wanyu/IteraTeR_full_sent huggingface dataset:
Original: The total number of COVID-19 infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options . Despite efforts to carefully monitor the COVID-19 pandemic, the reported number of confirmed cases is likely to underestimate the actual number of infections.
Revised: The total number of COVID-19 infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options . Despite efforts to carefully monitor the COVID-19 pandemic, the number of confirmed cases is likely to underestimate the actual number of infections.
Edit intent: clarity
Original: The total number of COVID-19 infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options . Despite efforts to carefully monitor the COVID-19 pandemic, the reported number of confirmed cases is likely to underestimate the actual number of infections.
Revised: The total number of COVID-19 infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options . Despite efforts to carefully monitor the COVID-19 pandemic, the reported number of confirmed cases may underestimate the total number of infections.
Edit intent: clarity
Original:  We aim to estimate the total number of COVID-19 infections in a straightforward manner using a demographic scaling model. This model is broadly applicable as it is based on little input data : deaths attributable to COVID-19 , COVID-19 infection fatality rates  , and life tables.
Revised:  We aim to estimate the total number of COVID-19 infections using an broadly applicable approach that is based on little input data : deaths attributable to COVID-19 , COVID-19 infection fatality rates , and life tables.
Edit intent: clarity
Original:  As many countries lack reliable estimates of age-specific infection fatality rates, we map them from a reference country onto countries of interest based on remaining life expectancy . This scaling accounts for cross-country differences in the age structure, the health status, and the health care system. We also introduce easy to apply formulas to quantify the bias that would be required in death counts and infection fatality rates in order to reproduce a certain estimate of infections. Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases.
Revised:  As many countries lack reliable estimates of age-specific infection fatality rates, we map them from a reference country onto countries of interest based on remaining life expectancy as a marker to account for differences in age structures, health conditions, and medical services. Across 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of ] confirmed cases.
Edit intent: clarity
Original:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases.
Revised:  Across the 10 countries with most COVID-19 deaths as of May 13 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of ] confirmed cases.
Edit intent: meaning-changed
Original:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases.
Revised:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of infections is estimated to be four ] confirmed cases.
Edit intent: meaning-changed
Original:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases.
Revised:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of 95\% prediction interval: 2-11] confirmed cases.
Edit intent: meaning-changed
Original:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases.
Revised:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of ] times higher than the number of confirmed cases.
Edit intent: meaning-changed
Original:  The uncertainty, however, is high, as the lower bound of the 95\% prediction interval suggests on average twice as many infections than confirmed cases, and the upper bound 10 times as many. Comparing our results with findings from local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population.
Revised:  Cross-country variation is high. The estimated number of infections is 1.4 million (six times the number of confirmed cases) for Italy; 3.1 million (2.2 times the number of confirmed cases) for the U.S.; and 1.8 times the number of confirmed cases for Germany, where testing has been comparatively extensive. Our prevalence estimates, however, are markedly lower than most others based on local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population.
Edit intent: meaning-changed
Original:  Comparing our results with findings from local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population.
Revised:  Comparing our results with findings from local seroprevalence studies . We introduce formulas for quantifying the bias that is required in our data on deaths in order to reproduce estimates published elsewhere. This bias analysis shows that either COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population.
Edit intent: meaning-changed
Original:  Comparing our results with findings from local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population.
Revised:  Comparing our results with findings from local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 deaths are severely underestimated, by a factor of two or more; or alternatively, the seroprevalence based results are overestimates and not representative for the total population.
Edit intent: meaning-changed
Original: Transmission rates in epidemic outbreaks  vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the course of the COVID-19 pandemic.
Revised: Transmission rates in epidemic outbreaks vary over time depending on the societal response. Non-pharmacological mitigation strategies such as social distancing and the adoption of protective equipment aim precisely at reducing transmission rates by reducing infectious contacts. To investigate the effects of mitigation strategies on the evolution of epidemics, nonlinear transmission rates that are influenced by the levels of infections, deaths or recoveries have been included in many variants of the classical SIR model. This class of models is particularly relevant to the course of the COVID-19 pandemic.
Edit intent: meaning-changed
Original: Transmission rates in epidemic outbreaks  vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the course of the COVID-19 pandemic. Following a mean field approach that models individuals like molecules in a well-mixed solution, I derive a modified SIR model in which the average daily contacts between susceptible and infected population are reduced based on the known infection levels, capturing the effects of social distancing policies.
Revised: Transmission rates in epidemic outbreaks vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the course of the COVID-19 epidemic, in which the population behavior has been affected by the unprecedented abundance and rapid distribution of global infection and death data through online platforms. This manuscript revisits a SIR model in which the reduction of transmission rate is due to knowledge of infections. Through a mean field approach that models individuals like molecules in a well-mixed solution, I derive a modified SIR model in which the average daily contacts between susceptible and infected population are reduced based on the known infection levels, capturing the effects of social distancing policies.
Edit intent: meaning-changed
Original:  Following a mean field approach that models individuals like molecules in a well-mixed solution, I derive a modified SIR model in which the average daily contacts between susceptible and infected population are reduced based on the known infection levels, capturing the effects of social distancing policies.
Revised:  Following a mean field approach that assumes individuals behave like molecules in a well-mixed solution, I derive a modified SIR model in which the average daily contacts between susceptible and infected population are reduced based on the known infection levels, capturing the effects of social distancing policies.
Edit intent: clarity
Original:  Following a mean field approach that models individuals like molecules in a well-mixed solution, I derive a modified SIR model in which the average daily contacts between susceptible and infected population are reduced based on the known infection levels, capturing the effects of social distancing policies. This approach yields a time-varying reproduction number that is continuously adjusted based on infection information through a negative-feedback term that is equivalent to Holling type II functions in ecology and Hill functions in chemistry and molecular biology.
Revised:  Following a mean field approach that models individuals like molecules in a well-mixed solution, one derives a time-varying reproduction number that is continuously adjusted based on infection information through a negative-feedback term that is equivalent to Holling type II functions in ecology and Hill functions in chemistry and molecular biology.
Edit intent: clarity
Original:  This approach yields a time-varying reproduction number that is continuously adjusted based on infection information through a negative-feedback term that is equivalent to Holling type II functions in ecology and Hill functions in chemistry and molecular biology.
Revised:  This approach yields a time-varying reproduction number that is continuously adjusted based on infection information through a negative feedback term that is equivalent to Holling type II functions in ecology and Hill functions in chemistry and molecular biology.
Edit intent: fluency
Original:  This approach yields a time-varying reproduction number that is continuously adjusted based on infection information through a negative-feedback term that is equivalent to Holling type II functions in ecology and Hill functions in chemistry and molecular biology.
Revised:  This approach yields a time-varying reproduction number that is continuously adjusted based on infection information through a negative-feedback term that is equivalent to Holling type II functions in ecology and Michaelis-Menten functions in chemistry and molecular biology.
Edit intent: meaning-changed
Original:  This feedback-adjustment of the transmission rate causes a structural reduction in infection peak, and simulations indicate that such reduction persists even in the presence of information delays. Simulations also show that a distancing policy based on infection data may substantially extend the duration of an epidemic.
Revised:  A step-by-step derivation of the model is provided, together with an overview of methods for its qualitative analysis, showing that negative feedback structurally reduces the peak of infections. At the same time, feedback may substantially extend the duration of an epidemic.
Edit intent: meaning-changed
Original:  If the distancing rate is linearly proportional to infections, this model adds a single parameter to the original SIR , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Revised:  Computational simulations agree with the analytical predictions, and further suggest that infection peak reduction persists even in the presence of information delays. If the mitigation strategy is linearly proportional to infections, this model adds a single parameter to the original SIR , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Edit intent: meaning-changed
Original:  If the distancing rate is linearly proportional to infections, this model adds a single parameter to the original SIR , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Revised:  If the distancing rate is linearly proportional to infections, a single parameter to the original SIR , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Edit intent: clarity
Original:  If the distancing rate is linearly proportional to infections, this model adds a single parameter to the original SIR , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Revised:  If the distancing rate is linearly proportional to infections, this model adds a single parameter is added to the SIR model , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Edit intent: clarity
Original:  If the distancing rate is linearly proportional to infections, this model adds a single parameter to the original SIR , making it useful to illustrate the effects of social distancing enforced based on awareness of infections .
Revised:  If the distancing rate is linearly proportional to infections, this model adds a single parameter to the original SIR , making it useful to illustrate the effects of infection-dependent social distancing .
Edit intent: clarity
Original:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Revised:  Here we show the unprecedented decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Edit intent: clarity
Original:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Revised:  Here we show that the decrease in global fossil CO2 emissions from January to April 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Edit intent: meaning-changed
Original:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Revised:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 7.8\% (938 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Edit intent: meaning-changed
Original:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) .
Revised:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a +6.8\% of 2- {\sigma} uncertainty) .
Edit intent: meaning-changed
Original:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) . Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Revised:  Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- {\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Edit intent: clarity
Original:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Revised:  Unlike other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Edit intent: meaning-changed
Original:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Revised:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) , industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Edit intent: fluency
Original:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Revised:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Edit intent: clarity
Original:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Revised:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 406 cities ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Edit intent: meaning-changed
Original:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation , and on heating degree days for commercial and residential sectors emissions (for 206 countries).
Revised:  Unlike other emerging estimates , ours show the temporal dynamics of emissions based on actual emissions data from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries ), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries).
Edit intent: meaning-changed
Original:  These dynamic estimates cover all of the human induced CO2 emissions from fossil fuel combustion and cement production. The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  These dynamic estimates cover all of the human induced CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: clarity