Sentences with phrase «based prediction systems»

Lastly, Rocket aims to integrate next - generation blockchain - based prediction systems such as Gnosis and Augur to allow investors to predict success of certain startups.

Her PhD research is to develop a non-linear pattern recognition technique called Self - Organizing Map (SOM) based prediction system and a multi-model ensemble forecasting system for the probabilistic extended range prediction of Indian summer monsoon 3 - 4 pentads in advance.

Not exact matches

The FICO scoring system bases its prediction of a consumer's future behavior on a comparison between the credit history of the consumer in question and historical profiles of consumers with similar credit histories.

With the «Loyalty Prediction» tool, Facebook is not only curating thousands of data points across its user base to serve up ad audiences, its feeding those data points into a machine learning system that will anticipate what the next data point will be — a stark difference from simply collecting user data.

In Chicago, the Park District will use a new high - tech system that uses computer software to give real - time predictions of bacteria counts based on such factors as water temperature, modeling of the lake bottom and wave action monitored by buoys.

«When computation systems begin to analyze what spreads on Twitter and then makes decisions based on these predictions faster than human response time we will see unpredictable consequences,» he says.

But those predictions are based on only vague understanding of the system's inner workings.

Aiming to do better, Tony Song of NASA devised a much more precise tsunami prediction system based on GPS readings; he tested it successfully for the first time this past year.

Kruse's discovery confirms an astronomer's prediction in 1973, based on stellar evolution models of the time, that such a system should be possible.

They pinned more faith on a local system developed for them by the Institute of Hydrology in Wallingford, Oxfordshire, which bases its predictions on rainfall measurements collected electronically from a network of rain gauges.

Jordi Mestres, coordinator of the IMIM and UPF research group on Systems Pharmacology at the Biomedical Informatics Program (GRIB) states «With this study we have contributed to complementing the detection of these quite unstable fragments, with information on the mechanism of action of the drug, based on three aspects: similarity to other medicines, prediction of their pharmacological profile, and interference with specific biological pathways.

The new findings of successful multi-year drought / fire predictions are based on a series of computer modeling experiments, using the state - of - the - art earth system model, the most detailed data on current ocean temperature and salinity conditions, and the climate responses to natural and human - linked radiative forcing.

«All the predictions, the way we started looking for a critical point so far, were based on patterns calculated assuming you have a pot boiling on a stove — a somewhat static system,» said Mukherjee.

At the core of this system is a deep learning technology based on convolutional recurrent video prediction, or dynamic neural advection (DNA).

Scientists are involved in the evaluation of global - scale climate models, regional studies of the coupled atmosphere / ocean / ice systems, regional severe weather detection and prediction, measuring the local and global impact of the aerosols and pollutants, detecting lightning from space and the general development of remotely - sensed data bases.

A number of recent studies linking changes in the North Atlantic ocean circulation to sea ice extent led Yeager to think that it would also be possible to make decadal predictions for Arctic winter sea ice cover using the NCAR - based Community Earth System Model...

These webtools make use of the multilayered datasets from the BXD mouse population to expedite in silico gene function prediction through a series of integrative and complimentary systems analytical approaches, including (expression - based) phenome - wide association, transcriptome - / proteome - wide association, and (reverse --RRB- mediation analysis.

The webtools make use of the multilayered datasets from the BXD mouse population to expedite in silico gene function prediction through a series of integrative and complimentary systems analytical approaches, including (expression - based) phenome - wide association, transcriptome - / proteome - wide association, and (reverse --RRB- mediation analysis (Figure 1 - 2).

The highest prediction of 6.0 million square kilometers is based on a dynamical model forecast using the US Navy Earth System Model (NESM), whereas the lowest prediction of 3.4 million square kilometers comes from a heuristic contribution.

It consists of creating an original progressive system that will enable the annotation of nucleotide variations and the prediction of their pathogenicity based on the most robust algorithms and taking into account multiple levels of analysis.

Here we undertake a synthesis of central concepts for evolutionary predictions, based on examples of microbial and viral systems, cancer cell populations, and immune receptor repertoires.

In this report Harris makes «Recommendations to Improve the Louisiana System of Accountability for Teachers, Leaders, Schools, and Districts,» the main one being that the state focus «more on student learning or growth --[by] specifically, calculating the predicted test scores and rewarding schools based on how well students do compared with those predictions.»

The concept also features an Advanced Driver Assist System (ADAS) that uses optical and acoustic recognition sensors all over its body to provide highly autonomous driver assistance by choosing the best route based on its predictions for complex road conditions.

Destination Prediction — automatic destination entry to navigation system based on historical usage.

The operating system, which is based on Android 4.4 Kitkat, comes with scanning tool Firefly (which was first launched on the Fire Phone), TV and movie prediction feature ASAP (Advanced Streaming and Prediction) and automatic shutdown function Smarprediction feature ASAP (Advanced Streaming and Prediction) and automatic shutdown function SmarPrediction) and automatic shutdown function Smart Suspend.

By focusing solely on the value of a business and avoiding attempts to make predictions based on crowd psychology an investor can benefit from a system which allows the investor to profit from inevitable market swings caused by the speculation of opthers.

Note also, that what the sceintists are calling for is prediction based on understanding the system, not a simple «prediction» by a single model.

Basing predictions off of the well - known orbital patterns of the earth / moon / sun system could make future predictions as straightforward as producing tidal tables.

Based on results from large ensemble simulations with the Community Earth System Model, we show that internal variability alone leads to a prediction uncertainty of about two decades, while scenario uncertainty between the strong (Representative Concentration Pathway (RCP) 8.5) and medium (RCP4.5) forcing scenarios [possible paths for greenhouse gas emissions] adds at least another 5 years.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicbased on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicBased (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicbased on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicBased Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Predictions of the future state of a chaotic system, based on wishful thinking, are useful if suckers believe you, and keep giving you money.

This analytical report describes how United Nations organizations use the information provided by space - based technologies to monitor the Earth's climate system and support decision - making about climate change adaptation, prediction and mitigation, including addressing the needs identified under the United Nations Framework Convention on Climate Change (UNFCCC).

Complexity of the climate system and uncertainty surrounding input data preclude extremely accurate predictions, but they are not incompatible with an ability to approximate real world outcomes within a range narrow enough to justify future planning on the basis of reasonable probabilities.

McLaren et al. (Met Office Hadley Centre); 5.5 Million Square Kilometers; Modeling Prediction is based on an experimental model prediction from the Met Office Hadley Centre seasonal forecasting system (GloSea4) that became operational in SeptePrediction is based on an experimental model prediction from the Met Office Hadley Centre seasonal forecasting system (GloSea4) that became operational in Septeprediction from the Met Office Hadley Centre seasonal forecasting system (GloSea4) that became operational in September 2009.

The model prediction is based on the coupled Air - Sea - Ice Climate Forecast System (CFS) at NCEP.

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predicbased on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predicBased (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictors.

Zhang (Applied Physics Lab, University of Washington); 4.1 ± 0.6; Model This is based on numerical ensemble predictions starting on 6/1/2011 using the Pan-arctic Ice - Ocean Modeling and Assimilation System (PIOMAS).

In this paper, af - ter a brief tutorial on the basics of climate nonlinearity, we provide a number of illustrative examples and highlight key mechanisms that give rise to nonlinear behavior, address scale and methodological issues, suggest a robust alternative to prediction that is based on using integrated assessments within the framework of vulnerability studies and, lastly, recommend a number of research priorities and the establishment of education programs in Earth Systems Science.

The deception by the IPCC is based on their knowledge that the climate is a «complex non linear chaotic system» yet the IPCC persists in leading the general public into thinking they can actually predict (not project) the future climate and on the basis of their «predictions», we need to radically alter our lives and beggar ourselves.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicbased on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicBased Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

GFDL NOAA (Msadek et al.), 4.82 (4.33 - 5.23), Modeling Our prediction for the September - averaged Arctic sea ice extent is 4.82 million square kilometers, with an uncertainty range going between 4.33 and 5.23 million km2 Our estimate is based on the GFDL CM2.1 ensemble forecast system in which both the ocean and atmosphere are initialized on August 1 using a coupled data assimilation system.

«The Earth's climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm... there is a relatively poor understanding of the different types of nonlinearities, how they manifest under various conditions, and whether they reflect a climate system driven by astronomical forcings, by internal feedbacks, or by a combination of both... [We] suggest a robust alternative to prediction that is based on using integrated assessments within the framework of vulnerability studies... It is imperative that the Earth's climate system research community embraces this nonlinear paradigm if we are to move forward in the assessment of the human influence on climate.»

This study evaluates the hydrologic prediction skill of a dynamical climate model - driven hydrologic prediction system (CM - HPS), based on an ensemble of statistically - downscaled outputs from the Canadian Seasonal to Interannual Prediction System prediction skill of a dynamical climate model - driven hydrologic prediction system (CM - HPS), based on an ensemble of statistically - downscaled outputs from the Canadian Seasonal to Interannual Prediction System prediction system (CM - HPS), based on an ensemble of statistically - downscaled outputs from the Canadian Seasonal to Interannual Prediction System (Cansystem (CM - HPS), based on an ensemble of statistically - downscaled outputs from the Canadian Seasonal to Interannual Prediction System Prediction System (CanSystem (CanSIPS).

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicbased on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predicBased Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Our prediction is based on the GFDL - FLOR ensemble forecast system, which is a fully - coupled atmosphere - land - ocean - sea ice model initialized using a coupled data assimilation system.

The 6th ECMWF reanalysis (ERA6) will use the (C++ based) Object - Oriented Prediction System (OOPS) and (Fortran - based) Integrated Forecasting System (IFS) and there is a requirement to optimise the variational bias correction system under OOPS anSystem (OOPS) and (Fortran - based) Integrated Forecasting System (IFS) and there is a requirement to optimise the variational bias correction system under OOPS anSystem (IFS) and there is a requirement to optimise the variational bias correction system under OOPS ansystem under OOPS and IFS.

A new study published in Scientific Reports has developed a state - of - the - art drought and wildfire prediction system based on the decadal climate prediction approach using the NCAR Community Earth System system based on the decadal climate prediction approach using the NCAR Community Earth System System Model.

The ECMWF provides its supercomputer - run Integrated Forecasting System, a world - renowned numerical weather prediction model, as a basis for some Copernicus services, such as atmospheric forecasts and reanalysis data.

The results of the DCPP are a contribution to the 6th Coupled Model Intercomparison Project (CMIP6), to the WCRP Grand Challenge on Near Term Climate Prediction (NTCP), potentially to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), to the Global Framework for Climate Services (GFCS), and as one of the bases for the development of a WMO Commission for Basic Systems (CBS) Global Decadal Climate Outlook (GDCO) in support of applications.