Sentences with phrase «based model parameter»

Not exact matches

Its parameters are based on additional detection of the carcinogenic compounds and modeling of groundwater flow, Marguerite Wolffsohn, the town's director of planning, told the board.

«Agent - based modeling is like a video game in the sense that you program certain parameters and rules into your simulation and then let your virtual agents play things out to the logical conclusion,» said Crabtree, who completed her Ph.D. in anthropology at WSU earlier this year.

Traditional CAD systems are «parametric,» which means that when engineers design models, they can change properties like shape and size («parameters») based on different priorities.

Demirci's Stanford team created the overall experimental device designs based on Tüzel's theoretical models, fabricated and tested the prototypes, and determined the best experimental parameters and medium for the sperm cells to swim through the device.

The new model calculates the amount of free hydrogen gas produced and stored beneath the seafloor based on a range of parameters — including the ratio of a site's tectonic spreading rate to the thickness of serpentinized rocks that might be found there.

Based on this data, we developed statistical models suitable for the inference of different climatic parameters in the past,» says the UCM researcher.

Based on the theoretical models, they predicted experimentally observable parameters of the reaction, such as the rate of hydrogen peroxide decomposition.

The NPV model enables users to translate a machine learning - based predictive model's performance over time from traditional empirical measures into dollar values by combining machine learning, data acquisition, operational costs, and investment parameters.

Coalescence - based modeling of demographic parameters estimate that the first domesticated rice population to split off from O. rufipogon was O. sativa ssp.

Future work includes building a multiscale simulation framework that establishes connections between the proposed model to atomistic models based on explicit parametrization of relevant transport and oxidation parameters via appropriate small - scale models.

The mesoscale models are based on a continuum description rather than an atomistic description, and therefore rely on effective parameters rather than direct simulation of chemical pathways.

Incorporating MRI - based parameters into a risk model could cut down on the number of unnecessary biopsies performed in patients with suspected prostate cancer.

Amsbio announces the launch of two new microplate - based metabolism stress kits enabling characterisation of the main parameters of mitochondrial function in live cells and evaluation of the glycolytic response of in vitro cell models to metabolic stress.

We characterize the capacity of the models to reproduce the disc parameters based on marginally resolved emission through analysis of two sets of simulated systems (based on the HIP 22263 and HIP 62207 data) with the noise levels typical of the Herschel images.

Summary estimates were calculated using a general variance - based method (random - effects model) with 95 % CIs.19 Because the potential confounders considered in multivariate analyses vary across studies, we used the parameter estimates in the most complex model, which typically include demographic, lifestyle, and dietary factors.

The exact parameters for this class are blurry: Base prices and features overlap with everyman crossovers like the larger Ford Escape and Volkswagen Tiguan, while well - equipped models encroach on Audi Q5 and Acura RDX territory.

For example, Volkswagen's Lane Assist feature uses a conventional, model - based program which precisely describes mathematical parameters for the visual recognition of lane markings.

This model uses Cholesky decomposition to generate multivariate correlated normal samples based on the user provided parameters.

The finding that based on the logistics model parameters used here, these differences can be demonstrated mathematically, supports present feeding guides and practices.

If we calibrate a driver / response relationship based on a criterion of some minimal correlation (or probability) from a linear model, but the calibration period from which that derives only actually samples some part of a more complex, non-linear response surface / curve, then the estimates of the parameter of interest in times past could be seriously wrong and / or the certainty in the parameter over-estimated.

Parameters are not the same thing as intuition based fiddling, they are needed to specify that it is the climate of our planet that is modeled.

The «no feedback» value is the one estimated by Myhre et al. 1998b, based on GH theory, laboratory spectroscopic data and model simulations (feedback parameter = 0).

They incorporated the effect of climate change by modifying the parameters of the Gumbel distribution of cyclone intensity based on increases in tropical cyclone intensity derived from climate model results over a broad region characteristic of the location in question.

All measurements are estimates of a physical parameter based on a model.

The errors in these parameters alone, much less scattering parameters for particulates, ice, etc. etc. would make any predictions based on models very insecure.

I define the SAW model as a filtered sawtooth using three «shape» parameters to describe the filtering, and base the AGW model on published work originating with Arrhenius, Hofmann, and Hansen.

However as David Springer keeps reminding us, the more parameters the less meaningful the model, and on that basis I would say any model as overfitted as Mike R's is not terribly meaningful.

I would be interested to see a range of results based on a plausible range of the free parameters, constrained where possible by physical models of the underlying phenomena.

The second plot shows the calculated Ocean Heat Content from the «Callendar model» fitted with the above parameters, and compares it with the 0 - 700m data held by NOAA, based on Levitus.

For a parameter that has meaning outside of one single model, as ECS has, each separate analysis based on a different model would have it's own Jeffreys's prior.

Oreskes (1998) argues for model evaluation (not validation), whereby model quality can be evaluated on the basis of the underlying scientific principles, quantity and quality of input parameters, the ability of a model to reproduce independent empirical data.

However, a Bayesian can make a forecast based on a prior with very broad support on a parameter (and broad support on a family of models), which results in a very diffuse prediction (or «range of predictions») which can be nearly nondisconfirmable on on a century's worth of data.

Well that's nice, the model you created, based on parameters you think are real world, comes up with an answer you think it should.

As Sorokhtin et al. (2007) mention, until recently a sound theory using laws of physics for the greenhouse effect was lacking and all numerical calculations and predictions were based on intuitive models using numerous poorly defined parameters.

As we learn further down this is based on a yet another study by parti - pris alarmists ramping up the climate change scare narrative using dodgy computer modeled projections of what might happen if all their parameters are correct (which they aren't).

We constrained these five parameters with five «observations» (either direct, or based on more complex model simulations).

As an alternative way, a number of (semi --RRB- empirical models are based on observations, aiming to associate cosmic - ray variations with various solar and heliospheric parameters.

Many physical modelers, and especially climate modelers, seems to think that as long as their models are «science - based» then there is no need to account for uncertainty in their outputs, notwithstanding that the model parameters are tuned with data, and that aspects of these models are likely to be ill posed (highly sensitive to small perturbations in the values assigned to parameters).

I can easily see a climate modeler adjusting their parameters based on a model run with too much negative internal variability because the results were too far from that expected.

The modeling approach is based on presenting the ionospheric parameter (most frequently the F - layer critical frequency foF2) by analytical expressions as a function of one or more geomagnetic or solar indices, called drivers.

This is achieved through the cooperation of an autoregression forecasting algorithm, called Time Series AutoRegressive — TSAR (Koutroumbas et al. 2008), with the empirical Storm Time Ionospheric Model — STIM (Tsagouri & Belehaki 2006, 2008) that formulates the ionospheric storm - time response based on solar wind input, exploiting recent advances in ionospheric storm dynamics that correlate the ionospheric storm effects with solar wind parameters (e.g., the magnitude of the IMF and its rate of change as well as the IMF's orientation in the north - south direction).

A subsequent paper, Modeling persistence in hydrological time series using fractional differencing (Water Resources, 1984), outlined a method to derive a particular ARFIMA model from the full autocorrelation function of a time series, and generate a corresponding random synthetic series based on the ARFIMA parameters derived from that autocorrelation structure.

They went on to select input parameters for the box model based on observations, computed the difference for those conditions, and asserted that there is a significant bias for climate studies.

[*] You had said: «is based purely on observational evidence, with no dependence on any climate model simulations... to obtain a direct measure of the overall climate response or feedback parameter... Measuring radiative flux imbalances provides a direct measure of Y, and hence of S, unlike other ways of diagnosing climate sensitivity.»

One of the parameters is high stand or low stand conditions based on sea level transgression / regression curves which is related to long term climate, but I am not aware of any oil companies that use anything remotely resembling what I understand to be a climate model with forcings, and certainly not one driven by something like CO2, solar or anyhting else, simply because you can not know the necessary parameters over the millions of years of geological time that you are interested in modelling.

«These model - based studies provide invaluable insight into the functioning of the climate system, because it is possible to vary processes and parameters independently, thus examining the role and importance of different climate mechanisms.

The entire climate change issue has been fabricated on the basis of these models through the introduction of a CO2 forcing parameter that has no physical basis and was fraudulently created for the sole purpose of relating CO2 emissions to global temperature when no such relationship possibly existed.

Usually in climate modeling, scientists will set the allowable range for each input parameter based on observational data.

To be valid RCP 8.5 would need to have some relevance in order to consider policy based on it's results, fudging models to produce results with parameters that have no bearing on the real physical world is pointless.

The partial radiative perturbations (PRPs) due to changes in cloud and due to the effects of the pre-existing climatological cloud distribution on non-cloud changes, known as «cloud masking», are calculated when atmospheric CO2 concentration is doubled for the HadSM3 and MIROC models and for a large ensemble of parameter perturbed models based on HadSM3.