wanyu/IteraTeR_full_sent huggingface dataset:
Original: China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century.
Revised: China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 C warming above pre-industrial levels by the end of the century.
Edit intent: clarity
Original:  Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, will contribute to China's meeting this target.
Revised:  We use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, will contribute to China's meeting this target.
Edit intent: coherence
Original:  Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, will contribute to China's meeting this target.
Revised:  Here, we use the Global Change Analysis Model to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, will contribute to China's meeting this target.
Edit intent: clarity
Original:  Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, will contribute to China's meeting this target.
Revised:  Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) in particular, will contribute to China's meeting this target.
Edit intent: fluency
Original:  Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, will contribute to China's meeting this target.
Revised:  Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC) , in particular, could contribute to China's meeting this target.
Edit intent: clarity
Original:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC.
Revised:  Our results show that negative emissions could play a large role, offsetting on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC.
Edit intent: clarity
Original:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC.
Revised:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 3 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC.
Edit intent: meaning-changed
Original:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC.
Revised:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 per year from difficult-to-mitigate sectors such as freight transportation and heavy industry. This includes up to a 1.6 GtCO2 per year of that coming from DAC.
Edit intent: meaning-changed
Original:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC. DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  Our results show that , for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year contribution from DAC , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Edit intent: clarity
Original:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  DAC, like other forms of negative emissions , constituting up to 60\% of total projected negative emissions in China. But DAC, like bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Edit intent: meaning-changed
Original:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation , has not yet been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Edit intent: clarity
Original:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at anywhere approaching the scales required to meaningfully contribute to climate mitigation . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Edit intent: clarity
Original:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at these scales will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Edit intent: fluency
Original:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and natural resources such as water, land, and energy in China and beyond .
Edit intent: clarity
Original:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond .
Revised:  DAC, like other forms of negative emissions , such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale . Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China .
Edit intent: clarity
Original:  However, recent KGE models tend to improve performance by excessively increasing vector dimensions, which would cause enormous training costs and save storage in practical applications. To address this problem, we first theoretically analyze the capacity of low-dimensional space for KG embeddings based on the principle of minimum entropy. Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Revised:  However, recent KGE models tend to improve performance by excessively increasing vector dimensions, which would cause enormous training costs and require more storage space. In this paper, instead of training high-dimensional models , we propose a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional KGE models as teachers . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Edit intent: coherence
Original:  Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Revised:  Then , we propose MulDE, a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional KGE models as teachers . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Edit intent: meaning-changed
Original:  Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Revised:  Then , we propose a novel knowledge distillation framework , which includes multiple low-dimensional KGE models as teachers . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Edit intent: clarity
Original:  Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Revised:  Then , we propose a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional hyperbolic KGE models as teachers . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Edit intent: meaning-changed
Original:  Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Revised:  Then , we propose a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional KGE models as teachers and two student components, namely Junior and Senior . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Edit intent: meaning-changed
Original:  Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
Revised:  Then , we propose a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional KGE models as teachers . Under a novel iterative distillation strategy, the Junior component, a low-dimensional KGE model, asks teachers actively based on its preliminary prediction results, and the Senior component integrates teachers' knowledge adaptively to train the Junior component based on two mechanisms: relation-specific scaling and contrast attention .
Edit intent: meaning-changed
Original:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000  Twitter users in the United States from February 1 to April 30  and dissect the hoarding-related tweets by age, gender, and geographic location.
Revised:  First, we collect hoarding-related tweets shortly after the outbreak of the coronavirus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 Twitter users in the United States from February 1 to April 30 and dissect the hoarding-related tweets by age, gender, and geographic location.
Edit intent: meaning-changed
Original:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000  Twitter users in the United States from February 1 to April 30  and dissect the hoarding-related tweets by age, gender, and geographic location.
Revised:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from February 1 to April 30 and dissect the hoarding-related tweets by age, gender, and geographic location.
Edit intent: meaning-changed
Original:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000  Twitter users in the United States from February 1 to April 30  and dissect the hoarding-related tweets by age, gender, and geographic location.
Revised:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 Twitter users in the United States from March 1 to April 30 and dissect the hoarding-related tweets by age, gender, and geographic location.
Edit intent: meaning-changed
Original:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000  Twitter users in the United States from February 1 to April 30  and dissect the hoarding-related tweets by age, gender, and geographic location.
Revised:  First, we collect hoarding-related tweets shortly after the outbreak of the corona virus . Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 Twitter users in the United States from February 1 to April 30 , 2020, and dissect the hoarding-related tweets by age, gender, and geographic location.
Edit intent: meaning-changed
Original:  With the proposed computational framework, we derive significant findings, e.g. the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter users . Furthermore, using topic modeling, we investigate the opinions expressed about the hoarding behavior by categorizing these topics according to demographic and geographic groups.
Revised:  We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter users . Furthermore, using topic modeling, we investigate the opinions expressed about the hoarding behavior by categorizing these topics according to demographic and geographic groups.
Edit intent: clarity
Original:  With the proposed computational framework, we derive significant findings, e.g. the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter users . Furthermore, using topic modeling, we investigate the opinions expressed about the hoarding behavior by categorizing these topics according to demographic and geographic groups.
Revised:  With the proposed computational framework, we derive significant findings, e.g. the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population . Furthermore, using topic modeling, we investigate the opinions expressed about the hoarding behavior by categorizing these topics according to demographic and geographic groups.
Edit intent: clarity
Original:  With the proposed computational framework, we derive significant findings, e.g. the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter users . Furthermore, using topic modeling, we investigate the opinions expressed about the hoarding behavior by categorizing these topics according to demographic and geographic groups.
Revised:  With the proposed computational framework, we derive significant findings, e.g. the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter users . Furthermore, using topic modeling, we investigate the opinions expressed towards the hoarding behavior by categorizing these topics according to demographic and geographic groups.
Edit intent: clarity
Original: Solving optimization tasks based on functions and losses with a topological flavor is a very active and growing field of research in  Topological Data Analysis, with plenty of applications in non-convex optimization, statistics and machine learning.
Revised: Solving optimization tasks based on functions and losses with a topological flavor is a very active , growing field of research in Topological Data Analysis, with plenty of applications in non-convex optimization, statistics and machine learning.
Edit intent: fluency
Original: Solving optimization tasks based on functions and losses with a topological flavor is a very active and growing field of research in  Topological Data Analysis, with plenty of applications in non-convex optimization, statistics and machine learning.
Revised: Solving optimization tasks based on functions and losses with a topological flavor is a very active and growing field of research in data science and Topological Data Analysis, with plenty of applications in non-convex optimization, statistics and machine learning.
Edit intent: meaning-changed
Original: Solving optimization tasks based on functions and losses with a topological flavor is a very active and growing field of research in  Topological Data Analysis, with plenty of applications in non-convex optimization, statistics and machine learning.
Revised: Solving optimization tasks based on functions and losses with a topological flavor is a very active and growing field of research in Topological Data Analysis, with applications in non-convex optimization, statistics and machine learning.
Edit intent: clarity
Original:  In this article , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient descent for such a functional .
Revised:  To address this issue , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient descent for such a functional .
Edit intent: meaning-changed
Original:  In this article , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient descent for such a functional .
Revised:  In this article , we study the differentiability of a general map associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient descent for such a functional .
Edit intent: fluency
Original:  In this article , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient descent for such a functional .
Revised:  In this article , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map . Building on real analytic geometry arguments, we propose a general framework that allows us to define and compute gradients for persistence-based functions in a very simple way. We also provide a simple, explicit and sufficient condition for convergence of stochastic subgradient descent for such a functional .
Edit intent: meaning-changed
Original:  In this article , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient descent for such a functional .
Revised:  In this article , we study the differentiability of a general functional associated with the most common topological construction, that is, the persistence map , and we prove a convergence result of stochastic subgradient methods for such functions .
Edit intent: clarity