09.08.13 — Deep-Sea News So as a biostatistician, Milloy should know more about models. Now arguably some of this rests with me because I didn’t discuss the model more or specify the type model. There are some different types of models in science and each seeks to address the world differently. Null models: These models ask mathematically what would occur in the absence of a variable of interest. Say I’m interested in what controls the diversity of snails. My hypothesis is that as food increases you get higher diversity of snails. I would create ( and have ) a model that includes everything but food to see what pattern would emerge. If my null model without food yields a different answer than the patterns than I find in the real world then this supports my hypothesis. If on the other hand, my null model without food modelled in shows a similar pattern to what I find between snail diversity and food then it doesn’t support my hypothesis. Because the pattern can emerge from processes not even related to food as shown by my model. Exploratory models: I find an intriguing pattern but I don’t know exactly why it arises. I dump everything I know about the system into a set of equations. I vary different parameters of the model in each trial run. Say in the model above I add food but in one trial I model in lots of food and in another little. In other trials I alter temperature or maybe aspects of the biology of snails like growth and reproduction. In addition, if the results start to match a pattern in the real world, this gives some insights into what might be occurring naturally. So for example, I can predict the amount of dispersal by larvae versus environmental need of an average adult clam that would be needed to generate patterns of diversity across the Atlantic Ocean. Statistical predictive models: These find the best mathematical equations to fit real world data. They are built up from data and test against other data. The best models (of any type) are always based on data, make multiple predictions, and tested with a whole variety of new real data. In the simplest case, this model could be a simple linear regression in which we predict one variable (snail diversity) as a function of another (temperature). The more knowledge we enter into the model the better the fit of the model to the data will be. In the most complicated case, you have oceanographic models that pull into them a multitude of data streams, equations, and scientific knowlege. These are able to predict, with high accuracy, anything from currents to temperature to salinity to phytoplankton blooms and much more. With any model, if it performs poorly then it is chucked. Why would we keep a crappy model. Indeed, if a model is crap I would publish a paper saying its crap, because hell I can always use another good publication on my CV. Like I mentioned before, I didn’t state what kind of model the ORAS4 is. It is number three. Magdalena Balmaseda, the lead author of the study, had this to say in an email.
Источник: Deep-Sea News
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