CREATE FUNCTION r.scaled_rank (score numeric, published timestamp with time zone, interactions_month numeric)
    RETURNS double precision
    LANGUAGE sql
    IMMUTABLE PARALLEL SAFE
    -- Add 2 to avoid divide by zero errors
    -- Default for score = 1, active users = 1, and now, is (0.1728 / log(2 + 1)) = 0.3621
    -- There may need to be a scale factor multiplied to interactions_month, to make
    -- the log curve less pronounced. This can be tuned in the future.
    RETURN (
        r.hot_rank (score, published) / log(2 + interactions_month)
);

  

    
CREATE FUNCTION r.hot_rank (score numeric, published timestamp with time zone)
    RETURNS double precision
    LANGUAGE sql
    IMMUTABLE PARALLEL SAFE RETURN
    -- after a week, it will default to 0.
    CASE WHEN (
now() - published) > '0 days'
        AND (
now() - published) < '7 days' THEN
        -- Use greatest(2,score), so that the hot_rank will be positive and not ignored.
        log (
            greatest (2, score + 2)) / power (((EXTRACT(EPOCH FROM (now() - published)) / 3600) + 2), 1.8)
    ELSE
        -- if the post is from the future, set hot score to 0. otherwise you can game the post to
        -- always be on top even with only 1 vote by setting it to the future
        0.0
    END;