You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 simpson_episodes
【Schema】
# Table: Credit
[
(episode_id:TEXT, Examples: [S20-E10, S20-E13, S20-E14]),
(category:TEXT, Examples: [Casting Department, Animation Department, Sound Department]),
(person:TEXT, Examples: [Bonita Pietila, Adam Kuhlman, Carlton Batten]),
(role:TEXT, Examples: [casting, additional timer, adr engineer]),
(credited:TEXT, Examples: [true, false])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [325, 326, 327]),
(organization:TEXT, Examples: [Primetime Emmy Awards]),
(year:INTEGER, Examples: [2009, 2010, 2017]),
(award_category:TEXT, Examples: [Primetime Emmy, Annie, Image Award]),
(award:TEXT),
(person:TEXT, Examples: [Dan Castellaneta, Hank Azaria, Harry Shearer]),
(role:TEXT, Examples: [executive producer]),
(episode_id:TEXT, Examples: [S20-E18, S20-E16, S20-E8]),
(season:TEXT),
(song:TEXT),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Person
[
(name:TEXT, Primary Key, Examples: [Adam Greeley, Adam Klein, Adam Kuhlman]),
(birthdate:TEXT, Examples: [1963-05-04, 1975-03-30, 1969-12-04]),
(birth_name:TEXT, Examples: [Marc Edward Wilmore, Valentina Lantigua, Joseph Stewart Burns]),
(birth_place:TEXT, Examples: [USA, Brooklyn, Chicago]),
(birth_region:TEXT, Examples: [California, New York, Illinois]),
(birth_country:TEXT, Examples: [USA, Canada, Ireland]),
(height_meters:REAL, Examples: [1.85, 1.75, 2.03]),
(nickname:TEXT, Examples: [Jim, Doofus, Hoops])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [S20-E1, S20-E10, S20-E11]),
(stars:INTEGER, Examples: [2, 3, 4]),
(votes:INTEGER, Examples: [16, 20, 36]),
(percent:REAL, Examples: [1.3, 1.7, 3.0])
]
# Table: Character_Award
[
(award_id:INTEGER, Examples: [325, 326, 327]),
(character:TEXT, Examples: [Homer Simpson, Moe Szyslak, Kent Brockman])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(season:INTEGER, Examples: [20]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [421, 422, 423]),
(title:TEXT, Examples: [Sex, Pies and Idiot Scrapes]),
(summary:TEXT),
(air_date:TEXT, Examples: [2008-09-28, 2008-10-05, 2008-10-19]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.2, 7.0, 7.1]),
(votes:INTEGER, Examples: [1192, 1055, 1015])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(keyword:TEXT, Primary Key, Examples: [1930s to 2020s, erotic bakery, cake])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person=Person.name
Award.person=Person.name
Award.episode_id=Episode.episode_id
Vote.episode_id=Episode.episode_id
Character_Award.award_id=Award.award_id
Keyword.episode_id=Episode.episode_id

    【Question】
    How many episodes have won the award for Outstanding Animated Program (Programming Under One Hour) with less than 100 votes? Calculate the percentage of episodes with less than 100 votes out of total episodes. less than 100 votes refers to votes < 100; percentage refers to DIVIDE(COUNT(episode_id when votes < 100), COUNT(episode_id)) * 100%

    【Evidence】
    less than 100 votes refers to votes < 100; percentage refers to DIVIDE(COUNT(episode_id when votes < 100), COUNT(episode_id)) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(CASE WHEN T2.votes < 100 THEN 1 ELSE 0 END) AS num , CAST(SUM(CASE WHEN T2.votes < 100 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Award AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Episode AS T3 ON T1.episode_id = T3.episode_id WHERE T1.award = 'Outstanding Animated Program (For Programming Less Than One Hour)';