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Regular readers of this blog will be aware of a growing tendency by climate scientists to say “we don’t know” when it suits the narrative. Whether you call that an unwillingness to make “bold” pronouncements, or an unwillingness to forego the training wheels of science, we’ll let the reader be the judge. The weakness of their science, and the almost complete scientific illiteracy of the politicians who, in governments around the world, are coming to believe the same thing, has provoked the usual chorus of boos and toads. I’ll leave that to other posts, but what I’d like to emphasize here is that simply not knowing is a legitimate stance for science to take, even if it doesn’t seem so at first. Consider the position of physics. How would Newton have reacted to an “I don’t know” statement from Einstein at any point during the “special theory of relativity”? Should researchers even bother with science if they can’t be sure of their conclusions? That’s the short answer, but the answer’s more complicated than that. When you consider what science is, and what it is for, you have to consider what science doesn’t do. Just consider the most familiar case: when you’re considering physical objects, science doesn’t have answers to questions of geometry. Then you’ve got yourself a problem. If, for example, you say “I don’t know”, you’ve just put yourselves beyond science. That’s the barest beginning of your problem. I’ve seen the explanations of how we do know about the shape of the universe and, thus, must make an assertion we don’t know. Yes, I’ve seen that. The trouble is that the explanations, and the limited science available to provide the explanations, come with a host of assumptions which, absent evidence, have no weight. If you make claims, you have to be able to back them up. If you only have an equation, without a context or a derivation of context, all the certainty in the world isn’t going to be enough. So, if you’re looking for an illustration of the confidence that science builds into its “I don’t know” statements, you’d do better looking to thermodynamics. Remember Mr. and Mrs. Reasonable, the couple who read up on nuclear energy and nuclear weapons on the internet and decided to buy a couple of

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Robustness analysis of pH-stabilized multicompartment bioreactors: applications for glucose fermentation. Multicompartment bioreactors are investigated for glucose fermentation in a pH-stabilized (limited diffusive mass transfer) environment. They consist of a glucose-containing vessel and a vessel filled with a carrier solution of 0.5% w/v KCl. The fluxes between the compartments are controlled at one boundary by a dialysis membrane in contact with one compartment and at the other boundary by a membrane covered by a diffusion cell. The advantages of a reactor with these boundaries are that multiple media can be used simultaneously and that the permeate flow rate is programmable. A model of a reactor is derived and general assumptions are made regarding time-dependent average fluxes. The model is solved numerically by a finite element method to provide stationary concentrations and fluxes for a given set of boundary conditions. A nonlinear optimization routine is used to determine the number of compartments and membrane area that best match a chosen set of experimental data. The robustness of the optimization is established through an analysis of the sensitivity of the optimization to the experimental setup and its sensitivity to the experimental data. This is done by varying each individual parameter while the others are kept fixed at a constant value. The model is validated by comparing simulated steady states of a reactor with the corresponding experimental data.Q: What’s the usage for $\mu$ and $\sigma$ and in what situations? I’ve seen a lot of example for the usage of $\mu$ and $\sigma$ in stats papers/books, but I couldn’t tell what the usage is. There are also few examples of how to use them in practice. Could someone explain the usage of them in a simple and clear way. For example, can I use $\mu$ and $\sigma$ for the concept of mean and std in the following way? $\mu \pm \sigma$ $\mu – \sigma$ and $\mu + \sigma$ Or does it not sound natural? What is $X$ and $Y$ about? What should I substitute them with to represent the concept? From the latter, can I use $\mu$ and $\sigma$ to calculate the probability of a variable is in a certain range $(a,b)$ using normal distribution? Could somebody please explain what is $\mu$, $\sigma$ and

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