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

    【Schema】
    【DB_ID】 law_episode
【Schema】
# Table: Credit
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(person_id:TEXT, Primary Key, Examples: [nm0000973, nm0001583, nm0001832]),
(category:TEXT, Examples: [Additional Crew]),
(role:TEXT, Examples: [technical advisor]),
(credited:TEXT, Examples: [true, false])
]
# Table: Person
[
(person_id:TEXT, Primary Key, Examples: [nm0000210, nm0000973, nm0001346]),
(name:TEXT, Examples: [Julia Roberts, Rene Balcer, Billy Fox]),
(birthdate:DATE, Examples: [1967-10-28]),
(birth_name:TEXT, Examples: [Julia Fiona Roberts]),
(birth_place:TEXT, Examples: [Smyrna, Montreal, Harrisburg]),
(birth_region:TEXT, Examples: [Georgia, Quebec, Pennsylvania]),
(birth_country:TEXT, Examples: [USA, Canada, England]),
(height_meters:REAL, Examples: [1.73, 1.88, 1.93]),
(nickname:TEXT, Examples: [Jules, Gus, Ed])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [258, 259, 260]),
(organization:TEXT),
(year:INTEGER, Examples: [1999, 2000]),
(award_category:TEXT, Examples: [Monitor, Primetime Emmy, Silver Gavel Award]),
(award:TEXT),
(series:TEXT, Examples: [Law and Order]),
(episode_id:TEXT, Examples: [tt0629149, tt0629398, tt0629248]),
(person_id:TEXT, Examples: [nm0937725, nm0792309, nm0049569]),
(role:TEXT, Examples: [Katrina Ludlow, production mixer, re-recording mixer]),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [tt0629204, tt0629228, tt0629170]),
(stars:INTEGER, Examples: [10, 9, 8]),
(votes:INTEGER, Examples: [36, 39, 64]),
(percent:REAL, Examples: [17.7, 19.2, 31.5])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(series:TEXT, Examples: [Law and Order]),
(season:INTEGER, Examples: [9]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [182, 183, 184]),
(title:TEXT, Examples: [Cherished, DWB, Bait]),
(summary:TEXT),
(air_date:DATE, Examples: [1998-09-23]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.9, 7.6, 8.2]),
(votes:INTEGER, Examples: [203, 177, 159])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [tt0629239, tt0629397, tt0629420]),
(keyword:TEXT, Primary Key, Examples: [nun, priest, police officer])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person_id=Person.person_id
Award.episode_id=Episode.episode_id
Award.person_id=Person.person_id
Vote.episode_id=Episode.episode_id
Keyword.episode_id=Episode.episode_id

    【Question】
    What percentage of people have worked on the True North episode as additional crew? the True North episode refers to title = 'True North'; additional crew refers to role = 'Additional Crew'; percentage = divide(count(episode_id where role = 'Additional Crew'), count(episode_id)) * 100% where title = 'True North'

    【Evidence】
    the True North episode refers to title = 'True North'; additional crew refers to role = 'Additional Crew'; percentage = divide(count(episode_id where role = 'Additional Crew'), count(episode_id)) * 100% where title = 'True North'

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

SQL: SELECT CAST(SUM(CASE WHEN T2.role = 'Additional Crew' THEN 1 ELSE 0 END) AS REAL ) * 100 / COUNT(T1.episode_id) FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'True North'