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\emph{6 page limit, total proposal + figures can be 11.}  One of the most powerful observational tools for constraining the physics governing galaxy formation and evolution is morphology. The structural features of a galaxy are known to have close relationships with its physical properties; eg. the link between star formation rate and Hubble type \citep{Masters2010,Bundy2010,Schawinski2014} or spiral arms \citep{Willett2015}, bars and AGN \citep{Oh2012,Hao09,Galloway2015}, bars and atomic gas content \citep{Masters2012}, [lots more possibilities of examples - help with more non-galaxy zoo examples?] It is known that the demographics of most morphological features are \emph{not}, in general, constant as a function of redshift. This is not surprising, given that key elements involved in the formation of galaxies are also shown to change as the Universe evolves, eg. star formation is known to peak at $z\sim$~1 $z\sim1$  and drop steadily thereafter. [few paragraphs of more descriptive examples of how galaxy physics is related to morphology + reasons for studying $0  Obtaining morphological data for such large numbers of galaxies is a unique challenge, in that to date there is no system that can produce both accurate and complete morphologies using automated methods. This problem is especially present with increasing redshift, for two reasons. First, images of distant galaxies are less resolved, making it difficult to distinguish finer features in the image. Second, galaxy shapes become increasingly irregular in the early Universe, due to increased merger rate and the clumpy nature of star formation. As large telescopes become more capable of imaging these distant galaxies, we continue to discover for the first time new large-scale structures which do not exist at low z; $z$;  this creates a difficulty in defining an automated categorization for these unique types. Until automated methods overcome these challenges, visual classification by humans remains the most accurate method of measuring galaxy morphology, especially for galaxies beyond the local Universe. Visual classification is of course not without its own challenges, which are time and efficiency. While humans produce more accurate and complete classifications than a computer, the time it takes to do so is overwhelming for the wealth of data becoming available by large surveys. The Galaxy Zoo project has developed a highly innovative method for bypassing the time drawback while maintaining the accuracy of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, \texttt{www.galaxyzoo.org} asks people to classify the images by eye. Within its first year, each of the $\sim1$~million SDSS galaxies had already been classified an average of 40 times through the efforts of hundreds of thousands of members of the general public providing $\sim$~40 million $\sim40$~million  classifications \citep{Lintott2008,Fortson2012}. In 2010, Galaxy Zoo moved beyond the local Universe by including $\sim$~100,000 HST $\sim100,000$~HST  galaxies in a project known as Galaxy Zoo: Hubble. All galaxies were classified at least 40 times by late 2012. This project enabled the first direct, morphologically accurate studies to be done on the evolution of galaxies, several of which have already been completed with the preliminary data, including bar fraction with redshift \citep{Cheung2014,Melvin2014} and passive disk fraction with redshift \citep{Galloway2016}. These only represent a small fraction of the numerous possibilities for scientific investigation capable with these data; disk/spheroidal distinction, bars, spiral arms, clumpiness, and bulge dominance are a portion of the morphological information provided by this catalog (for the full list see Figure \ref{fig:decision tree}). Our aim with this proposal is to develop the next phase of Galaxy Zoo:Hubble, which we will hereafter refer to as Galaxy Zoo:Hubble 2 (GZH2). The motivation for extending this project is twofold: First, although the visual classification methods have been immensely successful thus far in obtaining robust morphologies for large (~100,000) ($~100,000$)  samples of galaxies, automation methods have improved since the first release in the form of powerful machine-learning algorithms. These alone are still not independently capable of accurate classification for galaxies at all redshifts, however \emph{combining} these methods with the current system of human classifications has been shown to reduce the classification time of galaxies by 80\% (can we cite something/ provide a figure Melanie?), thereby significantly improving both the efficiency and accuracy of GZH classifications. The details for this process are explained in full in the Analysis Plan. Second, in addition to the original GZH galaxies, an additional XX,XXX HST galaxies will be added to the project to be classified by this new method. By combining machine-learning with human classifications, GZH2 will provide the most morphologically accurate data for the widest redshift range (to $z\sim$~1.2) $z\sim1.2$)  currently available. These data will enable countless new science projects involving galaxy evolution than has ever been capable to this level of accuracy. With the funding from this proposal, our team will focus on two science cases: clumpy galaxies (need better zinger description) and the mass-metallicity relation.