Post date: Jul 26, 2013 6:23:56 PM
In the first installment of my blog, I discussed how I found the program and the lab work that I generally do. I also made a mistake saying that we look at C-14, but we actually look at C-13. C-14 is radioactive−it decomposes exponentially−whereas C-13 is stable and its concentration does not change with time. Now I’ll briefly mention how to look at data from coral and the American Geophysical Union (AGU) and my plans to present there.
When we have a coral, we need a time model. We need to determine what year and time of year each sample is from. We do this partly with the density banding we see in the x-ray of the coral, but we also use the isotope data. When we look at this data, we often use line plots, which should look roughly like sine waves possibly with an overall slope from beginning to end. The wave-like shape of the graph is caused by varying precipitation rates in different seasons, which affects the concentration of Carbon-13. When we look at the data, there are certain arrangements that look suspicious, as if the data is not accurate. One of these is when a single data point has a far different y-value (dC13) than the points next to it and is not between them. This creates spikes in the line graph, which Dave Mucciarone (lab manager and go-to guy) calls spikey crap. If there is enough of this suspicious spikey crap, we rerun the samples from there in the hopes that this will get rid of the spikes.
I plan to apply to the bright stars program at the AGU conference held in San Francisco. I need to write an abstract in which I discuss what I will present. I have no idea how to do this. This blog hopefully will help me to outline my ideas on the subject. I planned to talk a bit about the organisms that build the reefs, how we can extract temperature data from these (theory as well as lab techniques) then show our data and how we massaged it to get temperature data. This would be mostly review for someone who studied corals, but the graduate student I’m working for would look at the implications of the data we gather, which would hopefully be new and exciting information.