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quadrant (where they can be observed by both the VLA and ALMA) presents an ideal  starting point for these observations.  %  \section{Acknowledgements} %  We thank the referee for thorough and very helpful comments that strengthened %  this Letter. This work was supported by NSF grant AST 1009847. %\bibliography{boundhii}  diff --git a/ch_conclusion.tex b/ch_conclusion.tex  index 513bfe0..592e3fd 100644  --- a/ch_conclusion.tex  +++ b/ch_conclusion.tex 
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\input{preface}  \chapter{Conclusions}  \label{ch:conclusion}  The key conclusions of this thesis are divided into two components. The first  centers on the  Bolocam Galactic Plane Survey and its high-level results.  The BGPS  has laid the grounds for an extensive study of dense gas within our Galaxy. It is the first blind survey of the Galactic plane at millimeter  wavelengths, where optically thin dust emission dominates the observed signal.  \begin{itemize}  \item The BGPS has had two data releases and its  pipeline has been well-characterized. The angular transfer function drops  from $100\%$ recovery at 100\arcsec to $\sim50\%$ recovery at 300\arcsec.  \item The BGPS map power spectra, when compared with Herschel Hi-Gal power  spectra, indicate that in some portions of the galaxy, the smallest  scales are \emph{warmer} than the largest scales, hinting that internal  heating by forming young stars is significant.  \item There are 3 massive proto-clusters in the northern Galactic plane,   G10.62, W49, and W51.  \item There are about 20 `clumps' of mass $M\gtrsim10^4$ \msun in the northern  plane.  \item All of these clumps are forming massive stars at present, implying that  the starless timescale for the parent clumps is $\tau_{starless}<0.5$ Myr.  \item The BGPS and comparable ground-based surveys are excellent tools for  identifying the precursors to massive clusters. Because the galaxy is transparent  at 1.1 mm, the BGPS can be used for galaxy-wide population analyses  \item Careful distance determination is crucial for population studies  \end{itemize}  %  I examined the brightest sources within the BGPS, discovering 18 with masses %  $M>10^4$ \msun, large enough to form bound clusters. These sources are all %  actively star-forming and can now be used for unbiased proto-cluster population %  studies. These observations allow us to place an upper limit on the starless %  lifetimes of young massive clusters $\tau_{starless} < 0.6$ Myr. The second component is a study of gas density and turbulence. The probability  distribution of gas density in the interstellar medium is generally thought to  be governed by turbulence, which robustly delivers a lognormal probability  distribution for density and velocity. Turbulence requires a driving force on  large scales to maintain such a distribution, so I examined both its potential  drivers and measures of the distribution.  I performed follow-up studies with \formaldehyde observations from the Green  Bank Telescope and Arecibo Observatory. With these observations, I measured  diff --git a/ch_software.tex b/ch_software.tex  index 0e464f6..01229ef 100644  --- a/ch_software.tex  +++ b/ch_software.tex 
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allowed some signal-processing features to be added to the BGPS pipeline that  could not be included in the original Bolocam pipeline.   \subsection{Pyflagger}  \url{http://agpy.googlecode.com/svn/trunk/agpy/pyflagger.py}\\  Pyflagger was originally intended as an interactive data-flagger for Bolocam  data, and was used as such, but it ended up being a complete data visualization  tool as well, implementing nearly the entire pipeline process within itself so  that each step could be easily visualized. Much of the pipeline debugging and  methodology development was performed using pyflagger. The interactive  flagging involves key and mouse commands to the \texttt{matplotlib} GUI.  Pyflagger uses the \texttt{idlsave} package to read IDL save files.  \section{PySpecKit}  \url{http://pyspeckit.readthedocs.org/} \\  PySpecKit was written in collaboration with Jordan Mirocha \citep{Ginsburg2011c}.  diff --git a/preface.tex b/preface.tex  index 600311d..4cd2d80 100644  --- a/preface.tex  +++ b/preface.tex 
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\usepackage{import}  \usepackage[utf8]{inputenc}  \usepackage{longtable}  \usepackage{booktabs}  diff --git a/thesis.tex b/thesis.tex  index 8b1a86f..ec1db78 100644  --- a/thesis.tex  +++ b/thesis.tex 
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\usepackage{import}  \usepackage[utf8]{inputenc}  \usepackage{longtable}  \usepackage{booktabs}  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%