Sentences with phrase «resulting general model»

The resulting general model is portrayed in Figure 1.

Not exact matches

The results presented on this slide are model performance results, which are unaudited and do not reflect actual results of a fund managed by the General Partner.

Two years ago I developed a simple statistical model that tries to predict the outcome of general elections from local election results.

New results challenge the existing paradigm that late model diesel cars are associated, in general, with far higher PM emission rates.

The researchers also tested their prediction models on the validation set, and this resulted in ROC (receiver operating characteristic) plot points with an AUC (area under the curve) of 91.6 percent for general responsiveness, 89.7 percent for TNFi response and 85.7 percent for rituximab response.

Cosmic ray ionization in the Martian atmosphere has been studied extensively using computational models (Whitten et al. 1971; Molina - Cuberos 2001; Molina - Cuberos et al. 2001; Norman et al. 2014; Gronoff et al. 2015), with the general result that the atmospheric ionization profile due to GCR is relatively flat, with monotonically increasing ionization rates with decreasing altitude and increasing atmospheric density.

However, it seems that one common trait among some climate models is the indication that a global warming may result in a more a general El Niño - type average state (eg.

The model, created by Gary Staebler of General Atomics and reported in a paper in Physics of Plasmas with Grierson as first author, investigated the DIII - D experimental results in conditions mimicking those expected in ITER.

Plenary Presentations Jessica Mester — «Comparison of Genetic Counselor Time Investment Utilizing Coupled and Uncoupled Practice Models: Final Results from the Cleveland Clinic General Genetics Clinic Time Study» — Best Abstract Award Recipient

The profiling methods used in this research to identify molecular changes may be a general approach for differentiating the molecular pathology of disease models resulting from agent - specific effects, such as the effects of the two neurotoxins used in this research.

Slingo, J.M., et al., 1996: Intraseasonal oscillations in 15 atmospheric general circulation models: Results from an AMIP Diagnostic Subproject.

That would make the argument a lot more convincing, especially for some of my more skeptical colleges that deeply distrust results from (in their view) overly optimized and complex General Circulation Models.

This model policy was developed as a result of the 2013 Virginia General Assembly amended Code § 22.1 - 276.01 to define bullying.

Overall results of the study indicate that the RLT model is effective in training general educators to implement such interventions as the Benchmark Strategy Instruction Process, but they also show that «traditional in - service without any follow - up was not effective staff development, since none of the control group teachers consistently implemented the [intervention].»

Charter schools in general and KIPP's model in particular have drawn their share of criticism, but the system's results in New Orleans have been worthy of consideration.

More Toyota - and General Motors - built SUVs last past 320,000 kms than any other model of vehicle, according to the results of an updated study commissioned by iSeeCars.com.

As a result, prospective customers and common folks alike do a general search and gather information about the new models through various sources.

Michiel van Ratingen, Euro NCAP Secretary General, says: «Proven safety benefits of ESC resulted in new legislation requiring the technology to be fitted as standard on all new models in Europe as of next year.

«If you are looking for immediate results, you might be a little disappointed,» said Alan Starling, an Osceola County auto dealer who owns a string of General Motors outlets.Domestic and foreign manufacturers will display more than 400 1996 models during the show, from cars and minivans to sport - utilities and trucks.

Euro NCAP's Secretary General, Michiel van Ratingen, said, «Volvo has invested in safety, has made key technologies standard across the model range and the results speak for themselves: a very impressive five - star rating.

Following the below steps will help dogs become model canine citizens and also result in safer communities, thus resulting in a reduction of the proposal of adverse legislation that targets bull breed owners and pet ownership in general.

Tweaks to suspension and aerodynamics systems along with other general adjustments will result in a handling model that provides much more feedback to the player and more responsiveness, meaning those little slides that spelt doom in F1 2010 can now be saved if you're quick enough.

It's something of an abstract concept, but with real world implications, and the universality of such physical models, based on things like radiative balance, atmospheric composition and density, distance from the local Sun, etc., is a very strong argument in favor of general acceptance of the results of climate models and observations on Earth.

I can't believe that people still don't understand that models are never perfect, but they can be illustrative and in the case of the hockey still the results are robust due to the fact that the same general results are found in multiple reconstructions including the reconstructions including the McIntyre / McKitrick corrections.

That would make the argument a lot more convincing, especially for some of my more skeptical colleges that deeply distrust results from (in their view) overly optimized and complex General Circulation Models.

«It is now widely known that the water vapor feedback in general circulation models (GCMs) is close to that which would result from a climate ‐ invariant distribution of relative humidity [Soden and Held, 2006], as long anticipated before the advent of such models [e.g., Arrhenius, 1896; Manabe and Wetherald, 1967].»

I think that is a little harsh, and we should not underestimate the importance of consistency in forecasting the general trends, nor the context it brings to how we interpret and present our present model results.

This thesis presents the results of several general circulation model simulations aimed at studying the effect of ocean circulation changes when they occur in conjunction with increased atmospheric trace gas concentrations.

Experiments with coupled ocean - atmosphere general circulation models (which represent the complexity of the climate system much more realistically than this simple model) give similar results.

«Numerical Results from a Nine - Level General Circulation Model of the Atmosphere.»

Rowlands (2012) write, «Here we present results from a multi-thousand-member perturbed - physics ensemble of transient coupled atmosphere — ocean general circulation model simulations.

It's importance is that it is a highly consistent general result across current numerical climate models.

These models may have consistent bias from bad or incomplete science, or other defects that produce general biased results.

The main justifications offered for climate alarmism are expensive general circulation models, which cost taxpayers many billions of dollars but prove nothing except that garbage in results in garbage out.

These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods... http://dx.doi.org/10.1175/JCLI-D-12-00766.1

Despite enthusiasm for the model outputs among mathematicians, the models in general produce predictions that simply do not conform to a wide range of experimental results.

Computer simulations of the climate, referred to as «general circulation models» (GCMs), can be used to assess the sensitivity of climate to changes that might result from increased greenhouse gases.

The fact that Wahl and Ammann (2006) admit that the results of the MBH methodology does not coincide with the results of other methods such as borehole methods and atmospheric - ocean general circulation models and that Wahl and Ammann adjust the MBH methodology to include the PC4 bristlecone / foxtail pine effects are significant reasons we believe that the Wahl and Amman paper does not convincingly demonstrate the validity of the MBH methodology.

These results place general questions on widely acknowledged vertical particle flux models, which apparently do not fully explain the relationship between primary production and organic carbon burial in high productive areas.

Importantly, the changes in cereal yield projected for the 2020s and 2080s are driven by GHG - induced climate change and likely do not fully capture interannual precipitation variability which can result in large yield reductions during dry periods, as the IPCC (Christensen et al., 2007) states: ``... there is less confidence in the ability of the AOGCMs (atmosphere - ocean general circulation models) to generate interannual variability in the SSTs (sea surface temperatures) of the type known to affect African rainfall, as evidenced by the fact that very few AOGCMs produce droughts comparable in magnitude to the Sahel droughts of the 1970s and 1980s.»

With all the many different ways of calculating these numbers (empirically and from simple models and general circulation models), and different results that have been obtained from these analyses, why hasn't this range and central value budged in over 3 decades?

All the General Circulation Models, also known as Global Climate Models (GCM), just set various evaporation and precipitation parameters to achieve approximately the result:

Several steps have been carried out in various workshops and meetings resulting in a compilation of relevant physical effects, a general modeling framework, a first compilation of important terminology, and handbook chapter structure.

Here we present results from a multi-thousand-member perturbed - physics ensemble of transient coupled atmosphere — ocean general circulation model simulations.

Let's be clear here, they're theorized feedbacks in unverified general circulation models whose results fit the IPCC's expectations, and as you know, when expectations are met it's difficult for even good scientists to look further, let alone for the politicians running the show.

General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Timegeneral aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in TimeGeneral Introduction Computation of FFT in Time Series

The fact that one model gives the right answer does not validate even the methodology of that model, let alone «the models» and their results in general.

The ensemble mean of the MMD models projects a general decrease in snow depth (Chapter 10) as a result of delayed autumn snowfall and earlier spring snowmelt.

The estimated GST drop due to fine dust from the actual atmospheric nuclear explosions based on the published simulation results by other researchers (a single column model and Atmosphere - Ocean General Circulation Model) has served to explain the stagnation in global warmodel and Atmosphere - Ocean General Circulation Model) has served to explain the stagnation in global warModel) has served to explain the stagnation in global warming.

When heavy rainfall probabilities were next investigated in ensembles of two atmospheric general circulation models, run with and without anthropogenically - induced sea surface temperature changes, results were model - dependent.