity level. This weighted approach ensures that content triggering higher severity levels contributes more significantly to the final moderation score, while still accounting for lower-severity indicators.
    

    
def sum_series(n):
    """
    Design a content moderation scoring function that evaluates posts across multiple severity thresholds. Starting from the maximum severity level n, the system needs to calculate a weighted score by summing the decreasing severity levels (n, n-2, n-4, etc.) until reaching zero or a negative threshold. Each decremental step represents a more lenient moderation threshold, with the sum determining the final moderation score for the content. The function should process these graduated thresholds by calculating the sum of (n - 2*i) for each step i from 0 to n/2, where n is the highest severity level. This weighted approach ensures that content triggering higher severity levels contributes more significantly to the final moderation score, while still accounting for lower-severity indicators.
    

    """
def sum_series(n):
    """
    Design a content moderation scoring function that evaluates posts across multiple severity thresholds. Starting from the maximum severity level n, the system needs to calculate a weighted score by summing the decreasing severity levels (n, n-2, n-4, etc.) until reaching zero or a negative threshold. Each decremental step represents a more lenient moderation threshold, with the sum determining the final moderation score for the content. The function should process these graduated thresholds by calculating the sum of (n - 2*i) for each step i from 0 to n/2, where n is the highest severity level. This weighted approach ensures that content triggering higher severity levels contributes more significantly to the final moderation score, while still accounting for lower-severity indicators.
    

    """
    if n < 1:
        return 0
    else:
        return n + sum_series(n - 2)