Sentences with phrase «order models predicted»

First - order models predicted that solar wind compression regions would induce an increase in the angular velocity of the equatorial plasma and decrease the currents related to the lag from corotation, thus resulting in a dimmer aurora (e.g. Southwood & Kivelson 2001).

Not exact matches

In order to value bitcoin and predict future prices, analysts at Barclays came up with a model that likens it to an infectious disease.

[5][6] The theory could potentially explain why a mysterious repulsive form of energy known as the «cosmological constant», and which is accelerating the expansion of the universe, is several orders of magnitude smaller than predicted by the standard Big Bang model.»

These models utilize machine - learning techniques — the same ones used by companies like Netflix or Amazon that «learn» a customer's preferences and make recommendations based upon that data — in order to predict which chemical structures are likely to have the best overall CO2 absorption properties.

The model led them to hypothesize that a daily three - hour dose would enable the bacteria to predict delivery of the drug, and go dormant for that period in order to survive.

The researchers predict that the approach described in their study will pave the way to further develop the modelling of biomedical parameters and large - scale datasets in order to improve biological knowledge and patient outcome.

Complex as they may be, the activities and effects of consumers should be incorporated into global vegetation models in order to accurately predict the likely consequences of global change.

The expansive filling mechanism uses the elastic recovery properties of the groove walls to load nectar on the tongue in an order of magnitude that allows the hummingbirds to extract nectar at higher rates than are predicted by capillarity - based foraging models.

His own project, FuturICT, envisioned a «planetary nervous system» to collect and analyze data on a large scale in order to model society and predict epidemics or the next financial crisis.

In order to predict future changes in climate, scientists verify and refine their models against paleoclimate data from the ice cores Taylor and others pull up.

He emphasized the need to better understand the deep ventilation of oceanic heat, in order to improve modeling to reliably predict the future state of the Arctic climate system.

A reduced - order model of the simulations helped Nichols more precisely predict how to change the jet configuration to eliminate feedback tones.

«A metric is developed to predict collisions and is used together with the reduced - order model to design self - folding structures that lock themselves into stable desired configurations.»

«[NASA's model] predicts a dust concentration in the asteroid belt about an order of magnitude higher than the dust density near earth.»

Then, we should also ask ourselves what the criteria would be in order to define the success, as at the end of the process we only get the model out of data, that only predicts and not exactly gives us the answer.

Forgot to add: DDT has a 1/2 life of a year or so, orders of magnitude different than the consensus modeling predicted and everyone accepted as gospel.

Is your problem with the models that the system is too complex to predict or that climate scientists are biasing the models in order to obtain the outcome they desire?

I'm entertained by the 3rd order polynomial fit... It'll be fun to see if it predicts the future better than the «climate models.»

I certainly agree that there are many variables impacting the climate and that all of these and the correct weighting of each must be taken into consideration in order for a model to effectively predict future climate.

Going forward, if we stick with climatology and its 30 year averaging period then in order to provide policy makers with information about the outcomes from their policy decisions we need to come up with independent variable and dependent variable time series that are of much greater duration than the HADCRUT3, for 150 observed events is about the bare minimum for a statistically validated model that predicts with statistical significance.

Subsequently, however, based on statistical models that employ semi-empirical relationships between past and predicted future increases in global temperature, Vermeer and Rahmsdorf (2009), Jevrejeva et al. (2010) and Grinsted et al. (2010) derived much greater increases on the order of 60 to 190 cm over the same time interval.

The University of Western Australia's Ryan Lowe led a team of researchers who studied a reef system off the coast of northwestern Australia, as well as other reef systems across the globe, in order to develop a new model for predicting how rapid sea level rise will impact daily water temperature extremes within these shallow reefs over the next century.

(80 is chosen because we have about 100 years of temperature record, and we have to at least test a prediction on 1966 - 1988 in order to test a model which will be used to predict 1988 to 2010).

Forget the models, they leave out so much science that they are nothing more than curve fitting routines tweaked to death in order to predict the past.

The scientists are now setting to work devising a website that would allow the public to enter personal data in order to find out how long the model predicts they'll live.

At one end of the spectrum, if we remain on the BAU path, and all available credible evidence implies that we will, the global climate models predict we will experience global mean temperature increases on the order of 5 C by the end of the century.

It means that, for every W m $ ^ -LCB--2 -RCB- $ of excess energy we put into our system, our model predicts that the surface temperature must increase by $ -1 / \ lambda = 0.3 $ K in order to re-establish planetary energy balance.

So in order to predict AGW accurately, the models need to be validated, and so far, with the exception of climateprediction.net, most validation studies I have read have failed to take into account the sensistivity of the models to parameter tuning.

The model outputs are generally presented as an average of an ensemble of individual runs (and even ensembles of individual runs from multiple models), in order to remove this variability from the overall picture, because among grownups it is understood that 1) the long term trends are what we're interested and 2) the coarseness of our measurements of initial conditions combined with a finite modeled grid size means that models can not predict precisely when and how temps will vary around a trend in the real world (they can, however, by being run many times, give us a good idea of the * magnitude * of that variance, including how many years of flat or declining temperatures we might expect to see pop up from time to time).

According to the informant, the company used it to build a model predicting voter's behavior during US Presidential election in order to influence it.