wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this review, we provide an overview of what is currently known about the impact of residues on plant disease epidemics and describe how microbial interactions on these residues could be exploited to develop innovative crop protection strategies . Starting from the classical view of residues as a source of  inoculum, we  considered possibilities for the quantitative management of residues in crop protection. We describe residues as a transient half-plant/half-soil compartment constituting a key fully fledged microbial ecosystem  .
Revised:  In this review, we provide an overview of what is currently known about the impact of residues on plant disease epidemics and describe how microbial interactions on these residues could be exploited to develop innovative crop protection strategies . Starting from the classical view of residues as a source of inoculum, we considered possibilities for limiting the amount of residues on the soil surface to reduce the pathogen pressure. We then describe residues as a transient half-plant/half-soil compartment constituting a key fully fledged microbial ecosystem .
Edit intent: coherence
Original:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment  '.
Revised:  Finally, we discuss how the interactions between the microbial communities and the pathogens present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment '.
Edit intent: meaning-changed
Original:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment  '.
Revised:  Finally, we discuss how these communities present on residues could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment '.
Edit intent: clarity
Original:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment  '.
Revised:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: identification of keystone taxa and beneficial assemblages, then preservation of these taxa by adapted agronomic practices or development of synthetic communities , rather than the introduction of exogenous biocontrol agents as a 'treatment '.
Edit intent: meaning-changed
Original:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment  '.
Revised:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of a single exogenous biocontrol species designed as a 'treatment '.
Edit intent: clarity
Original:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment  '.
Revised:  Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of URLanisms naturally present to their preservation , rather than the introduction of exogenous biocontrol agents as a 'treatment product '.
Edit intent: clarity
Original:  We connected residue microbiome with the survival of residue-borne fungal plant pathogens, thus combining knowledge in microbial ecology and epidemiology, two disciplines still not sufficiently connected.
Revised:  We connect residue microbiome with the survival of residue-borne fungal plant pathogens, thus combining knowledge in microbial ecology and epidemiology, two disciplines still not sufficiently connected.
Edit intent: fluency
Original:  Starting from the classical but still relevant view of crop residues as a source of pathogen inoculum, we first considered possibilities for limiting the amount of residues on the soil surface to reduce the pathogen pressure.
Revised:  Starting from the classical but still relevant view of crop residues as a source of pathogen inoculum, we first consider possibilities for limiting the amount of residues on the soil surface to reduce the pathogen pressure.
Edit intent: fluency
Original:  We demonstrate that, for biologically realistic numbers of receptors, response functions, and odor mixture complexity, this remarkably simple method of elimination turns an underdetermined decoding problem into an overdetermined one, allowing accurate determination of the odorants in a mixture and their concentrations.
Revised:  We demonstrate that, for biologically realistic numbers of receptors, response functions, and odor mixture complexity, this remarkably simple method of elimination turns an underdetermined decoding problem into an overdetermined one, allowing accurate determination of odorants in a mixture and their concentrations.
Edit intent: fluency
Original: We propose the ideal individual model and the behavior coordinate system . Based on the ideal individual model, the behavior coordinate system and the quantum probability, a toy quantum model of human psychology is presented here in a different way.
Revised: The behavior coordinate system and the ideal individual model and the behavior coordinate system . Based on the ideal individual model, the behavior coordinate system and the quantum probability, a toy quantum model of human psychology is presented here in a different way.
Edit intent: coherence
Original: We propose the ideal individual model and the behavior coordinate system . Based on the ideal individual model, the behavior coordinate system and the quantum probability, a toy quantum model of human psychology is presented here in a different way.
Revised: We propose the ideal individual model are presented. The behavior state of an ideal individual is assumed to be represented by a behavior state function . Based on the ideal individual model, the behavior coordinate system and the quantum probability, a toy quantum model of human psychology is presented here in a different way.
Edit intent: meaning-changed
Original: There is increasing evidence that fairness and generosity are not exclusive human traits. Indeed, several experiments on chimpanzees, monkeys and other mammals show an inequity-aversion behavior. Namely, if some valuable resource, e.g. food, is unevenly divided between two individuals, the one who gets a smaller share may reject the reward, and, in some cases, there can even be an attempt of the animal with the larger share at equalizing the division. Therefore, animals can deliberately lower their gain in name of an apparent sense of fairness. Here we show the emergence of generosity in a resource-gathering-and-sharing gameinspired by animal behavior .
Revised: Resource sharing outside the kinship bonds is rare. Besides humans, it occurs in chimpanzee, wild dogs and hyenas as well as in vampire bats. Resource sharing is an instance of animal cooperation, where an animal gives away part of the resources that it owns for the benefit of a recipient. Taking inspiration from blood-sharing in vampire bats, here show the emergence of generosity in a resource-gathering-and-sharing gameinspired by animal behavior .
Edit intent: meaning-changed
Original:  Our results show that inequity aversion is favored by three factors: a long time horizon over which the players try to optimize their own game, by the similarity among players in their ability of performing the resource-gathering task, as well as by the availability of resources in the environment.
Revised:  Our results show that resource sharing is favoured by three factors: a long time horizon over which the players try to optimize their own game, by the similarity among players in their ability of performing the resource-gathering task, as well as by the availability of resources in the environment.
Edit intent: clarity
Original:  Our results show that inequity aversion is favored by three factors: a long time horizon over which the players try to optimize their own game, by the similarity among players in their ability of performing the resource-gathering task, as well as by the availability of resources in the environment.
Revised:  Our results show that inequity aversion is favored by three factors: a long time horizon over which the players try to optimize their own game, the similarity among players in their ability of performing the resource-gathering task, as well as by the availability of resources in the environment.
Edit intent: fluency
Original:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Revised:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Edit intent: fluency
Original:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Revised:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Edit intent: fluency
Original:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Revised:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Edit intent: fluency
Original:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Revised:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individ-ual epileptic patients, and limited clinical data constraints .
Edit intent: fluency
Original:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
Revised:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited availability of clinical data .
Edit intent: clarity
Original: To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type classification.
Revised: To overcome these challenges, in this paper, we present adeep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type classification.
Edit intent: meaning-changed
Original: To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type classification.
Revised: To overcome these challenges, in this paper, we present a deep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type classification.
Edit intent: clarity
Original: To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type classification. We evaluate our framework for seizure type classification and seizure detection on the recently released TUH EEG Seizure Corpus , where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby setting new benchmarks on the dataset.
Revised: To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type clas-sification. Experiments on the recently released TUH EEG Seizure Corpus , where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby setting new benchmarks on the dataset.
Edit intent: clarity
Original:  We evaluate our framework for seizure type classification and seizure detection on the recently released TUH EEG Seizure Corpus , where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby setting new benchmarks on the dataset.
Revised:  We evaluate our framework for seizure type classification and seizure detection on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset.
Edit intent: meaning-changed
Original:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Revised:  This task is challenging due to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Edit intent: clarity
Original:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Revised:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal-to-noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Edit intent: fluency
Original:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Revised:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Edit intent: fluency
Original:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Revised:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Edit intent: fluency
Original:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data.
Revised:  Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among epileptic patients, and limited availability of clinical data.
Edit intent: fluency
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset .
Edit intent: meaning-changed
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectral feature embeddings using an ensemble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset .
Edit intent: clarity
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type classification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset .
Edit intent: fluency
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEG Seizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset .
Edit intent: fluency
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure typeclassification setting new benchmarks on the dataset .
Edit intent: meaning-changed