Sentences with phrase «heuristics based»

To sort these out, and many more, we use heuristics based on various contextual and metadata reference points.

So that in general, a model can describe either a physics - based behavior, or it could describe a heuristic based solely on the empirical observations.

Wadhams (University of Cambridge); 4.1; Heuristic Based on recent EM measurements of first year ice thickness merged into probability density functions of ice thickness from recent submarine voyage and subtracting an assumed summer melt of up to 2 m.

They neglect the fact that thermodynamics is the valid macroscopic heuristic for statistical mechanics, and that stat mech computations can not be done on an heuristic basis at all; they are horrendously difficult, involving taking limits of nearly infinite sums in just the right way to get the relevant part of the answer and discard the parts that don't scale up to relevance as one goes to large systems.

Not exact matches

Psychologists call it «affect heuristic,» or the tendency to make judgments based on mood or emotion.

In short, we use all kinds of heuristics on a daily basis and apparently we do so for a good reason.

Selecting your future spouse based on the recognition heuristic might be overdoing it a bit, but when overwhelmed with potential choices at a speed - dating event, supermarket aisle or restaurant menu, going with a simple heuristic is a reasonable option.

One idea is that they are based on heuristics — mental rules of thumb which, applied in appropriate situations, allow us to make fast decisions with minimal cognitive effort.

System 1 is generally automatic, affective and heuristic - based, which means that it relies on mental «shortcuts.»

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.

The paper, titled «Heuristic drift - based model of the power scrape - off width in low - gas - puff H - mode tokamaks» can be found here.

It is not unreasonable to believe that error rates are higher in education because of the lack of reliable heuristics and a relatively thin research base.

Some of ExpertusONE Cloud LMS features include: simplified, customizable user interface with Web 2.0 features and heuristic design, personalized content based on each individual's role, profile and goals, universal mobile access with native iOS and Android apps and offline sync, and social and collaborative learning tools, including course - or role - based discussion threads, blogs, wikis, chat and expert networks.

A flow chart could even be made based on the heuristics of things to ask themselves.

Five research - based heuristics for using video in preservice teacher education.

The second heuristic, availability, dictates that we perceive something based on how easy that perception comes to mind.

We often supplement factual decisions for ones based on emotions, biases and «heuristics» (rules of thumb).

Furthermore, index funds that use heuristics (using a subset of stocks to mimic an index) create more imbalance as share price movements are magnified or demagnified based on the relationship between the subset and the true index.

So we do have to make some heuristic loose choices based on loose patterns — the kind of thing that we expect to be doing in a strategy game.

Like Dark Souls, much of the enjoyment stems from the heuristic - based gameplay, with players gradually learning the game's nuances with each mistake.

You do need a model though (which can be statistical, heuristic, or physics - based like a GCM)-- observations on their own are not sufficient.

Andersen, 3.9, Statistical / Heuristic (same as June) I continue to use the same method based on the maximum area in spring at the relatively stable reduction fraction we have seen the last 8 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.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, composites of several approaches, estimates based on various non sea ice datasets and trends, and subjective information (the heuristic category).

As with the pan-arctic outlooks, the regional responses are based on statistical methods, numerical models, and heuristic estimates.

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 prHeuristic / 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 prheuristic 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.

Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 + / - 0.7.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, estimates based on various non-sea ice datasets and trends, and subjective information (the «heuristic» category).

People will not necessarily loose faith in scientist X who says that Y will happen, or won't happen based on actual events because people have heuristics for discounting disconfirming evidence.

One analysis in particular called Hubbert Linearization that Deffeyes popularized has no fundamental basis and is often inadequate as a heuristic.

As the lead time shortens, there is more opportunity for heuristic forecasts based on persistence or observations of the current state of the atmosphere and ocean.

Lukovich et al. (Centre for Earth Observation Science, U. of Manitoba); 4.6; Heuristic - Dynamics Investigation of dynamical atmospheric contributions in spring to sea ice conditions in fall, based on comparison of 2011 and 2007 stratospheric and surface winds and sea level pressure (SLP) in April and May suggests regional differences in sea ice extent in fall, in a manner consistent with recent studies highlighting the importance of coastal geometry in seasonal interpretations of sea ice cover (Eisenman, 2010).

Morison and Untersteiner (University of Washington); 5.6 Million Square Kilometers; Heuristic Estimate is based on recent observations, including the previous winter Arctic Oscillation (AO), ice concentrations observed during North Pole Environmental Observatory (NPEO) hydro surveys, atmospheric and ice surface conditions observed with NPEO buoys and Web Cams, and recent ice trajectories.

Rigor et al. (Polar Science Center, University of Washington); 5.4 Million Square Kilometers; Heuristic This estimate is based on the prior winter Arctic Oscillation (AO) conditions, and the spatial distribution of the sea ice of different ages as estimated from a Drift - age Model (DM), which combines buoy drift and retrievals of sea ice drift from satellites (Rigor and Wallace, 2004, updated).

As with the pan-arctic outlooks, the regional responses were based on statistical methods, numerical models, and heuristic estimates.

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.

Lukovich et al. (University of Manitoba); 4.0 Million Square Kilometers; Heuristic Surface, stratospheric, and ice conditions in 2010 relative to 2007 atmospheric and ice conditions during June provide the basis for projection of September sea ice extent.

Regional outlooks were based on numerical models, statistical methods, and heuristic estimates.

Lukovich et al, 4.3, n / a, Heuristic It is hypothesized that the 2012 fall sea ice extent will attain values comparable to those of 2011 based on a heuristic assessment of sea ice and surface atmospheric dynamics, with regional losses governed by local wind and ice coHeuristic It is hypothesized that the 2012 fall sea ice extent will attain values comparable to those of 2011 based on a heuristic assessment of sea ice and surface atmospheric dynamics, with regional losses governed by local wind and ice coheuristic assessment of sea ice and surface atmospheric dynamics, with regional losses governed by local wind and ice conditions.

Morison, 4.8 (± 0.5), Heuristic (Same as June) My estimate of 4.8 million km2 is based on prior years» ice and Arctic Oscillation index plus in situ observations of ice in April and June.

You might actually try to directly address the algebra instead of waving your hands with word arguments based on an incorrect heuristic that is leading you to accept a hypothesis that violates the second law of thermodynamics.

Kaleschke and Tian - Kunze, 3.6 (± 0.7), Heuristic / Statistical (same as June) Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) milHeuristic / Statistical (same as June) Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) milheuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) million km2.

Morison, 5.0 (± 1.0), Heuristic My estimate is based on prior year's snow and ice and the Arctic Oscillation index plus in situ observations of ice in April, June, and now July.

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 prHeuristic / 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 prheuristic 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.

The heuristic forecast, based on current ice conditions and the regional temperature history, predicts open water in Kane Basin and Smith Sound.

The «backradiation» explanation is simply an heuristic argument based on the fact that, in equilibrium, the backradiation from the atmosphere and the incoming solar radiation must balance with the outgoing surface radiation.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, estimates based on various non-sea ice datasets and trends, and subjective information (i.e., the «heuristic» category).

As with the pan-arctic outlooks, the regional responses are based on heuristic estimates, numerical modeling, and statistical methods.

Heuristic methods represent a «rule of thumb» or trial and error approach toward problem solving based on discovery and experimentation.

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.