Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1     | Col_2      |   Col_3 | Col_4   |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |   Col_19 |
|--------:|:----------|:-----------|--------:|:--------|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|    6015 | Del Norte | California |       2 | S3784   |       1 |       0 |       0 |       0 |       0 |        0 |        1 |        1 |        1 |        0 |        0 |        0 |        0 |        0 |        0 |
|    8083 | Montezuma | Colorado   |       2 | S3783   |       1 |       0 |       0 |       0 |       0 |        0 |        1 |        1 |        1 |        0 |        0 |        0 |        0 |        0 |        0 |
|    6011 | Colusa    | California |       1 | S3784   |       1 |       0 |       0 |       0 |       0 |        0 |        1 |        1 |        1 |        0 |        0 |        0 |        0 |        0 |        0 |
|    6009 | Calaveras | California |       1 | S3784   |       1 |       0 |       0 |       0 |       0 |        0 |        1 |        1 |        1 |        0 |        0 |        0 |        0 |        0 |        0 |
|    6001 | Alameda   | California |       1 | S3784   |       1 |       0 |       0 |       0 |       0 |        0 |        1 |        1 |        1 |        0 |        0 |        0 |        0 |        0 |        0 |

Candidate column headers:
Hurricanes_ Typhoons_ Tropical Storms
Excessive rain_ moisture_ humidity
County
Designation Code
Fire_ Wildfire
Hail
DROUGHT
FLOOD_ Flash flooding
Mudslides_ Debris Flows_ Landslides
Wind_ High Winds
FIPS
Volcano
Tornadoes
Heat_ Excessive heat High temp. (incl. low humidity)
State
Designation Number
Ground Saturation Standing Water
Severe Storms_ thunderstorms
Winter Storms_ Ice Storms_ Snow_ Blizzard
Frost_ FREEZE



headers: Hurricanes_ Typhoons_ Tropical Storms
Excessive rain_ moisture_ humidity
County
Designation Code
Fire_ Wildfire
Hail
DROUGHT
FLOOD_ Flash flooding
Mudslides_ Debris Flows_ Landslides
Wind_ High Winds
FIPS
Volcano
Tornadoes
Heat_ Excessive heat High temp. (incl. low humidity)
State
Designation Number
Ground Saturation Standing Water
Severe Storms_ thunderstorms
Winter Storms_ Ice Storms_ Snow_ Blizzard
Frost_ FREEZE
label: FIPS
County
State
Designation Code
Designation Number
DROUGHT
FLOOD_ Flash flooding
Excessive rain_ moisture_ humidity
Severe Storms_ thunderstorms
Ground Saturation Standing Water
Hail
Wind_ High Winds
Fire_ Wildfire
Heat_ Excessive heat High temp. (incl. low humidity)
Winter Storms_ Ice Storms_ Snow_ Blizzard
Frost_ FREEZE
Hurricanes_ Typhoons_ Tropical Storms
Tornadoes
Volcano
Mudslides_ Debris Flows_ Landslides