Table:
characteristics
univariate models
multivariate model

odds ratios

province of residence

atlantic
1.14
0.92

quebec
0.70
0.80

ontario
1.00
1.00

prairies
0.84
0.72

british columbia
1.09
0.91

age group

under 20 years
1.91
1.77

20 to 24 years
2.14
1.95

25 to 29 years
2.08
1.88

30 to 34 years
1.00
1.00

35 years and over
1.30
1.35

sex

male
1.12
1.06

female
1.00
1.00

region of birth

europe
0.92
0.69

africa
0.73
0.86

asia or oceania
0.83
0.77

united states
1.00
1.00

central or south america
0.57
0.63

de facto marital status on census day

single, widowed, separated or divorced
1.85
1.64

married or common-law
1.00
1.00

mother tongue on census day

english
1.00
1.00

french
0.66
0.77

non-official language
0.84
0.98

first npr permit

no
1.00
1.00

yes
1.19
0.60

holds more than one permit

no
1.00
1.00

yes
0.78
0.84

length of stay in canada

0 to 6 months
1.98
2.24

6 to 12 months
1.78
2.09

12 to 24 months
1.07
1.11

24 months or more
1.00
1.00

type of permit

refugee status claimant
0.54
0.53

work permit
1.00
1.00

study or minister's permit
1.01
0.77

percent

r-squared
..
7.0

number

number of observations
..
937

Given a table image and a set of target cells, your task is to identify their positions. Represent each cell's location as JSON in the format {'value': '<cell value>', 'location': (Row_ID, Column_ID)}, with both row and column IDs begin from 1.
Refer to the values below:
(1) 'number'
(2) '0.54'
(3) '0.57'
(4) '1.85'
(5) 'under 20 years'

Some cell values may not be contained in this table. If so, represent their locations as 'DOES NOT EXIST'. 

The target cell locations are as follows:
(1) {'value': 'number', 'location': (48, 2)}
(2) {'value': '0.54', 'location': (43, 2)}
(3) {'value': '0.57', 'location': (23, 2)}
(4) {'value': '1.85', 'location': (25, 2)}
(5) {'value': 'under 20 years', 'location': (10, 1)}