You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Namely, winter is coming. On the minds of many restaurant owners is the fact that this month's mild weather is just weeks away from becoming a cold, windy, rainy mess, perhaps heavily impacting outdoor dining - one of the few ways restaurants have been able to (barely) scrape by.

Abstract:
Background: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus.
Prior research on this topic has been inconclusive.
Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.
Methods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020.
Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study.
Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19.
We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.
Results: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date.
Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R].
Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees.
Moreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample.
We also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R].
Sensitivity analysis and simulations show that results are robust to multiple assumptions.
Despite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.
Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.
However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.
{"verdict": "NEI", "evidence": []}