Steven Reece
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View article: Infection of prepubertal heifer calves as a natural host model for Tritrichomonas foetus
Infection of prepubertal heifer calves as a natural host model for Tritrichomonas foetus Open
Introduction Tritrichomonas foetus is a sexually transmitted flagellate that causes economic loss in the cattle industry throughout the world. In the United States, there are no approved treatments for the parasite. Owing to its transmissi…
View article: Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks
Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks Open
There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial instituti…
View article: Hydrological concept formation inside long short-term memory (LSTM) networks
Hydrological concept formation inside long short-term memory (LSTM) networks Open
Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract i…
View article: Disaster, Infrastructure and Participatory Knowledge: The Planetary Response Network
Disaster, Infrastructure and Participatory Knowledge: The Planetary Response Network Open
There are many challenges involved in online participatory humanitarian response. We evaluate the Planetary Response Network (PRN), a collaboration between researchers, humanitarian organizations, and the online citizen science platform Zo…
View article: Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya
Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya Open
East Africa has experienced a number of devastating droughts in recent decades, including the 2010/2011 drought. The National Drought Management Authority in Kenya relies on real-time information from MODIS satellites to monitor and respon…
View article: Supplement on CC1
Supplement on CC1 Open
Abstract. Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to…
View article: Comment on hess-2021-566
Comment on hess-2021-566 Open
Abstract. Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains, what have these models learned? Is it possible to…
View article: Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks
Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks Open
Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains, what have these models learned? Is it possible to extract i…
View article: Disaster mapping from satellites: damage detection with crowdsourced\n point labels
Disaster mapping from satellites: damage detection with crowdsourced\n point labels Open
High-resolution satellite imagery available immediately after disaster events\nis crucial for response planning as it facilitates broad situational awareness\nof critical infrastructure status such as building damage, flooding, and\nobstru…
View article: Disaster mapping from satellites: damage detection with crowdsourced point labels
Disaster mapping from satellites: damage detection with crowdsourced point labels Open
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructi…
View article: Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models Open
Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated…
View article: Comment on hess-2021-127
Comment on hess-2021-127 Open
Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have d…
View article: Belief revision and dialogue management in information retrieval
Belief revision and dialogue management in information retrieval Open
This report describes research to evaluate a theory of belief revision proposed by Galliers in the context of information-seeking interaction as modelled by Belkin, Brooks and Daniels and illustrated by user-librarian dialogues. The work c…
View article: Comment on hess-2021-127
Comment on hess-2021-127 Open
Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have d…
View article: Comment on hess-2021-127
Comment on hess-2021-127 Open
Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have d…
View article: Reply on CC2
Reply on CC2 Open
Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have d…
View article: Reply on CC1
Reply on CC1 Open
Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have d…
View article: Comment on hess-2021-127
Comment on hess-2021-127 Open
Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have d…
View article: Benchmarking Data-Driven Rainfall-Runoff Models in GreatBritain: A comparison of LSTM-based models with four lumpedconceptual models
Benchmarking Data-Driven Rainfall-Runoff Models in GreatBritain: A comparison of LSTM-based models with four lumpedconceptual models Open
Long short-term memory models (LSTMs) are recurrent neural networks from the emerging field of Deep Learning (DL), which have shown recent promise when predicting time-series especially when data are abundant. Rainfall-runoff modelling pre…
View article: Rainfall-Runoff Simulation and Interpretation in Great Britain using LSTMs
Rainfall-Runoff Simulation and Interpretation in Great Britain using LSTMs Open
<p>Techniques from the field of machine learning have shown considerable promise in rainfall-runoff modelling. This research offers three novel contributions to the advancement of this field: a study of the performance of LSTM based …
View article: Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes Open
Satellites allow large‐scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very‐high‐resolution satellite imagery has been successfully used to detect and count a number of wildlife speci…
View article: Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning
Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning Open
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mini…
View article: Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning
Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning Open
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mini…
View article: Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes Open
Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, hom…
View article: Mining and Tailings Dam Detection In Satellite Imagery Using Deep\n Learning
Mining and Tailings Dam Detection In Satellite Imagery Using Deep\n Learning Open
This work explores the combination of free cloud computing, free open-source\nsoftware, and deep learning methods to analyse a real, large-scale problem: the\nautomatic country-wide identification and classification of surface mines and\nm…
View article: Deep Learning for Drought and Vegetation Health Modelling: Demonstrating the utility of an Entity-Aware LSTM
Deep Learning for Drought and Vegetation Health Modelling: Demonstrating the utility of an Entity-Aware LSTM Open
<p>Tools from the field of deep learning are being used more widely in hydrological science. The potential of these methods lies in the ability to generate interpretable and physically realistic forecasts directly from data, by utili…
View article: Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources Open
Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these different information sources could bring major benefits to app…
View article: The Third Space: The Meeting of Jew and Christian in the Act of Remembering, Restoring, and Reconciling - A Case Study of the Matzevah Foundation
The Third Space: The Meeting of Jew and Christian in the Act of Remembering, Restoring, and Reconciling - A Case Study of the Matzevah Foundation Open
Problem Due to long-standing religious, racial, and cultural tensions, a complex and challenging relationship exists between Jews and Christians. The resulting breach isolates and separates these two faith groups from each other. Consequen…